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Understanding digital construction management
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Machine learning (ML) is a purpose technology already starting to transform the global economy and has the potential to transform the construction industry with the use of data-driven solutions to improve the way projects are delivered. Unrealistic productivity predictions cause increased delivery cost and time. This study shows the application of supervised ML algorithms on a database including 1,977 productivity measures that were used to train, test, and validate the approach. Deep neural network (DNN), k-nearest neighbours (KNN), support vector machine (SVM), logistic regression, and Bayesian networks are used for predicting productivity by using a subjective measure (compatibility of personality), together with external and site conditions and other workforce characteristics. A case study of a masonry project is discussed to analyse and predict task productivity.
Energy consumption in buildings is a significant cost to the building’s operation. As faults are introduced to the system, building energy consumption may increase and may cause a loss in occupant productivity due to poor thermal comfort. Research towards automated fault detection and diagnostics has accelerated in recent history. Rule-based methods have been developed for decades to great success, but recent advances in computing power have opened new doors for more complex processing techniques which could be used for more accurate results. Popular machine learning algorithms may often be applied in both unsupervised and supervised contexts, for both classification and regression outputs. Significant research has been performed in all permutations of these divisions using algorithms such as support vector machines, neural networks, Bayesian networks, and a variety of clustering techniques. An evaluation of the remaining obstacles towards widespread adoption of these algorithms, in both commercial and scientific domains, is made. Resolutions for these obstacles are proposed and discussed.
This article presents a state-of-the-art review of the applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in building and construction industry 4.0 in the facets of architectural design and visualization; material design and optimization; structural design and analysis; offsite manufacturing and automation; construction management, progress monitoring, and safety; smart operation, building management and health monitoring; and durability, life cycle analysis, and circular economy. This paper presents a unique perspective on applications of AI/DL/ML in these domains for the complete building lifecycle, from conceptual stage, design stage, construction stage, operational and maintenance stage until the end of life. Furthermore, data collection strategies using smart vision and sensors, data cleaning methods (post-processing), data storage for developing these models are discussed, and the challenges in model development and strategies to overcome these challenges are elaborated. Future trends in these domains and possible research avenues are also presented.
The construction industry is one of the world's largest industries, with an annual budget of $10 trillion globally. Despite its size, the efficiency and growth in labour productivity in the construction industry have been relatively low compared to other sectors, such as manufacturing and agriculture. To this extent, many studies have recognised the role of automation in improving the efficiency and safety of construction projects. In particular, automated monitoring of construction sites is a significant research challenge. This paper provides a comprehensive review of recent research on the real-time monitoring of construction projects. The review focuses on sensor technologies and methodologies for real-time mapping, scene understanding, positioning, and tracking of construction activities in indoor and outdoor environments. The review also covers various case studies of applying these technologies and methodologies for real-time hazard identification, monitoring workers’ behaviour, workers’ health, and monitoring static and dynamic construction environments.
Digital technologies have recently started to enter the construction industry, gradually changing how infrastructure, real estate and other built assets are designed, constructed, operated and maintained. Being among the least digitalized sectors, it is predicted that digital technologies will substantially increase the productivity, decrease the costs, and improve site safety of construction projects. Thus, the primary objective of this pilot study is to explore the usage of digital technologies for the 12 components of construction project management. A total of 32 respondents participated in the online survey. The results indicate that the usage of digital technologies was significantly higher in the management of scheduling, documenting, designing, and assigning costs. The aspects for digitalized safety, stakeholders, equipment, and materials are room for further improvement.
Productivity in the US construction industry has been widely reported to be on the decline and among several causes identified, the lack of innovation in project management practices has been described as having led to lost productivity, especially in the face of growing project complexity and the increasing need for enhanced stakeholder collaboration. Digitalization has the potential to improve project management and thus help in reversing this decline in productivity. However, several public agencies have not fully adopted construction or document management software, known herein as digital construction-phase information management (DCIM) systems. This paper identifies the potential positive impacts that stem from the use of such technologies towards project management with the goal of incentivizing their use. Eighteen such potential positive impacts of DCIM systems on project management for public owners were identified and verified using a systematic literature review and statistical analysis of survey responses from industry professionals. Furthermore, a comparison between two representative types of public owners was performed to identify how owner preferences vary in the industry. The results show an overall agreement amongst end-users regarding the identified potential positive impacts of DCIM systems. The result further identified the top five potential positive impacts with a significant strong agreement. The analysis also identified differences in what impacts were most significant to the two types of tested sub-populations. These findings can enhance project owners’ understanding of the potential positive impacts brought using digitalization in the project administration and delivery process. This research can also enhance DCIM systems developers’ understanding of the needs of end-users, particularly project owners, and guide the development of future solutions for project administration and delivery.
Cloud computing technologies have revolutionised several industries for several years. Although the construction industry is well placed to leverage these technologies for competitive and operational advantage, the diffusion of the technologies in the industry follows a steep curve. This study therefore highlights the current contributions and use cases of cloud computing in construction practices. As such, a systematic review was carried out using ninety-two (92) peer-reviewed publications, published between 2009 and 2019. A key highlight of the findings is that cