No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
On-demand monitoring of construction projects through a game-like hybrid application of BIM and machine learning
Abstract
While unavoidable, inspections, progress monitoring, and comparing as-planned with as-built conditions in construction projects do not readily add tangible intrinsic value to the end-users. In large-scale construction projects, the process of monitoring the implementation of every single part of buildings and reflecting them on the BIM models can become highly labour intensive and error-prone, due to the vast amount of data produced in the form of schedules, reports and photo logs. In order to address the mentioned methodological and technical gap, this paper presents a framework and a proof of concept prototype for on-demand automated simulation of construction projects, integrating some cutting edge IT solutions, namely image processing, machine learning, BIM and Virtual Reality. This study utilised the Unity game engine to integrate data from the original BIM models and the as-built images, which were processed via various computer vision techniques. These methods include object recognition and semantic segmentation for identifying different structural elements through supervised training in order to superimpose the real world images on the as-planned model. The proposed framework leads to an automated update of the 3D virtual environment with states of the construction site. This framework empowers project managers and stockholders with an advanced decision-making tool, highlighting the inconsistencies in an effective manner. This paper contributes to body knowledge by providing a technical exemplar for the integration of ML and image processing approaches with immersive and interactive BIM interfaces , the algorithms and program codes of which can help replicability of these approaches by other scholars.
... However, achieving robust window detection is challenging, as construction sites are visually complex and prone to occlusion, and windows from nearby infrastructure may also be detected. While several existing works have implemented object detection to monitor building construction [15,21], these methods did not consider measures to ensure that the detections used were robust. Automating the progress monitoring process for PPVC buildings would allow for consistent and unbiased reporting. ...
... Wei et al. [29] adapted a Mask R-CNN model into an instance segmentation model to measure progress for wall plastering based on the number of pixels within the plastering region as a percentage of the pixels in the wall. Rahimian et al. [21] incorporated the use of FuseNet to identify various target objects such as columns and beams through semantic segmentation of RGB-D data for the purpose of creating a virtual environment that is used for progress monitoring. A pretrained convolutional neural network (CNN) was also added in an object removal module to identify and remove unwanted objects such as humans, but these objects must be different from the target objects. ...
... Although the works by Wang et al. [14] and Zheng et al. [15] incorporated the use of 2D object detection to identify prefabricated components for progress monitoring, they did not propose any methods to overcome detection errors. In the work by Rahimian et al. [21], the proposed occlusion removal technique was ultimately still dependent on the detection robustness of the occlusion itself. ...
Prefabricated prefinished volumetric construction (PPVC) is a relatively new technique that has recently gained popularity for its ability to improve flexibility in scheduling and resource management. Given the modular nature of PPVC assembly and the large amounts of visual data amassed throughout a construction project today, PPVC building construction progress monitoring can be conducted by quantifying assembled PPVC modules within images or videos. As manually processing high volumes of visual data can be extremely time consuming and tedious, building construction progress monitoring can be automated to be more efficient and reliable. However, the complex nature of construction sites and the presence of nearby infrastructure could occlude or distort visual data. Furthermore, imaging constraints can also result in incomplete visual data. Therefore, it is hard to apply existing purely data-driven object detectors to automate building progress monitoring at construction sites. In this paper, we propose a novel 2D window-based automated visual building construction progress monitoring (WAVBCPM) system to overcome these issues by mimicking human decision making during manual progress monitoring with a primary focus on PPVC building construction. WAVBCPM is segregated into three modules. A detection module first conducts detection of windows on the target building. This is achieved by detecting windows within the input image at two scales by using YOLOv5 as a backbone network for object detection before using a window detection filtering process to omit irrelevant detections from the surrounding areas. Next, a rectification module is developed to account for missing windows in the mid-section and near-ground regions of the constructed building that may be caused by occlusion and poor detection. Lastly, a progress estimation module checks the processed detections for missing or excess information before performing building construction progress estimation. The proposed method is tested on images from actual construction sites, and the experimental results demonstrate that WAVBCPM effectively addresses real-world challenges. By mimicking human inference, it overcomes imperfections in visual data, achieving higher accuracy in progress monitoring compared to purely data-driven object detectors.
... Following closely, Huang MQ, 2021, Tunneling and Underground Space Technology [50] has 165 citations, reflecting the critical role of BIM in tunneling and underground space applications. Rahimian FP, 2020, Automation in Construction [51] garnered 159 citations, further emphasizing the sustained focus on BIM in Table 9 ranks the most globally cited publications in the field of BIM research, highlighting their academic impact and significance. Jiang F, 2021, Automation in Construction [49] is the most cited paper, with 168 citations, indicating its substantial influence in the field of automation and construction. ...
... Following closely, Huang MQ, 2021, Tunneling and Underground Space Technology [50] has 165 citations, reflecting the critical role of BIM in tunneling and underground space applications. Rahimian FP, 2020, Automation in Construction [51] garnered 159 citations, further emphasizing the sustained focus on BIM in automated construction processes. Other highly cited papers include Sepasgozar SME, 2021, Buildings-Basel [52] with 141 citations and Li X, 2022, Journal of Construction Engineering and Management [53] with 112 citations, showcasing their broad impact in architecture and construction management. ...
... Following closely, Huang MQ, 2021, Tunneling and Underground Space Technology [50] has 165 citations, reflecting the critical role of BIM in tunneling and underground space applications. Rahimian FP, 2020, Automation in Construction [51] garnered 159 citations, further emphasizing the sustained focus on BIM in automated construction processes. Other highly cited papers include Sepasgozar SME, 2021, Buildings-Basel [52] with 141 citations and Li X, 2022, Journal of Construction Engineering and Management [53] with 112 citations, showcasing their broad impact in architecture and construction management. ...
Building Information Modeling (BIM) has emerged as a transformative technology in the Architecture, Engineering, and Construction (AEC) industry, with increasing application in civil infrastructure projects. This study comprehensively reviews the research landscape of BIM applications in civil infrastructure through bibliometric analysis. Based on data from the Web of Science database, 646 relevant papers published between 2020 and 2024 were collected, and 416 papers were selected for in-depth analysis after screening. Using bibliometric methods, the analysis reveals the evolution of research trends, identifies key contributors and influential publications, and maps the knowledge structure of the field. Our study shows a significant increase in research output over the past five years, particularly in studies focusing on the integration of BIM with emerging technologies such as Digital Twins, the Internet of Things (IoT), and Machine Learning. The results indicate that the United States, China, and the United Kingdom lead in terms of research output and citation impact. Additionally, based on clustering results and representative keywords, several key research clusters were identified, including BIM in infrastructure lifecycle management, BIM collaboration in large-scale projects, and BIM for sustainable infrastructure design.
... Increase in worker competencies: VR allows for realistic simulation of different tasks and situations, enhancing the skills and competencies of workers (Delgado et al., 2020;Li et al., 2018) 14. Remote progress monitoring: Through VR, facility managers can remotely track the progress of construction and maintenance and make informed decisions (Delgado et al., 2020;Rahimian et al., 2020) ...
... The VR system helps in planning the routes of crane applications during highway Nguyen et al. (2016); construction, thus reducing traffic disruptions Davidson et al. (2020) Construction support-Track the progress of building construction in a virtual environment using bim data and construction monitoring real site images Delgado et al. (2020); Rahimian et al. (2020) Construction support-A VR construction safety training system that is more effective than a traditional training Constructionsafety system Delgado et al. (2020); ...
... Lack of interoperability between different systems Davidson et al. (2020; High investment in equipment and training of qualified personnel Rahimian et al. (2020) Lack of security-approved hardware-different certifications and security approvals required Construction support-Limited internet access-in some remote and isolated areas it can be challenging to operational support provides stable internet access Delgado et al. (2020); Difficulties in archiving output VR formats Tang et al. (2010); VR teleoperation systems can reduce efficiency as operators need more time to perform tasks Kamezaki et al. (2013) Management and marketing Lack of integration with other facility management systems Delgado et al. (2020); Lorusso et al. (2022); El Ammari and Hammad (2019) ...
... Currently, this environment can be provided with wearable devices called VR Headsets or multi-screen digital rooms. At first, VR became popular in the gaming and entertainment industries, but its entry into the architecture, engineering, and construction (AEC) industry did not take long since the discovery that VR applications allow better visualization and simulation of various scenarios (Pour Rahimian et al., 2020). VR technology, on its own or in combination with model-supporting technologies such as BIM, can act as a guide for better construction management. ...
... In the literature, it is stated that there is an increasing interest in AR in the AEC industry and examples of application areas such as education, damage measurement of buildings, risk management, energy control, facility management, synergic site visualization (i.e., tracking or determining the position of the components behind the finished wall), site planning, examining the consistency of what is planned and built and equipment operation are given (Karji et al., 2017;J. Kim & Irizarry, 2021;Pour Rahimian et al., 2020;Rohani et al., 2014). Improving the visualization, it provides more understandable 3D project data, and sharing this data with stakeholders over the internet creates a more efficient and easier construction management system (Bhadaniya et al., 2021a; M. J. Kim et al., 2011;Zhong & Hao, 2014). ...
... Kim & Irizarry, 2021;Li et al., 2019;Q. Liu et al., 2020;Oke et al., 2020;Pour Rahimian et al., 2020;Qian, 2021;Reinbold et al., 2019;Rohani et al., 2014;Turk & Klinc, 2017) Contract Management and Payments BIM, Blockchain (Darabseh & Martins, 2020;Li et al., 2019;Li & Kassem, 2021;Lin et al., 2021;Turk & Klinc, 2017;Wahab et al., 2022) Cost Management AI, AR, BIM, Blockchain (Agostinelli, Cinquepalmi, et al., 2019;Aziz et al., 2014;Bhadaniya et al., 2021b;Dallasega et al., 2020;Darabseh & Martins, 2020;Ghanem, 2022;Li et al., 2019;Nguyen, 2021;Rankohi & Waugh, 2013;Turk & Klinc, 2017;Zhong & Hao, 2014) Energy Efficiency AR, BIM, IoT (Karji et al., 2017;J. Kim & Irizarry, 2021;Pour Rahimian et al., 2020;Rohani et al., 2014;Sepasgozar et al., 2021) Facility Management AR, BIM, Blockchain, IoT (Darabseh & Martins, 2020;Karji et al., 2017;J. ...
Effective construction management plays a significant role in overcoming challenges the industry faces including low efficiency and productivity, inability to control processes, low profit, etc. In the digital transformation era, several interrelated aspects such as competition, changing customer demands, new generation employees and stakeholders, presented new instruments which have started to become widespread in construction management. Extensively applied in the automotive and manufacturing industries, Digital Twin (DT) has emerged as a technology-based innovation as one of the advances of the digitalization journey to improve and support construction practices. DT is a virtual system that brings design, construction, and operation together by data-linking to its real and physical assets bidirectionally with combined technologies. To answer key questions-namely "What is the definition of Digital Twin (DT)?", "What are the elements, technologies, and applications of DT in the construction industry?", and "How DT can be used for enhanced construction management?"-a synthesis of the digital twin literature through the lens of construction management will be made and the high potential of DT to improve construction management practices will be demonstrated. Furthermore, the frequency of consideration of DT practices and the level of awareness in the industry will be increased.
... Without practical case studies, there is a risk of underestimating or overlooking critical implementation challenges, such as technical compatibility, cost implications, and the learning curve associated with using these technologies. This gap in research needs to be addressed to validate the theoretical benefits of BIM-XR integration and to guide the industry in effectively leveraging these tools for improved project outcomes (Rahimian et al., 2020). ...
... While the integration of BIM with XR technologies has been well-documented, there is limited research on how these integrated systems can further interact with other emerging technologies, such as Artificial Intelligence (AI) and the Internet of Things (IoT). The potential of combining BIM-XR with AI and IoT for predictive analytics, real-time data processing, and automated decisionmaking is mainly unexplored (Rahimian et al., 2020). Current studies have not fully addressed how these technologies can synergize to create more intelligent, responsive construction management systems. ...
This study investigates the integration of Building Information Modeling (BIM) and Extended Reality (XR) technologies in construction education, focusing on their application in internship programs. Traditional internships often face limitations such as restricted exposure to complex scenarios and geographical constraints. To address these challenges, this research examines how BIM-XR technologies can bridge the gap between theoretical knowledge and practical application in the Architecture, Engineering, and Construction (AEC) disciplines. A mixed-methods approach was employed, combining qualitative data from interviews with students, educators, and industry professionals alongside quantitative survey data. This comprehensive methodology enabled an in-depth assessment of BIM-XR's impact on student learning outcomes and operational efficiency. The findings revealed that BIM-XR integration significantly enhances educational experiences by providing immersive simulations that help students visualize complex construction processes. This leads to better preparedness for real-world challenges, increased engagement, and more effective internships. Additionally, BIM-XR offers practical benefits for industry practices, such as improved project planning, real-time decision-making, and enhanced stakeholder collaboration. However, the study also highlights challenges, including the steep learning curve, limited access to necessary technologies, and high implementation costs. These limitations point to the need for comprehensive training programs, affordable technology options, and institutional support to ensure broader adoption. Looking ahead, the study suggests that further integration of BIM-XR with emerging technologies like Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) could further enhance educational outcomes and industry practices. ARTICLE HISTORY
... Recent advancements in immersive VR environments have enabled more intuitive interactions between designers and their design spaces, enhancing collaboration and engagement [28]. Other benefits include enabling distributed design collaboration, facilitating greater efficiency and cost-effectiveness in design [29], increasing design accuracy [30], increasing levels of engagement with the design, augmenting the perception of shared design representations [24] and facilitating deeper architectural understandings about space and place [31]. ...
... This suggests that designers expend less cognitive effort in exploring an immersive VR environment, thus they can focus more on other design activities such as exploring the design solution and problem space. These findings are in accordance with prior studies which have suggested that immersive virtual environments can facilitate more intuitive interactions between designers as well as between designers and their design environment [28], causing improved spatial cognition [42] that may benefit more effective design collaboration. ...
This paper presents the results of a protocol study exploring the impact of various digital technologies on team collaborative design processes. Previous studies have suggested that compared to traditional methods such as sketching, digital technologies can provide further benefits for collaborative processes. However, there persists a lack of understanding about the impacts of digital technologies on such processes, particularly in relation to emerging significant digital technologies such as immersive Virtual Reality (VR). Therefore, this study aims to fill that gap by exploring team collaboration behaviours of two groups of professionals working in two digital design environments-desktop 3D modelling with Revit and immersive VR using Hyve-3D-as well as their behaviours during traditional sketching sessions for benchmarking purposes. Utilising protocol analysis method, the think-aloud data of participants was recorded, transcribed and coded using an adapted collaborative practice model. Team collaboration activities are broadly categorised as 'Con-tent' or 'Process': content referring to design task-based activities, while process refers to activities related to the organising of group processes. The results suggest that during the design collaboration process, designers allocated the majority of their efforts towards process-oriented design activities. Differences between design environments only had a minor impact on the amount of effort expended on process-oriented activities and content-oriented activities. Moreover, traditional sketching design environments were shown to be potentially beneficial for problem-solution and associated negotiation activities. Additionally, immersive environments were associated with a reduction in the designers' cognitive effort that was expended on exploring the design environment.
... Selain itu, BIM berperan sebagai alat yang merekam jejak karbon secara rinci dan sangat penting dalam mendesain bangunan yang berorientasi ramah lingkungan. BIM bahkan dapat digunakan untuk menghitung kadar CO2 dalam beton precast dan energi yang dihasilkan oleh material bangunan (Rahimian et al., 2020). Hal ini membuktikan bahwa BIM bukan hanya alat biasa, melainkan fondasi penting dalam upaya mengurangi dampak lingkungan dari industri konstruksi (Tulenheimo, 2015;Lee et al., 2015;Zhang et al., 2018;Soust-Verdaguer et al., 2020;Liu & Wang, 2022). ...
... • Dalam kurun waktu 11 tahun, penelitian mengenai BIM terus meningkat dari tahun ke tahun, dengan pembahasan mulai dari implementasi BIM, tingkatan adopsi BIM, peranan BIM dalam setiap fase konstruksi, teknologi lain yang dapat diintegrasikan dengan BIM, dan keberlanjutan (sustainability) dari penggunaan BIM. • Pada saat ini terdapat banyak sekali teknologi yang dapat dipadukan dengan BIM dalam keperluan konstruksi seperti penggunaan drone dalam kegiatan pemetaan (Cheng et al., 2022), perhitungan carbon footprint suatu bangunan (Rahimian et al., 2020), bahkan penggunaan sensor untuk kegiatan pengawasan dalam lapangan kerja (Tan et al., 2021). • Penelitian mengenai peranan BIM dalam manajemen fasilitas masih sangat rendah dengan hanya dapat ditinjau 3 jurnal. ...
Technological advancements have reshaped our work principles, deviating from established standards. In the construction industry, many individuals and companies persist in using outdated technology, overlooking current technological strides. Building Information Modeling (BIM) stands out as a contemporary solution, offering numerous benefits not only to individual stakeholders but also to all parties involved in a construction project. Despite BIM's potential advantages, numerous practitioners remain entrenched in their comfort zones, demonstrating a lack of awareness. In response to this, a collection of articles addressing various aspects of BIM, including its benefits, obstacles, challenges, and integration with technologies like Computer-Aided Design (CAD), Unmanned Aerial Vehicles (UAV), and the Internet of Things (IoT), has been assembled. By accentuating BIM's distinctive characteristics, this research seeks to enhance awareness, ultimately fostering increased adoption and development of BIM technologies. The comprehensive exploration of BIM's traits aims to bolster awareness, paving the way for widespread adoption and advancement within the construction industry. Abstrak Pekembangan teknologi terus mengubah prinsip-prinsip kerja yang sudah terbentuk sebelumnya. Pada industri konstruksi, sayangnya masih banyak individu dan perusahaan yang menggunakan teknologi lama dan mengabaikan kemajuan teknologi saat ini. Building Information Modeling (BIM) menjadi salah satu solusi terkini yang memberikan banyak manfaat tidak hanya untuk pihak-pihak terkait individual tetapi juga untuk semua pihak yang terlibat dalam proyek konstruksi. Meskipun demikian, banyak praktisi yang masih nyaman menggunakan teknologi lama, hal tersebut menunjukkan masih kurangnya kesadaran terhadap pentingnya BIM sebagai teknologi terkini. Sebagai respons terhadap hal tersebut, beberapa artikel terkait BIM telah dikumpulkan, membahas manfaat, hambatan, tantangan, dan integrasi dengan teknologi lain seperti Computer-Aided Design (CAD), Unmanned Aerial Vehicles (UAV), dan Internet of Things (IoT). Dengan menyoroti karakteristik unik BIM, penelitian ini bertujuan untuk meningkatkan kesadaran, sehingga memperkuat peluang adopsi dan pengembangan teknologi BIM. Eksplorasi menyeluruh terhadap sifat-sifat BIM bertujuan untuk memperkuat kesadaran (awareness), membuka jalan bagi adopsi dan kemajuan luas dalam industri konstruksi.
... Laser scanning, LiDAR, Photogrammetry, Point clouds, 4D BIM, Reality capture, Object recognition, Semantic segmentation. (Alizadehsalehi and Yitmen, 2023;Pan and Zhang, 2021;Pour Rahimian et al., 2020;Rausch and Haas, 2021) DIM Blockchain-enabled DT Integrating DT with blockchain to enable traceable, immutable, and secure data sharing. Blockchain, Smart contracts, SHA-256 algorithm, Data querying (Jiang, Liu, et al., 2021;Zhao et al., 2023), Data Schemas-enabled DT Utilizing semantic web technologies and data schemas to address the integration of heterogenous construction data. ...
... Although in many articles especially within the manufacturing industry, being able to provide real-time data with high frequency and low latency is a requirement for a system to be a DT, in many construction applications that might not be achievable or even valuable. For example, in some applications, it was adequate to collect data, mostly visual, at a frequency of once a day (Hasan and Sacks, 2023;Pour Rahimian et al., 2020;Zhao et al., 2022). Yet, in other applications where data gets updated more dynamically, a higher frequency is essential. ...
Digital twin in the construction phase—termed
Construction Digital Twin (CDT)—faces more
significant challenges than in other phases, such as
operation or maintained phase, due to the dynamic and
evolving nature of construction sites and their broad
spectrum of applications. To enhance our understanding
of the CDT domain, it is crucial to clearly define it,
establish a detailed taxonomy of its current applications
and enabling technologies, and elucidate how attributes,
data requirements, and technology choices within CDT
vary across different construction applications. This
objective remains unmet in existing literature, a gap this
paper addresses through an approach combining
systematic review, thematic coding, and
conceptualization of CDT architecture comprising of five
layers: sensing, communication, storage, analytics, and
visualization. The study identifies seven major
applications of CDT and maps them to the five
architectural layers and their enabling technologies,
providing insights into the suitability and prevalence of
these technologies for specific applications of interest.
... Through a detailed literature review it was found that even though such platforms have been explored for several project management aspects including safety and quality through immersive games, their potential in progress monitoring is still unexplored [10][11][12]. Gamification can likely enable monitoring processes to be more engaging by inspiring project managers, contractors, and employees for active participation [13]. This will result in improved data quality and reliability, enabling better project controls. ...
... Recent research has also explored the combination of gamification with extended reality to create workflows that facilitate progress monitoring. For example, a proof-ofconcept framework for on-demand simulation of projects for progress monitoring was presented through a game-like hybrid application of BIM and machine learning [11]. However, further studies are needed to explore the potential of gamification in construction progress monitoring to its full potential. ...
Existing progress monitoring systems in the construction industry rely heavily on manual data entry and have limited visualization capabilities. Although several researchers have attempted to address these deltas through various automated technologies, their practical implementation in sites has not reached its full potential. This could be achieved through easy-to-implement platforms that facilitate automated data capture and visual representation of progress, thereby providing a better understanding of the project status and facilitating improved project controls. Furthermore, features to visualize the performance of multiple crews will enable comparative analysis and establish improved benchmarks for the project goals. ConXR, a platform that integrates automated data capture, an extended reality environment for progress visualization, and comparative participant analysis, is proposed. ConXR is designed to be a structured platform that displays as-planned and as-built models generated from the automatically acquired data. In ConXR, crews are designated as the players within a competitive environment to enable comparative performance assessment in each construction stage. The architecture and development details of the platform are presented, along with an illustration of its application. The interactive features of ConXR have the potential to establish and achieve improved progress targets based on preceding crew performances. Furthermore, ConXR can enable immersive progress visualization of automatically acquired data as digital models. The novelty of ConXR lies in its comparative participatory features that enable crew performance evaluation by engaging them in an environment where constraints and their impacts are documented in a structured form.
... ad (Rafsanjani & Nabizadeh, 2023). En los últimos años, el mantenimiento preventivo ha ganado reconocimiento significativo, apuntando incluso al mantenimiento predictivo, especialmente en el contexto de los puentes, que son componentes cruciales de la infraestructura crítica que asegura conectividad, transporte, rutas comerciales y calidad de vida (Pour et. al, 2020). ...
Los procesos de inspección en minería, infraestructura y energía son esenciales. La tendencia hacia la gestión preventiva y predictiva ha impulsado el uso de tecnologías de acceso remoto, como drones y robots, para acceder a zonas de riesgo y realizar inspecciones desde un computador. No obstante, estas no reemplazan completamente el juicio de un trabajador en terreno. La realidad virtual (RV) permite a los profesionales realizar inspecciones de manera remota y en primera persona. Esta optimiza procesos de revisión, análisis y colaboración, proyectando modelos 3D y reconstruyendo sitios de trabajo reales. Este artículo muestra el desarrollo de un método para la inspección remota de infraestructura mediante realidad virtual, desarrollando un caso de implementación en puentes. Utilizando nubes de puntos creadas a partir de imágenes de drones, integradas con modelos BIM, se permite una inspección detallada de elementos estructurales, evaluando daños según normativa. Diseñada en Unity 3D, la experiencia incluye un tutorial interactivo y generación de informes detallados, con potencial para sincronizarse con los modelos BIM y almacenar la información. Su implementación demuestra aspectos de correcta funcionalidad y eficiencia en la inspección, en particular en el caso de estudio de puentes, pero ampliable a diversas infraestructuras.
... BIM technology has demonstrated its capacity to bolster energy efficiency and advance environmental sustainability, outpacing traditional methods (Gerbino et al., 2021;Rahimian et al., 2020;Theißen et al., 2020;Wang & Tang, 2021;Yuan & Fan 2018). Integrating Building Energy Consumption Simulation (BECS) software via the Industry Foundation Classes (IFC) data standard facilitates building energy consumption simulations, thereby enhancing the digitization, precision, and efficiency of assessment results. ...
The construction industry’s rapid growth significantly impacts energy consumption and environmental health. It is crucial to develop optimization strategies to enhance green building energy efficiency and encompass comprehensive analysis methods. This study aims to introduce and validate a novel framework for optimizing energy efficiency design in green buildings by integrating Building Information Modeling (BIM) technology, Life Cycle Cost (LCC) analysis, and orthogonal testing methods, focusing on enhancing energy efficiency and reducing life cycle costs. The optimization parameters for the building envelope are identified by analyzing energy consumption components and key green building factors. The orthogonal testing method was applied to streamline design options. Building Energy Consumption Simulation (BECS) software and LCC analysis tools were employed to calculate each optimized option’s total annual energy consumption and the current life cycle costs. Using the efficiency coefficient method, each optimization scheme’s energy consumption and economic indicators were thoroughly analyzed. The framework’s validity and applicability were confirmed through an empirical analysis of a campus green building case in Fujian Province, demonstrating that the optimized framework could reduce energy consumption by 4.85 kWh/m2 per year and lower costs by 38.89 Yuan/m2 compared to the reference building. The case study highlights the framework’s significant benefits in enhancing environmental performance and economic gains. The results provide critical parameter selection and offer scientific and technological support for the design of building energy efficiency, promoting optimization techniques and sustainable development within the construction industry.
... Strategic and Systematic Thinking; This procedure involves developing a strategic plan with quality as a fundamental element [12]. Integrated System; Establish a work attitude that prioritizes quality above all else, and uses diagrams, graphs, and other visual aids to explain to staff how their duties contribute to the organization's larger goals [13]. Decision-Management; Make informed judgments by consulting reliable historical data and safeguarding prior decisions. ...
An effective strategy aligning costs and quality is pivotal for augmenting project value in the construction industry. This study develops and applies a model to evaluate the impact of Total Quality Management (TQM) principles on Egyptian construction projects. Objectives include identifying key TQM principles, quantifying their value, and validating outcomes through a case study. Methodologically, a TQM evaluation model, utilizing the Relative Importance Index (RII) method and validated via Cronbach’s alpha, is formulated. A value formula estimates the financial impact of top TQM principles compared to construction project life cycle costs. Applied to a 2023 Egyptian construction project case study, the formula demonstrates cost savings surpassing the required investment. Specifically, project value improved 2.77 times using the created Value Engineering Business Approach (VEBA) formula, translating to an estimated 12.8% reduction in total life cycle costs. This research advocates a data-driven approach to prioritize TQM principles, showcasing positive financial returns for firms and endorsing TQM as an effective framework for the Egyptian construction sector.
... People, companies, and government organizations use BIM to plan, design, build, operate, and repair buildings [6][7][8][9][10][11]. As a widely used intelligent technology, BIM may have a significant potential to help with VE in the early stages of project initiation [12][13][14]. ...
This study explores integrating Building Information Modeling (BIM) and Value Engineering (VE) to enhance cost management and project value optimization in residential construction. The primary aim is to develop a comprehensive framework that synergizes 5th Dimension Building Information Modeling (5D BIM) with VE processes to identify and evaluate the most cost-effective construction alternatives. Employing a mixed-methods approach, this research includes an extensive review of existing practices, an integrated BIM-VE framework proposal, and the application of 5D BIM principles to improve visualization, cost estimation, and scheduling. The findings reveal that integrating BIM and VE significantly enhances project efficiency, quality, and cost-effectiveness. Specifically, the case study of a twin villa project demonstrates a 42% reduction in floor material costs and a 30% reduction in door material costs, resulting in an overall project cost savings of 35%. Additionally, the integrated approach contributes to a 15% reduction in project duration and a notable improvement in design quality and stakeholder collaboration. This research contributes significantly by providing a robust framework for BIM and VE integration, emphasizing its potential to revolutionize cost management practices in the construction industry. The novelty of this study lies in its detailed and practical approach to merging BIM with VE, offering a viable solution for resource optimization and sustainable building practices. This study highlights the transformative potential of BIM-VE integration, advocating for its broader adoption to achieve superior project outcomes.
... In this sense, the generation of 3D models of historical buildings has become a standard process [42]. The aim is to obtain results with a high level of reliability with the use of 3D laser technology, thus guaranteeing correct documentation on which to work on restoration, conservation and maintenance projects of the heritage asset [42][43][44][45][46][47][48][49]. The integration of a 3D model with its respective information in a single virtual environment is achieved with the Building Information Modeling (BIM) tool, which allows obtaining the Historical Building Information Modeling (HBIM) of the built heritage [50][51][52][53][54][55]. ...
Currently, tangible heritage has been affected by lack of maintenance, human interventions and deterioration due to natural causes, which is why research aimed at the conservation of heritage assets that preserve the history, tradition and identity of a place is required. The current study is performed in order to apply modern methodologies in a heritage asset, starting from a Historic Building Information Modeling (HBIM) model of architectural documentation to conduct its structural evaluation in a non-linear finite element analysis software. The proposed workflow begins with collecting information with a 3D laser scanner. The processing, debugging and management of data is realized in the Trimble Real Works software, then the model is exported to the HBIM ArchiCAD 24 software, where the building can be seen in 3D. This is how the detail plans in plan and elevation are generated, of which the model is exported to allow the structural evaluation to a software for finite element analysis DIANA FEA (acronym for Displacement Analyzer). Using a three-dimensional geometric model, a pushover analysis is performed, which allows data to be obtained on the most critical or vulnerable elements that would be affected in the event of a seismic movement. The results of this evaluation are of relevant importance in the case of the Balbanera church, since they constitute a basis, from which an intervention project can be proposed considering the current requirements of the structure. In addition, the HBIM model offers the facility of multipurpose, that is, one is able to work on several topics included in the same 3D model. The proposed methodology could become a standard model for studies in other heritage buildings.
... It has been demonstrated that the ANN algorithm can estimate time and cost with perfect weight values [58]. It has also been possible to apply AI technology by merging deep learning modeling of construction management systems with 3D reconstruction [59,60]. Deep learning autoencoders were created as a way to deal with small data sets in construction management by augmenting and creating synthetic data [61]. ...
Decision-making in Construction Project Management (CPM) involves numerous ambiguous information and uncertainties due to the nature of construction project. The Soft Computing (SC) approach, which offers several data processing strategies under uncertainty, has been extensively researched in CPM studies for decision problem solving. Decisions that cannot be adequately handled by conventional computer systems are facilitated by the SC approach. The SC approach encompasses a variety of SC techniques that are constantly developing and becoming more widely used to address real construction challenges. This study aims to conduct Systematic Literature Reviews (SLR) on the development of mainstream SC techniques and their current application in construction projects. Using an inventive SLR technique, 83 CPM papers covering the years 2018 to 2023 were selected for this study and then classified into four primary application themes of SC in CPM. The research trend was then described using bibliometric analysis. Afterwards, a topic-based qualitative analysis was conducted to investigate the application of SC approaches in the construction field. Several potential challenges to current research were then elaborated. It also contributed to suggesting future directions for the advancement of SC techniques that would be advantageous for construction research and practice. Doi: 10.28991/CEJ-2024-010-06-020 Full Text: PDF
... It can support the implementation of remote monitoring, particularly for dangerous sites. Rahimian, et al. (2020) proposed an integrated approach of BIM models and as-built image data in unity to achieve progress monitoring in a virtual environment. Alam, et al. (2017) presented a VR-based Internet of Things (IoT) platform, monitoring extreme environment work to improve safety and reduce errors. ...
Purpose - The research presents a study of the barriers that hamper industry adoption of Virtual Reality (VR) for construction activities in China. Design/methodology/approach - The research adopted a qualitative research methodology, using semi-structured interviews to collect data from participants. The initial set of codes was established through an open coding method for the subsequent content analysis of data. Findings - The findings obtained identified the economic issue as the main barrier impeding the adoption of VR to deliver construction projects in China. Other barriers were also identified including insufficient technical depth and awareness, and the fragmented nature of the supply chain within the industry. Originality/value - The study proposed recommendations on raising awareness, setting up workflows and government support to mitigate the limiting barriers. The findings of the study provide valuable insights to researchers and Chinese construction industry practitioners to better understand and promote VR adoption in the delivery of construction projects.
... AI's potential to assist with actualising specific construction objectives has been reported. For instance, AI's ability to automate construction tasks, connect sensors using IoTs, predict and manage construction-related risks and uncertainties, and monitor building and construction project performance has been reported [72]. AI's potential to facilitate effective decarbonisation of the built environment through contributing towards effective decision-making for improved circularity performance of construction, particularly around materials and energy, has been elucidated [73]. ...
Mitigating climate change challenges in the built environment through the decarbonisation of energy and construction materials remains a pressing challenge. The circular economy (CE) has been identified as a critical pathway to achieving this objective. CE promotes the efficient use of resources, extending their lifecycle and minimising their environmental impact using a plethora of methods. The link between CE and decarbonisation
becomes evident when the intertwined relationship between materials, energy, and the environment is considered. By reducing waste and ensuring the continuous use of materials and energy resources, CE significantly lowers carbon emissions. This approach is inherently aligned with the overarching goals of the decarbonisation agenda. The emergence of digital technologies such as artificial intelligence (AI) has continued to transform how
the built environment activities are conducted and improved. However, the utility of AI models in engendering
the actualisation of the decarbonisation agenda through improved circular economy performance within the built environment context remains under-researched. This study addresses this knowledge-practice gap, using a scientometric and scoping analysis of relevant peer-reviewed and grey literature. Findings from the scientometric analysis revealed AI has been explored separately in circular economy and decarbonisation. Yet, studies
exploring AI in relation to the circularity performance of the built environment for improved decarbonisation remain scant. The narrative review from the scoping analysis further revealed the usefulness of AI in driving optimal decarbonisation and levels through improved circularity performance of materials and energy across various economic sectors, including the built environment for optimal decision making which in turn, encourages responsible producer and consumer behaviour for improved CE performance.
... Production monitoring (Ali, Lee, and ParkAli et al., 2020a), tracking hardware and inventory (Bosché, et al., 2014), and progress measurements (Rebolj, et al., 2017) all primarily rely on manual visual assessments and traditional progress reports. However, these practices are neither commonplace nor efficient, often demanding significant time investment and being susceptible to errors (Rahimian, et al., 2020). Expounding on this notion, Park, et al. (2013) outline a comprehensive three-stage framework for implementing augmented reality (Park, et al., 2013). ...
In contemporary design practices, the conflict between initial design approaches and subsequent manufacturing and construction stages presents a notable challenge. To address this disparity, our study aims to establish a comprehensive digital design workflow, bridging these gaps. The authors introduce a conceptual framework that seamlessly integrates the imperatives of LEED with the realm of robotic manufacturing, specifically tailored for construction sites. The proposed methodology encompasses four distinct iFOBOT modules: iFOBOT-environment, iFOBOT-design, iFOBOT-construct, and iFOBOT-monitor. The integration of these modules allows for a holistic approach to design and construction, fostering efficient collaboration between multidisciplinary teams. To validate the efficacy of the author’s approach, we conducted an empirical study involving the creation of a double-skin facade panel perforation using this integrated process. Initial findings emphasize the enhanced constructability achieved through simulated robotic interventions utilizing a heuristic function. Moreover, this research presents a functional prototype as a tangible embodiment of the method’s practical application and potential impact on the field of architectural design and construction.
... Our implementation harnesses the DQN model, which is an extension of the PyTorch's nn.Module class. This architecture is characterized by multiple fully connected layers, adept at mapping the input state to the corresponding Q values for each possible action, as outlined in reference [10]. To materialize this DQN model, we introduce the QNet class, which encapsulates our DQN architecture. ...
This paper explores the dynamic intersection of Reinforcement Learning (RL) and Deep Learning (DL), emphasizing their collaborative strength in the realm of Deep Reinforcement Learning (DRL). The fusion of RL's strategy optimization through agent-environment interactions and DL's advanced perception and decision-making abilities results in DRL's powerful framework. This framework is extensively utilized to tackle intricate decision-making challenges across diverse fields. The paper begins by laying a solid theoretical foundation, explaining the core principles of both reinforcement learning and deep learning, and highlighting their synergistic potential. Further, the focus shifts to the practical implementation of the Deep Q Network (DQN) algorithm, particularly in the context of elementary path analysis problems. This segment starts with an accessible introduction to deep neural networks, paving the way for a deeper understanding of DQN. It then explores key aspects such as the utilization and accumulation of experiences, a critical component of the learning process in DRL. Additionally, the paper addresses the inherent limitations of the DQN algorithm, suggesting avenues for potential improvements and enhancements.
... Simultaneously, coordination between all relevant parties is necessary to ensure that the project progresses in line with the established plan. The application of DTs in construction primarily involves construction monitoring [40], on-site scheduling [41], construction safety [42], and human-machine collaboration [43,44]. Li et al. [45] developed and tested an integrated framework that combines DT technology and blockchain. ...
Digital solutions, such as digital-twin (DT) technologies, can significantly improve the construction industry by addressing its inherent challenges, such as complex project management, delays, quality control, safety issues, and environmental impact. Building-integrated photovoltaics (BIPVs) is a promising system for building envelopes to harvest renewable solar energy onsite. Interest in the application of DT technologies in BIPV systems has grown, including the prediction and optimization of energy performance and accurate diagnosis of faults. Despite increased research on the integration of BIPV systems with DT technologies, a systematic, holistic analysis that encompasses all project phases for effective lifecycle management is still lacking. This study comprehensively analyzes the integration of BIPV systems and DT technology throughout the building lifecycle, including design, construction, operation, demolition, and recycling, and it underscores the potential of combining BIPV systems with DT technology as a strategic approach to enhance efficiency, safety, and reliability. This study investigates three primary objectives: 1) the current applications/attempts to apply DTs to BIPV systems from a lifecycle perspective; 2) the challenges of integrating DTs and BIPV; and 3) promising DT technologies/strategies for promoting BIPV development. This study offers significant insights by highlighting the use of DT technologies across the full lifecycle of BIPV systems. It details the application of these technologies across five crucial layers in BIPV systems, data technologies, modelling methods, and simulation techniques. Moreover, it elucidates current research gaps in this domain and proposes valuable recommendations for future research avenues.
... One of the latest endeavors for BIM is to integrate with the game engine for various applications in the construction sector, such as collaborative decision-making (Du et al., 2018), interactive design change (Panya et al., 2023), and construction project monitoring (Pour Rahimian et al., 2020). Ratajczak et al. (2019) proposed a working prototype of augmented reality for construction (AR4C). ...
Unmanned aerial vehicles (UAVs), building information modeling (BIM), and game engines are evolving technologies that are rapidly being adopted to enhance construction safety management and create safety training platforms. For construction safety improvements, utilizing game engines plays a crucial role where a 3D model is required. 3D models of a real construction site can be produced using UAV photogrammetry. High-quality UAV photogrammetry-derived 3D models have the potential to be integrated with game engines and support construction safety. However, the qualities of the 3D models from the photogrammetry techniques vary due to several factors, such as flight altitude, image overlapping percentages, and structure from motion (SfM) algorithms of post-processing tools. Hence, this study aims to evaluate the qualities of photogrammetric products (point cloud and 3D models) by employing several novel methods for more efficient integration with game engines. Furthermore, the study's goal is to ascertain whether construction safety improvement can benefit from integrating game engines with 3D models generated from UAV photogrammetry. A game is developed to provide virtual instructions to workers and safety associates based on OSHA regulations through the integration of a UAV-derived 3D model and game engine. On the other hand, BIM is another source of 3D models, and in this study, the potentialities and limitations of BIM technology in improving safety management are discussed. In addition, a comprehensive framework is developed to integrate BIM data and a game engine. Finally, two case studies are conducted on real-life scaffolding accident simulation and emergency evacuation modeling following the framework.
... Photographic surveying and digital image correlation can also be employed to monitor the deformation of similar material model. Photographic surveying can obtain spatial information on many points in the model simultaneously, and the measurement accuracy is high (Wu et al. 2018;Rahimian et al. 2020). However, this modus cannot monitor the dynamic movement of the model measuring points (Zhang et al. 2018). ...
The dynamic subsidence disaster caused by underground mining of coal resources is a complex spatiotemporal process, which is a common disaster in mining areas. The backfilling strip mining technology is a green and sustainable coal mining method, which has been commonly used to reduce the subsidence disaster of the overlying strata and protect surface buildings. The transient deformation is the main reason of surface buildings damage; therefore, in this study, the similar material model was used to research dynamic deformation characteristics of the overlying strata in backfilling strip mining at different time scales, and the optical image method was employed to monitor and obtain the movement data of the overlying strata automatically. The data analysis shows that there is a time-scale effect in mining subsidence. The deformation of the overlying strata increases instantaneously at a certain time under the monitoring of small time scale, and this phenomenon gradually disappears as time scales increase. According to the subsidence velocity of small time scale, the subsidence state of the overlying strata can be further divided into the abrupt subsidence state and the gentle subsidence state. This is really significant for promoting the development of the backfilling strip mining technology and preventing the damage of surface buildings.
... Another study [9] focused on integrating BIM and machine learning in railway systems to localize defects in railway infrastructure. Using new technologies such as image processing, machine learning, and virtual reality (VR) along with BIM to automate construction project simulation [10] is another example of how machine learning is applied with BIM. ...
The complexity of BIM models challenges the engaged parties to deliver an accurate model suitable for various purposes. This is especially important during the construction stage, where errors in construction drawings entail considerable cost and time burdens. As a possible solution, artificial intelligence and machine learning (ML) techniques can be deployed to assist BIM parties with the time and resourceconsuming task of checking the quality of BIM models. This study aims to use machine learning techniques to check the quality of BIM models, especially in precast structural wall openings. A machine learning model was used in a BIM model of a project to detect anomalies in openings of precast structural walls, and it was able to detect all the openings with wrong information, which, consequently, would negatively impact the final delivery of the walls. Considering the applicability of using such an ML model in other projects, the contribution of this study is to reduce the errors in the construction drawings and consequently secure the projects in terms of time and cost burdens due to these errors.
... Another study [9] focused on integrating BIM and machine learning in railway systems to localize defects in railway infrastructure. Using new technologies such as image processing, machine learning, and virtual reality (VR) along with BIM to automate construction project simulation [10] is another example of how machine learning is applied with BIM. ...
Livro de atas do Congresso ptBIM 2024, onde se promove a discussão técnico-científica em língua Portuguesa da metodologia ‘Building Information Modelling’ (BIM), envolvendo a participação ativa das comunidades profissional e académica das áreas de Arquitetura, Engenharia e Construção. Pretende-se enfatizar os problemas e esforços de implementação BIM no Ambiente Construído e reforçar as redes de profissionais que incorporam práticas BIM nas suas atividades. https://ptbim.org/
... In the current sophisticated era, elementary school students' critical thinking ability must be empowered as an effective way to make students skilled in carrying out the learning process (Areni et al., 2018;Long et al., 2020). Critical thinking ability, quoted from Ennis, is a natural and reactive way of thinking to determine focus in determining what to believe and do (Abbasi et al., 2019;Pour et al., 2020). Critical thinking is a very important ability to support the success of students' understanding so that it will impact student learning outcomes (Wang et al., 2021;Weeks et al., 2021). ...
Limited students' critical thinking skills in learning can be strengthened by using augmented reality technology applications. The main objective of this research is to analyze the problem-based learning model with integrated augmented reality educational games in improving students' critical thinking abilities. This quasi-experimental research used a nonequivalent control group design. The research sample consisted of 60 students from SD Muhammadiyah 09 Surabaya who were selected using purposive sampling. Data was collected by observing the implementation of learning and testing students' critical thinking levels. The data obtained in the research was processed statistically based on research obtained with a significant value of 0.000 < 0.05, confirming that Ho was rejected and H1 was accepted. Thus, the probability value is 0.000 < 0.05, so all coefficients have meaning. Applying the PBL model with integrated augmented reality educational games on critical thinking skills has a significant effect. From the ANOVA test or F-test, Fcount was 45,348 with a significance level of 0.000. This probability is smaller than 0.05, so the learning model to predict the level of participation in learning with a problem-based learning model through educational games integrated with augmented reality influences students' critical thinking skills.
... Designing and constructing a building is a complex and time-consuming process that involves careful planning, diverse participation, and effective management (Rahimian et al., 2020;Li et al., 2019). The Royal Institute of British Architects (RIBA) has established the RIBA work plan (see Figure 1) that identifies five project design stages, their primary outcome, and the cost information available at each stage (RIBA, 2021). ...
Purpose- Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.
Design/methodology/approach- Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021-2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).
Findings- This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes (300,000, 500,000, 1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.
Research limitations/implications- The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.
Originality/value- This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.
... The on-site activities involved in progress monitoring and controlling include the supervision of construction work, quantifying the level of completion of the work, assessing building quality, reporting on construction progress, and addressing on-site issues. Most progress monitoring and controlling tasks are still traditionally conducted using 2D drawings, reports, schedules, photo logs, and paper-based assembly instructions [2]. These pose problems, which are attributed to poor-quality management control due to their complexity and inefficient nature. ...
In recent years, mixed reality (MR) technology has gained popularity in construction management due to its real-time visualisation capability to facilitate on-site decision-making tasks. The semantic segmentation of building components provides an attractive solution towards digital construction monitoring, reducing workloads through automation techniques. Nevertheless, data shortages remain an issue in maximizing the performance potential of deep learning segmentation methods. The primary aim of this study is to address this issue through synthetic data generation using Building Information Modelling (BIM) models. This study presents a point-cloud-based deep learning segmentation approach to a 3D light steel framing (LSF) system through synthetic BIM models and as-built data captured using MR headsets. A standardisation workflow between BIM and MR models was introduced to enable seamless data exchange across both domains. A total of five different experiments were set up to identify the benefits of synthetic BIM data in supplementing actual as-built data for model training. The results showed that the average testing accuracy using solely as-built data stood at 82.88%. Meanwhile, the introduction of synthetic BIM data into the training dataset led to an improved testing accuracy of 86.15%. A hybrid dataset also enabled the model to segment both the BIM and as-built data captured using an MR headset at an average accuracy of 79.55%. These findings indicate that synthetic BIM data have the potential to supplement actual data, reducing the costs associated with data acquisition. In addition, this study demonstrates that deep learning has the potential to automate construction monitoring tasks, aiding in the digitization of the construction industry.
... The Virtual Reality Metaverse, on the other hand, completely immerses users in a [24] 2022 √ √ √ Research on the potential of DL and XR integration in construction management 4 [25] 2022 √ Investigating the use of AR in AEC education performance 5 [26] 2022 √ √ Meta-analysis of literature on AR/VR usage in the AEC industry 6 [27] 2022 √ Exploration of using immersive VR to enhance BIM in the AEC sector 8 [28] 2022 √ √ √ Analysis of XR applications, requirements, and development software in the construction project lifecycle 9 [29] 2022 √ Development of a BIM-based AR defect management system for on-site construction inspections 11 [30] 2021 √ Study of BIM-based MR for bridge inspection and maintenance 12 [31] 2021 √ Examination of BIM-enabled VR for sustainability life cycle and cost assessment 14 [32] 2021 √ Research on MR-based dataset generation for learning-based Scan-to-BIM 15 [9] 2021 √ √ √ Assessing the effectiveness of using digital twin technology and reality capture Monitoring and managing construction progress through XR technology. 16 [4] 2020 √ √ √ Investigation of BIM-based XR process for AEC project management 17 [33] 2020 √ Study of MR applications in the AECO industry 19 [34] 2020 √ Research on the integration of BIM, LC, and MR 20 [35] 2019 √ Analysis of design and construction education 21 [36] 2019 √ Examination of construction safety 24 [37] 2018 √ √ Study of collaborative decision making 27 [38] 2018 √ Research on construction safety training and prefabrication 28 [39] 2018 √ Analysis of site survey 29 [40] 2018 √ Evaluation of the effectiveness of BIM and AR 30 [41] 2018 √ Comparison of VR and AR 31 [42] 2018 √ Study of benefits and challenges of VR in the construction industry 32 [43] 2017 √ √ Analysis of construction safety training and jobsite management 33 [44] 2017 √ Examination of building energy performance gap 34 [45] 2017 √ Research on architecture and environmental planning education 35 [46] 2017 √ Analysis of real-time communication and problem solving 36 [47] 2017 √ Research on the potential of DL and XR integration in construction management 37 [48] 2016 √ Investigating the use of AR in AEC education performance 38 [49] 2016 √ Meta-analysis of literature on AR/VR usage in the AEC industry 39 [50] 2015 √ √ √ Exploration of using immersive VR to enhance BIM in the AEC sector 40 [51] 2014 √ Analysis of XR applications, requirements, and development software in the construction project lifecycle 41 [52] 2014 √ Study of VR-based cloud BIM platform for integrated AEC projects 42 [53] 2012 √ Examination of real-time communication and integration of BIM into site and task conditions, and the interaction with the field crew. ...
... One of the latest endeavors for BIM is to integrate with the game engine for various applications in the construction sector, such as collaborative decision-making (Du et al., 2018), interactive design change (Panya et al., 2023), and construction project monitoring (Pour Rahimian et al., 2020). Ratajczak et al. (2019) proposed a working prototype of augmented reality for construction (AR4C). ...
Building Information Modeling (BIM) has unique features that improve safety management in construction by visually identifying potential risks. Integrating BIM with a real-time game engine is a cutting-edge idea for more effective safety management. This study aims to conduct two case studies by integrating BIM data with game engines from two aspects: 1) Construction Safety Training and 2) Pre-construction Safety Management. A framework that covers techniques for extraction of safety ideas, managing the game engine, and character modeling tools and resources is used to carry out the case studies. In the first case study, a construction site was created by Revit, and a real-life scaffolding failure accident was simulated by Unity to warn workers to prevent similar future events. The second case study was conducted on the procedure of evacuation modeling in an emergency, integrating a BIM model and Unity following distinct pathways. This evacuation modeling can be used as a training platform for the occupants to acquaint themselves with the inside facility, show directions of the shortest evacuation path from specific points, and provide necessary information on emergency equipment. Finally, the study explains how the integration of the BIM model and game engine applications can be applied for effective, straightforward, and helpful safety management with the most efficient BIM data transition.
This paper explores the transformative impact of agent-based modeling (ABM) on the architecture, engineering, and construction (AEC) industry, highlighting its indispensable role in revolutionizing project management, construction processes, safety protocols, and sustainability initiatives including energy optimization and occupants’ comfort. Through an in-depth review of 178 documents published between 1970 and 2024 on current practices and the integration of ABM with emerging digital technologies, this study underscores the critical importance of ABM in facilitating enhanced decision-making, resource optimization, and complex system simulations. For instance, ABM is shown to reduce project delays by up to 15% through enhanced resource allocation and improve safety outcomes by simulating worker behavior and identifying potential hazards in dynamic construction environments. The results reveal ABM’s potential to significantly improve construction methodologies, integrate technological advancements seamlessly, and contribute to the development of sustainable and resilient building practices. Furthermore, this paper identifies key areas for future research, including the exploration of ABM’s capabilities in conjunction with other digital innovations to unlock new avenues for efficiency and sustainability in construction. This study sets out a forward-looking agenda for providing this modeling approach to address contemporary challenges and harness opportunities for innovation and growth in the AEC sector.
Purpose
This paper aims to fill the research gap on digital twin technology and its broad applicability during construction by shedding light on its interaction with Building Information Modeling (BIM) from a construction project management perspective. It presented the true potential of the digital twins in the construction phase of the project lifecycle.
Design/methodology/approach
The paper employed a two-step methodology that included a comprehensive synthesis of the literature on digital twins through the construction management lens and a questionnaire survey to assess the impact of digital twin services brought to light on parallel BIM uses.
Findings
The paper provides validated applications and many advantages of the digital twin on construction project management. It suggests that the industry should take advantage of 10 digital twin services introduced to eliminate the low efficiency and lack of productivity that the construction industry is still facing.
Research limitations/implications
The paper is one of the rare and pioneering studies that addresses the interaction between the digital twin and BIM from a construction management perspective with a quantitative approach examining the reflection of academic publications on the industry and their reception among industry professionals.
Practical implications
The paper provides a meaningful definition for the industry by grounding the concept of digital twin in existing technologies and their practical applications. This provides a roadmap for managers to approach the problems and BIM limitations they need to overcome in their companies or projects with tailor-made solutions.
Originality/value
The paper is one of the pioneering quantitative studies that fulfills an identified need to investigate digital twin technology for construction management and its extensive applicability for quickly evolving construction sites.
Offsite construction (OSC) techniques are argued to provide superior quality and shorter schedules compared with traditional techniques. Nonetheless, the pace of OSC implementation has been slow due to the influence of several barriers. In recent years, virtual reality (VR) applications have been used to address many of these barriers and promote the implementation of OSC projects. However, a comprehensive and coherent literature review that establishes the current state and categorizes VR applications in OSC projects is still lacking. To address this research gap, this study provides a state-of-the-art review of VR applications in OSC (VR–OSC) using the scientometric and systematic review methods. This study characterizes the synthesis between VR and OSC and identifies research trends and gaps that can be studied in future VR–OSC research. The scientometric review focuses on identifying the main topics of both research domains separately and combined based on the collected articles. The systematic review, meanwhile, qualitatively evaluates these articles, highlighting the existing research gaps and anticipating future research frontiers. The scientometric results indicate that VR applications in OSC can be organized into a number of clusters, such as Crane Operations and Onsite Planning, Educational Applications, Safety and Ergonomics, and Evaluation of Design Alternatives. The qualitative analysis identifies several future research directions to advance the field of VR–OSC, including (1) multiuser VR models in crane operation planning, (2) consideration of the role of human emotions in VR safety training by adopting biometric sensors, (3) decentralized web-VR platforms for remote OSC planning, and (4) VR-solutions for modeling robotic movements in OSC factories. This study can serve as a useful point of reference for VR–OSC researchers and provides a sound foundation for future research on VR–OSC.
This paper addresses a main gap in the literature: the lack of a comprehensive taxonomy of Digital Twin (DT) applications for the construction phase, and the insufficient conceptualization of the interconnections between DT applications, technologies, and data. Through a systematic review and thematic coding of 112 papers, this paper presents a taxonomy of Digital Twin (DT) applications for construction sites, which includes seven application areas (1. Safety and risk management; 2. Progress monitoring and control; 3. Supply chain and logistics; 4. Quality control and assurance; 5. Data integration and management; 6. Construction robotics and automation; and 7. Sustainability and circular construction”) and 19 uses. The paper then identifies the interplays between each DT application, the five DT technological layers (i.e. sensing; communication; storage; analytics; and visualization), and the data utilized. These findings are crucial for developing DT solutions that effectively tackle the dynamic and complex nature of the construction phase.
Despite Digital Twins' (DTs) growing popularity in the Architecture, Engineering, Construction and Operations sector, currently, only a limited number of studies have focused on the applicability and potential offered by DT to deal with the whole Construction Supply Chain (CSC) challenges, justifying the significance of the present study. As a response to provide a holistic insight into DT's contribution to overcoming CSC challenges, this paper follows an extensive literature review approach. This review aims explicitly to identify the existing applications of DT in dealing with current CSC challenges and explore its possible contributions by investigating examples of other industries that adopted DT to tackle similar challenges. Firstly, it utilises Scopus as a database to collect CSC-related data. Subsequently, it employs VOSviewer to extract and visualise CSC hotspots. Finally, this review conducts extensive discussions to identify the CSC challenges around the identified hotspots and the DT-provided solutions.
Construction Progress Monitoring (CPM) plays a pivotal role in ensuring the timely and cost-effective completion of construction projects. Previous research has classified CPM techniques into manual and automated methods. While traditional manual CPM has been prevalent in the Sri Lankan construction industry, it suffers from several limitations that can impede project success. Despite the significance of CPM, both manual and automated techniques face challenges in implementation. Therefore, the research aims to explore the challenges associated with CPM in the Sri Lankan construction industry. A comprehensive literature review was conducted to establish a theoretical framework. A quantitative research approach was employed, utilising a questionnaire survey with a heterogeneous purposive sampling method, involving 68 respondents. Data analysis was performed using IBM SPSS software. The study revealed different challenges in manual CPM and automated CPM specifically within the Sri Lankan context. One of the key takeaways of this study is that the challenges in manual CPM outweigh those in automated techniques. However, statistical analysis indicated that both manual and automated CPM face significant challenges, as evidenced by a negative skewness in survey data. Automated CPM heavily relies on computer vision technologies, with issues primarily arising from reality-capturing technologies. This study significantly contributes to the existing body of knowledge by identifying and categorising challenges in both manual and automated CPM within the Sri Lankan construction industry. The findings provide a platform for future research endeavours to devise strategies and solutions to address these challenges, ultimately enhancing the efficiency and effectiveness of construction progress monitoring in the industry.
Over the past decade, there has been a dramatic increase in the use of various technologies in the Architecture, Engineering, and Construction sector. Artificial intelligence has played a significant role throughout the different phases of the design and construction process. A growing body of literature recognizes the importance of artificial neural network applications in numerous areas of the construction industry and the built environment, presenting a need to explore the main research themes, attributes, benefits, and challenges. A three-step extensive research method was utilized by conducting a bibliometric search of English language articles and conducting quantitative and qualitative analyses. The bibliometric analysis aimed to identify the current research directions and gaps forming future research areas. The scientometric analysis of keywords revealed diverse areas within the construction industry linked to ANNs. The qualitative analysis of the selected literature revealed that energy management in buildings and construction cost predictions were the leading research topics in the study area. These findings recommend directions for further research in the field, for example, broadening the application ranges of ANNs in the current Construction 4.0 technologies, such as robotics, 3D printing, digital twins, and VR applications.
Automated interior construction progress monitoring (ICPM) has gained increasing academic attention. This emerging research field faces numerous technical challenges that have been noted in previous studies but lack a holistic examination to analyze these challenges and their potential impacts. This study addresses this gap by conducting a systematic review of ICPM technical challenges, collecting related literature from Scopus, Web of Science, and ScienceDirect databases, and utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) framework for filtering and selecting literature. The filtration results in 44 strongly related technical papers. Alongside summarizing these challenges, the study explores their impacts on the entire ICPM automation process and proposes innovative solutions. Specifically, this study highlights the key phases of ICPM automation development, including data acquisition, 3D reconstruction, as-planned modeling, as-built modeling, progress comparison, and progress quantification, and subsequently identifies challenges for each phase, totaling 11 major challenges composed of 41 subchallenges. The data acquisition phase is found to have the most and most severe challenges, and challenges in other phases also impact the automation system performance. This review encompasses the identification of potential issues and proposes corresponding solutions, enabling future researchers to anticipate challenges and develop more advanced and user-friendly monitoring systems.
SUMMARY: In recent years, the construction industry has witnessed a surge in the adoption and application of emerging technologies. Substantial evidence indicates the impact of these technologies on the lifecycle of construction projects. Among the notable categories of technologies embraced in construction, Information and Communication Technologies (ICTs) stand out. This category encompasses BIM models, reality capture, cloud computing, robotics, and more. However, a literature investigation that specifically focuses on the significant influence of these technologies on construction sites is lacking. To address this gap, this study conducted a systematic literature review, supported by a bibliometric analysis of published articles in the field of construction management. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, data was sourced from Scopus and Web of Science, covering the period from 2009 to 2023. The findings shed light on nine prominent ICT tools widely adopted on construction sites for project execution, with contributions from 113 countries emphasizing their profound impacts. Despite these positive aspects, this study also identifies notable challenges and provides recommendations for mitigating them. A smart site data integration framework was developed to facilitate the seamless integration of these technologies while ensuring data security. This study provides fundamental insights for future research in this field aimed at informing the construction industry about the importance of embracing cutting-edge technologies for enhanced efficiency on construction jobsites and for timely project delivery.
The construction industry has made significant improvements in adopting and integrating emerging technologies. One example is the wide use of Building Information Modeling (BIM), which offers numerous benefits to the construction industry. BIM assists project teams in achieving better quality with lower risk, shorter timeframes, and reduced costs. Meanwhile, the Project Management Body of Knowledge (PMBOK) defines the required discipline to meet stakeholders' needs. PMBOK aims to facilitate the achievement of predefined project deliverables and improve project goals through effective communication and decision-making processes. This paper presents a questionnaire-based investigation to measure whether BIM implementation enhances the PMBOK in projects and whether BIM positively affects Project Management Knowledge Areas (PMKAs) on a global scale. The questionnaire consisted of 7 general questions, 37 PMBOK-oriented questions, and 4 questions targeting BIM advantages and obstacles. 95 experts participated in the survey based on the LinkedIn search and survey invitation. The results showed that BIM dramatically influences the quality of execution of PMKAs, specifically in Project Integration Management. Additionally, the study examined the effect of BIM on the product lifecycle that BIM improves project coordination and discovers clashes during the engineering phase. In the construction phase, BIM facilitates as-built drawings and enhances communication, and during the post-construction phase, BIM enhances facility and components' lifecycle management. The research also found that the most significant obstacles to implementing BIM are the lack of experts, value propositions, and efficient training.
The recent rise in the applications of advanced technologies in the sustainable design and construction of transportation infrastructure demands an appropriate medium for their integration and utilization. The relatively new concept of Civil Integrated Management (CIM) is such a medium; it enhances the development of digital twins for infrastructure and also embodies various practices and tools, including the collection, organization, and data-management techniques of digital data for transportation infrastructure projects. This paper presents a comprehensive analysis of advanced CIM tools and technologies and categorizes its findings into the following research topics: application of advanced surveying methods (Advanced Surveying); geospatial analysis tools for project planning (Geospatial Analysis); multidimensional virtual design models (nD Modeling); Integrated Geospatial and Building Information Modeling (GeoBIM); and transportation infrastructure maintenance and rehabilitation planning (Asset Management). Despite challenges such as modeling complexity, technology investment, and data security, the integration of GIS, BIM, and artificial intelligence within asset-management systems hold the potential to improve infrastructure’s structural integrity and long-term performance through automated monitoring, analysis, and predictive maintenance during its lifetime.
Livro de atas do Congresso ptBIM 2024, onde se promove a discussão técnico-científica em língua Portuguesa da metodologia ‘Building Information Modelling’ (BIM), envolvendo a participação ativa das comunidades profissional e académica das áreas de Arquitetura, Engenharia e Construção. Pretende-se enfatizar os problemas e esforços de implementação BIM no Ambiente Construído e reforçar as redes de profissionais que incorporam práticas BIM nas suas atividades. https://ptbim.org/
University in partial fulfilment of the requirements for an MSc in Construction Management with BIM ABSTRACT Cost estimation is considered to be one of the most significant part of a project planning process. The purpose of the research was to identify the capabilities of building information modelling in cost estimation. And also to find how betterment can be made in the process of integrated project delivery through these costing strategies the research has addressed the major issues of cost overturn and poor costing structure by developing an adaptable strategy and proposing an adaptable model. The findings proposed by the research identification and exploitation of the building information modelling capabilities and drawbacks of the current costing system also the activity-based costing, Target value analysis and Monte Carlo simulation strategies and their effectiveness are found to be less effective. So conclusions can be drawn that the industry has to incorporate BIM more into the costing process along with exploiting its potential to the maximum. There is a need for managemental initiatives to be drawn for better encouragement and support for the employees of the industry. Recommendations are made on the basis of analysis of the data obtained from surveys, archive studies and case studies which state that construction stages of the projects are of supreme importance and proper and accurate cost estimation is to be done with the help of BIM and IPD strategies that would help to upgrade the construction industry and tackle the current dilemmas. DECLARATION I declare that this thesis is entirely my own work and that any use of the work of others has been appropriately acknowledged as in-text citations and complied with in the references list. I also confirm that the project has been conducted in compliance with the University's research ethics policy and evidence of this has been included in my thesis I agree that the project thesis can be made available as a
There have been numerous simulation tools utilised for calculating building energy loads for efficient design and retrofitting. However, these tools entail a great deal of computational cost and prior knowledge to work with. Machine Learning (ML) techniques can contribute to bridging this gap by taking advantage of existing historical data for forecasting new samples and lead to informed decisions. This study investigated the accuracy of most popular ML models in the prediction of buildings heating and cooling loads carrying out specific tuning for each ML model and using two simulated building energy data generated in EnergyPlus and Ecotect and compared the results. The study used a grid-search coupled with cross-validation method to examine the combinations of model parameters. Furthermore, sensitivity analysis techniques were used to evaluate the importance of input variables on the performance of ML models. The accuracy and time complexity of models in predicting heating and cooling loads are demonstrated. Comparing the accuracy of the tuned models with the original research works reveals the significant role of model optimisation. The outcomes of the sensitivity analysis are demonstrated as relative importance which resulted in the identification of unimportant variables and faster model fitting.
As a result of progressive use of BIM in the AEC sector, the amount of diverse project information is increasing rapidly, thus necessitating interoperability of tools, compatibility of data, effective collaboration and sophisticated data management. Media-rich VR and AR environments have been proven to help users better understand design solutions, however, they have not been quite advanced in supporting interoperability and collaboration. Relying on capabilities of openBIM and IFC schema, this study posits that this shortcoming of VR and AR environment could be addressed by use of BIM server concept allowing for concurrent multiuser and low-latency communication between applications. Successful implementation of this concept can ultimately mitigate the need for advanced technical skills for participation in design processes and facilitate the generation of more useful design solutions by early involvement of stakeholders and end-users in decision making. This paper exemplifies a method for integration of BIM data into immersive VR and AR environments, in order to streamline the design process and provide a pared-down agnostic openBIM system with low latency and synchronised concurrent user accessibility that gives the "right information to the right people at the right time". These concepts have been further demonstrated through development of a prototype for openBIM-Tango integrated virtual showroom for offsite manufactured production of self-build housing. The prototype directly includes BIM models and data from IFC format and interactively presents them to users on both VR immersive and AR environments, including Google Tango enabled devices. This paper contributes by offering innovative and practical solutions for integration of openBIM and VR/AR interfaces, which can address interoperability issues of the AEC industry.
Abstract Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy efficiency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most effective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy efficiency at a very early design stage. On the other hand,efficient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, artificial intelligence (AI) in general and machine learning (ML) techniques in specific terms have been proposed for forecasting of building energy consumption and performance. This paper provides a substantial review on the four main ML approaches including artificial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance.
Defects and errors in new or recently completed construction work continually pervade the industry. Whilst inspection and monitoring processes are established vehicles for their 'control', the procedures involved are often process driven, time consuming, and resource intensive. Paradoxically therefore, they can impinge upon the broader aspects of project time, cost and quality outcomes. Acknowledging this means appreciating concatenation effects such as the potential for litigation, impact on other processes and influence on stakeholders' perceptions—that in turn, can impede progress and stifle opportunities for process optimisation or innovation. That is, opportunities relating to for example, logistics, carbon reduction, health and safety, efficiency, asset underutilisation and efficient labour distribution. This study evaluates these kinds of challenge from a time, cost and quality perspective, with a focus on identifying opportunities for process innovation and optimisation. It reviews—within the construction domain—state of the art technologies that support optimal use of artificial intelligence, cybernetics and complex adaptive systems. From this, conceptual framework is proposed for development of real-time intelligent observational platform supported by advanced intelligent agents, presented for discussion. This platform actively, autonomously and seamlessly manages intelligent agents (Virtual Reality cameras, Radio-Frequency Identification RFID scanners, remote sensors, etc.) in order to identify, report and document 'high risk' defects. Findings underpin a new ontological model that supports ongoing development of a dynamic, self-organised sensor (agent) network, for capturing and reporting real-time construction site data. The model is a 'stepping stone' for advancement of independent intelligent agents, embracing sensory and computational support, able to perform complicated (previously manual) tasks that provide optimal, dynamic, and autonomous management functions.
On-site construction inspection for progress monitoring is a manual, time consuming and labour intensive pro-cess consumed by exhaustive manual extraction of data from drawings and databases. Efforts have been made to facilitate the inspection process by using emerging technologies such as Augmented Reality (AR). AR based systems can simplify and reduce the time of inspection by providing the inspector with instantane-ous access to the information stored in the Building Information Modelling (BIM). However, precise align-ment between the BIM model and the real world scene is still a challenge. For estimating the position and ori-entation of the user, methods have been proposed that either use markers or confine the user to a specific loca-tion, or use Global Positioning System (GPS) which cannot operate efficiently in an indoor environment. This paper presents an evaluation of different methods that could potentially be used for a marker-less BIM regis-tration in AR. We implemented and tested line, edge, and contour detection algorithms using images, data from LSD and ORB Simultaneous Localisation and Mapping (SLAM) methods and 3D and positioning data from Kinect sensor and Google Project Tango. The results indicate that sparse 3D data is the input dataset that leads to the most robust results when combined with XYZ method.
A growing awareness in the construction industry has emerged to pay a sharp attention to ICT as a catalyst that would mitigate the deficiencies characterized by this industry. In comparison with other cited review articles, this paper is aimed to 1) compile the research published on “ICT Technologies” in correspondence to “Construction Tasks” in the construction industry over the past two decades (1996- 2016), 2) demonstrate the trends and patterns in the use of different types of ICT Technologies 3) discuss the correspondence of the identified ICT Technologies to the identified Construction Tasks and 4) exhibit the construction needs of ICT. By the employment of a five phases profiling methodology, a set of 68 out of 202 articles and papers indexed by Elsevier’s Scopus database was considered relevant for the current review paper. This research is targeting the beginning researchers and practitioners in the field of ICT in construction
Construction safety management has been a popular issue in research and practice in recent years due to the high accident and death rates in the construction industry. The complexity and variability of construction sites makes safety management more difficult to implement than in other industries. As a promising technology, visualization has been extensively explored to aid construction safety management. However, a comprehensive critical review of the visualization technology in construction safety management is absent in the literature.
In this paper we address the problem of semantic labeling of indoor scenes on RGB-D data. With the availability of RGB-D cameras, it is expected that additional depth measurement will improve the accuracy. Here we investigate a solution how to incorporate complementary depth information into a semantic segmentation framework by making use of convolutional neural networks (CNNs). Recently encoder-decoder type fully convolutional CNN architectures have achieved a great success in the field of semantic segmentation. Motivated by this observation we propose an encoder-decoder type network, where the encoder part is composed of two branches of networks that simultaneously extract features from RGB and depth images and fuse depth features into the RGB feature maps as the network goes deeper. Comprehensive experimental evaluations demonstrate that the proposed fusion-based architecture achieves competitive results with the state-of-the-art methods on the challenging SUN RGB-D benchmark obtaining 76.27% global accuracy , 48.30% average class accuracy and 37.29% average intersection-over-union score.
This work presents a Multi-View Stereo system for robust and efficient dense modeling from unstructured image collections. Our core contributions are the joint estimation of depth and normal information , pixelwise view selection using photometric and geometric priors , and a multi-view geometric consistency term for the simultaneous refinement and image-based depth and normal fusion. Experiments on benchmarks and large-scale Internet photo collections demonstrate state-of-the-art performance in terms of accuracy, completeness, and efficiency.
Incremental Structure-from-Motion is a prevalent strategy for 3D reconstruction from unordered image collections. While incremental reconstruction systems have tremendously advanced in all regards, robustness, accuracy , completeness, and scalability remain the key problems towards building a truly general-purpose pipeline. We propose a new SfM technique that improves upon the state of the art to make a further step towards this ultimate goal. The full reconstruction pipeline is released to the public as an open-source implementation.
Over the past few years, the application of camera-equipped Unmanned Aerial Vehicles (UAVs) for visually monitoring construction and operation of buildings, bridges, and other types of civil infrastructure systems has exponentially grown. These platforms can frequently survey construction sites, monitor work-in-progress, create documents for safety, and inspect existing structures, particularly for hard-to-reach areas. The purpose of this paper is to provide a concise review of the most recent methods that streamline collection, analysis, visualization, and communication of the visual data captured from these platforms, with and without using Building Information Models (BIM) as a priori information. Specifically, the most relevant works from Civil Engineering, Computer Vision, and Robotics communities are presented and compared in terms of their potential to lead to automatic construction monitoring and civil infrastructure condition assessment.
This research discusses the implementation of Radio Frequency Identification (RFID) and Virtual Reality (VR) simulations in construction processes and projects. The purposes are to improve the control and monitor of construction projects and to find the optimized solutions for construction processes. The authors have implemented a Building Information Model (BIM) and used it in a VR world. The details of the BIM model are the basis for the optimization analysis in construction. Using VR technology in the analysis helps the architect, engineer, and contractor (A/E/C) team to understand construction projects and plans. The team can gain experience by developing and critiquing construction sequences in a full-scale virtual environment. The team can also gain interactive operation, simulation, and optimization experiences. VR simulations in construction should not only focus on the pre-construction stage, but also record the site changes and update the system in real time. The real-time VR simulation in this study can be used throughout the entire construction process. In particular, the technology will help in the optimization of steel installation sequencing during construction. The real-time VR simulation is done via the use of RFID and its related database. In this study, the authors are experimenting with the use of a data hub for staging and cleansing the data before feeding it into the BIM database. This BIM/RFID enriched VR environment improves the straight forward representations which VR simulation can provide, automates construction data collection by acquiring RFID in real time, and accomplishes data integration through the use of BIM. The A/E/C team can use the system to explore different construction sequences, temporarily re-arrange facility or equipment locations, coordinate trades, identify safety issues, and improve constructability.
This paper presents a new appearance-based material classification method for monitoring construction progress deviations at the operational-level. The method leverages 4D Building Information Models (BIM) and 3D point cloud models generated from site photologs using Structure-from-Motion techniques. To initialize, a user manually assigns correspondences between the point cloud model and BIM, which automatically brings in the photos and the 4D BIM into alignment from all camera viewpoints. Through reasoning about occlusion, each BIM element is back-projected on all images that see that element. From these back-projections, several 2D patches are sampled per element and are classified into different material types. To perform material classification, the expected material type information is derived from BIM. Then the image patches are convolved with texture and color filters and their concatenated vector-quantized responses are compared with multiple discriminative material classification models that are relevant to the expected progress of that element. For each element, a quantized histogram of the observed material types is formed and the material type with the highest appearance frequency infers the appearance and thus the state of progress. To validate, four new datasets of incomplete and noisy point cloud models are introduced which are assembled from real-world construction site images and BIMs. An extended version of the Construction Material Library (CML) is also introduced for training/testing the material classifiers. The material classification shows an average accuracy of 92.4% for CML image patches of 100 × 100 pixels. The experiments on those four datasets show an accuracy of 95.9%, demonstrating the potential of appearance-based recognition method for inferring the actual state of construction progress for BIM elements.
On-site progress monitoring is essential for keeping track of the ongoing work on construction sites. Currently, this task is a manual, time-consuming activity. The research presented here, describes a concept for an automated comparison of the actual state of construction with the planned state for the early detection of deviations in the construction process. The actual state of the construction site is detected by photogrammetric surveys. From these recordings, dense point clouds are generated by the fusion of disparity maps created with semi-global-matching (SGM). These are matched against the target state provided by a 4D Building Information Model (BIM). For matching the point cloud and the BIM, the distances between individual points of the cloud and a componentÕs surface are aggregated using a regular cell grid. For each cell, the degree of coverage is determined. Based on this, a confidence value is computed which serves as basis for the existence decision concerning the respective component. Additionally, process- and dependency-relations are included to further enhance the detection process. Experimental results from a real-world case study are presented and discussed.
Building Information Models (BIMs) are becoming the official standard in the construction industry for
encoding, reusing, and exchanging information about structural assets. Automatically generating such
representations for existing assets stirs up the interest of various industrial, academic, and governmental
parties, as it is expected to have a high economic impact. The purpose of this paper is to provide a general
overview of the as-built modelling process, with focus on the geometric modelling side. Relevant works
from the Computer Vision, Geometry Processing, and Civil Engineering communities are presented and
compared in terms of their potential to lead to automatic as-built modelling.
Construction phase still presents many difficulties and contingencies related to the project documents information management between the actors involved. Different studies investigated methods and models to optimize the operational management of the construction phase. Concerning workers’ health and safety protection most of them focused on the development of on-site and in real-time control systems. Therefore, the authors identified the exigency of a prevention information system based on workers’ involvement. To reach the aim three objectives have been identified: (1) the check of workers’ training, (2) the definition of works operational procedures and (3) the schematization of the assembly/disassembly/use of site equipment and temporary structures. The developed system is characterized by a synergic application of BIM model and QR-code and it has been applied on a case study. At the same time a questionnaire has been developed and proposed to different subjects of the construction sector in order to evaluate the potential use of BIM model and QR-code. The field test has allowed to demonstrate the practical use of the tool, enhancing communication between the Client technical structure, the General contractor and sub-contractors, improving workers’ integration and participation and allowing a better availability of on-site health and safety information and documents.
The problem of housing delivery is of great concern in many countries of the world. This problem is more predominant in developing countries and Nigeria is not an exception. In Nigeria, this challenge has been magnified as a result of myriad of issues, e.g. high population growth rate, shortage of necessary skills, disintegrated supply chain etc. Seminal literature has proven that offsite manufacturing (OSM) can help improve housing delivery efforts both in terms of quantity and quality. The purpose of the research was to investigate the current housing delivery problems in Nigeria and evaluate the feasibility of adopting OSM. To achieve this, the study conducted substantial literature review to explore the benefits of OSM, identify the problems of housing delivery in Nigeria and explore different dimensions and the issues that can be associated with using OSM in Nigeria. The results revealed that there are a good number of benefits promised by OSM; notable among these benefits are: less wastage on site, faster construction time, quality improvement and reduction in wet trades. Moreover, some of the problems of housing delivery in Nigeria identified included: skills shortage, reliance on conventional construction techniques, slow pace of construction, and low quality of housing. The study also showed that adaptation of OSM has been quite useful in other countries facing the similar issues of housing shortage, e.g. Malaysia. As such, it is argued that OSM can improve skills shortages, improve the speed of construction, improve the overall quality of housing and help eliminate wet trades. For the problems of housing delivery to be tackled in Nigeria, it is important to undertake additional research to identify the benefits, barriers and context-specific ways of adopting OSM in Nigeria.
As-built spatial data are useful in many construction-related applications, such as quality control and progress monitoring. These data can be collected using a number of imaging and time-of-flight-based (e. g., laser scanning) sensor methods. Each application will demand a particular level of data accuracy and quality, yet little information is available to help engineers choose the most cost-effective approach. This paper presents an analytical and quantitative comparison of photogrammetric, videogrammetric, and time-of-flight-based methods. This comparison is done with respect to accuracy, quality, time efficiency, and cost. To this end, representative image-based three-dimensional reconstruction software and commercially available hardware (two cameras and a time-of-flight-based laser scanner) are evaluated. Spatial data of typical infrastructure (two bridges and a building) are collected under different settings. The experimental parameters include camera type, resolution, and shooting distance for the imaging sensors. By comparing these data with the ground truth collected by a total station, it is revealed that video/photogrammetry can produce results of moderate accuracy and quality but at a much lower cost as compared to laser scanning. The obtained information is useful to help engineers make cost-effective decisions and help researchers better understand the performance impact of these settings for the sensor technologies. DOI: 10.1061/(ASCE)CO.1943-7862.0000565. (C) 2013 American Society of Civil Engineers.
In this paper we study the problem of object detection for RGB-D images using
semantically rich image and depth features. We propose a new geocentric
embedding for depth images that encodes height above ground and angle with
gravity for each pixel in addition to the horizontal disparity. We demonstrate
that this geocentric embedding works better than using raw depth images for
learning feature representations with convolutional neural networks. Our final
object detection system achieves an average precision of 37.3%, which is a 56%
relative improvement over existing methods. We then focus on the task of
instance segmentation where we label pixels belonging to object instances found
by our detector. For this task, we propose a decision forest approach that
classifies pixels in the detection window as foreground or background using a
family of unary and binary tests that query shape and geocentric pose features.
Finally, we use the output from our object detectors in an existing superpixel
classification framework for semantic scene segmentation and achieve a 24%
relative improvement over current state-of-the-art for the object categories
that we study. We believe advances such as those represented in this paper will
facilitate the use of perception in fields like robotics.
Background
Global competition and the transdisciplinary nature of evolving Architecture-Engineering-Construction (AEC) activities makes it progressively important to educate new AEC professionals with appropriate skill sets. These skills include the ability and capability of not only developing routine projects, but also delivering novel design solutions and construction processes (some of which may be unknown), to feasible, surprising, or potentially patentable solutions. For example, despite recent innovations in immersive visualisation technologies and tele-presence decision-support toolkits, the AEC sector as a whole has not yet fully understood these technologies, nor embraced them as an enabler.
Methods
Given this, this paper proposes a new approach for delivering education and training to address this shortcoming. This approach focuses on doing traditional (routine) work with creative thinking in order to address these challenges. This rationale is based on the principles of Successful Education as a new paradigm for engineering education, which is inspired by the Theory of Successful Intelligence, by the Medici Effect and Leonardo da Vinci’s Seven Principles. The paper presents the educating AEC professionals is presented the AEC sector. The Theory of Successful Intelligence and its three forms of intelligence (Practical, Analytical, and Creative), are supported by lessons learned from the Renaissance, including the Medici Effect and da Vinci’s Seven Principles.
Results
Based on these theoretical pillars, a new approach to educating AEC professionals is presented with a proof-of-concept prototype that uses a game-like virtual reality (VR) visualisation interface supported by Mind Mapping is introduced as an exemplar.
Conclusion
The developed interface in this study applies Game Theory to non-collocated design teams in accordance with Social Sciences Theory (social rules) and Behavioural Science Theory (decision making). It contributes by supporting new insights into AEC actor involvement, pedagogy, organisational behaviour, and the social constructs that support decision making.
We describe two new approaches to human pose estimation. Both can quickly and accurately predict the 3D positions of body joints from a single depth image without using any temporal information. The key to both approaches is the use of a large, realistic, and highly varied synthetic set of training images. This allows us to learn models that are largely invariant to factors such as pose, body shape, field-of-view cropping, and clothing. Our first approach employs an intermediate body parts representation, designed so that an accurate per-pixel classification of the parts will localize the joints of the body. The second approach instead directly regresses the positions of body joints. By using simple depth pixel comparison features and parallelizable decision forests, both approaches can run super-real time on consumer hardware. Our evaluation investigates many aspects of our methods, and compares the approaches to each other and to the state of the art. Results on silhouettes suggest broader applicability to other imaging modalities.
In this paper, we propose a framework for interactive image segmentation. The goal of interactive image segmentation is to classify the image pixels into foreground and background classes, when some foreground and background markers are given. The proposed method minimizes a min–max Bayesian criterion that has been successfully used on image segmentation problem and it consists of several steps in order to take into account visual information as well as the given markers, without any requirement of training. First, we partition the image into contiguous and perceptually similar regions (superpixels). Then, we construct a weighted graph that represents the superpixels and the connections between them. An efficient algorithm for graph clustering based on synthetic coordinates is used yielding an initial map of classified pixels. This method reduces the problem of graph clustering to the simpler problem of point clustering, instead of solving the problem on the graph data structure, as most of the known algorithms from literature do. Finally, having available the data modeling and the initial map of classified pixels, we use a Markov Random Field (MRF) model or a flooding algorithm to get the image segmentation by minimizing a min–max Bayesian criterion. Experimental results and comparisons with other methods from the literature are presented on LHI, Gulshan and Zhao datasets, demonstrating the high performance and accuracy of the proposed scheme.
A number of methods are commonly used today to collect infrastructure's spatial data (time-of-flight, visual triangulation, etc.). However, current practice lacks a solution that is accurate, automatic, and cost-efficient at the same time. This paper presents a videogrammetric framework for acquiring spatial data of infrastructure which holds the promise to address this limitation. It uses a calibrated set of low-cost high resolution video cameras that is progressively traversed around the scene and aims to produce a dense D point cloud which is updated in each frame. It allows for progressive reconstruction as opposed to point-and-shoot followed by point cloud stitching. The feasibility of the framework is studied in this paper. Required steps through this process are presented and the unique challenges of each step are identified. Results specific to each step are also presented.
Concrete pouring represents a major critical path activity that is often affected by design limitations, structural considerations and on-site operational constraints. As such, meticulous planning is required to ensure that both the aesthetic and structural integrity of joints between cast in-situ components is achieved. Failure to adequately plan concrete pouring could lead to structural defects, construction rework or structural instability, all having major financial implications. Given the inherent complexity of large-scale construction projects, the 'manual planning' of concrete pouring is a challenging task and prone to human errors. Against this backdrop, this study developed 4D Building Information Management (BIM) approach to facilitate automated concrete joint positioning solution (as a proof of concept) for design professionals and contractors. The study first developed structural model in Revit, then extracted spatial information regarding all construction joints and linked them to dynamic Microsoft (MS) Excel and Matlab spreadsheets using integration facilitated by Dynamo software. Midspan points of each beam as well as floor perimeter information were gathered via codes developed in MS Excel macros. Based on the Excel outputs, Matlab programming was used to determine best concreating starting points and directions, and daily allowed concrete volume, considering limitations due to cold joints. These information were then pushed back to Revit via Dynamo in order to develop daily concrete scheduling. The developed automated programme framework offers a cost-effective and accurate methodology to address the limitations and inefficiencies of traditional methods of designing construction joints and planning pours. This framework extends the body of knowledge by introducing innovative solutions to integrate structural design considerations, constructional procedures and operational aspects for mitigating human error, and providing a novel, yet technically sound, basis for further application of BIM in structural engineering.
The automation of digital twinning for existing reinforced concrete bridges from point clouds remains an unresolved problem. Whilst current methods can automatically detect bridge objects in point clouds in the form of labelled point clusters, the fitting of accurate 3D shapes to point clusters remains largely human dependent largely. 95% of the total manual modelling time is spent on customizing shapes and fitting them correctly. The challenges exhibited in the fitting step are due to the irregular geometries of existing bridges. Existing methods can fit geometric primitives such as cuboids and cylinders to point clusters, assuming bridges are comprised of generic shapes. However, the produced geometric digital twins are too ideal to depict the real geometry of bridges. In addition, none of the existing methods have explicitly demonstrated how to evaluate the resulting Industry Foundation Classes bridge data models in terms of spatial accuracy using quantitative measurements. In this article, we tackle these challenges by delivering a slicing-based object fitting method that can generate the geometric digital twin of an existing reinforced concrete bridge from four types of labelled point cluster. The quality of the generated models is gauged using cloud-to-cloud distance-based metrics. Experiments on ten bridge point cloud datasets indicate that the method achieves an average modelling distance of 7.05 cm (while the manual method achieves 7.69 cm), and an average modelling time of 37.8 seconds. This is a huge leap over the current practice of digital twinning performed manually.
This paper takes a longitudinal view of literature to explain the current period as disruptive technology drives an evolutionary adaptation of the construction industry in a historical socio-technological process. The authors argue the way Internet of Things (IoT) solutions are conceived as singularly focused “point solutions” undermine future opportunities. An evolutionary view is overlooked because extant literature describes technology in a particular epoch. An ecosystem perspective needs to influence IT strategy as an emerging “digital layer” transcends a smart city and continues to function long after a traditional construction project completes. We describe innovation as a succession of transformational waves in an evolutionary process that is currently manifesting as “Industry 4.0” and changing expectations for the construction industry. The paper concludes by listing emerging trends and warns existing UK construction companies must understand the transformational process they are in and learn how to adapt with a stronger drive for R&D.
We address the problem of hole filling in depth images, obtained from either active or stereo sensing, for the purposes of depth image completion in an exemplar-based framework. Most existing exemplar-based inpainting techniques, designed for color image completion, do not perform well on depth information with object boundaries obstructed or surrounded by missing regions. In the proposed method, using both color (RGB) and depth (D) information available from a common-place RGB-D image, we explicitly modify the patch prioritization term utilized for target patch ordering to facilitate improved propagation of complex texture and linear structures within depth completion. Furthermore, the query space in the source region is constrained to increase the efficiency of the approach compared to other exemplar-driven methods. Evaluations demonstrate the efficacy of the proposed method compared to other contemporary completion techniques.
Vision-based monitoring methods have been actively studied in the construction industry because they can be used to automatically generate information related to progress, productivity, and safety. Object detection is essentially used in such monitoring methods to infer jobsite context. However, as many classes of construction entities exist in a job site, large amounts of image data are required to train a detection algorithm to detect each class object in images. Although image data augmentation methods using 3D models were proposed, publicly available 3D models are limited to some construction object classes. Therefore, this study proposes a three-dimensional reconstruction method to generate the image data required for training object detectors. To use the generated synthetic images as training data, a histogram of oriented gradient (HOG) descriptor of a target object is obtained from these images. The descriptor is refined by a support vector machine to increase sensitivity to the target object in test images. The performance of the HOG-based object detector is evaluated using real images from ImageNet. The result shows that the proposed method can generate training data more effectively than existing manual data collection practices.
Information and communications technology (ICT) has had major effects on the architecture, engineering, and construction (AEC) research fields in recent decades, but a comprehensive and in-depth review of how ICT has been used to enable different modes of information flow on project sites is missing from the current literature. To fill this gap, this paper defines a systematic approach for classifying information flow modes and uses this method to determine trends in information communication modes reported in recent publications. These trends were determined through the identification and analysis of 119 journal articles published between 2005 and 2015 in order to determine the mode of information flow reported. The results show that the majority of papers (70.6%) report a unidirectional flow of information, while a much smaller portion report one of two bidirectional information flow modes (26.9% non-automated, and 2.5% automated). The contribution of this work is in systematically defining current trends in ICT publications related to information flow and also in the identification of the typical technologies used to enable these communication modes.
The ever increasing volume of visual data due to recent advances in smart devices and camera-equipped platforms provides an unprecedented opportunity to visually capture actual status of construction sites at a fraction of cost compared to other alternatives methods. Most efforts on documenting as-built status, however, stay at collecting visual data and updating BIM. Hundreds of images and videos are captured but most of them soon become useless without properly being localized with plan document and time. To take full advantage of visual data for construction performance analytics, three aspects (reliability, relevance, and speed) of capturing, analyzing, and reporting visual data are critical. This paper 1) investigates current strategies for leveraging emerging big visual data and BIM in construction performance monitoring from these three aspects, 2) characterizes gaps in knowledge via case studies and structures a road map for research in visual sensing and analytics.
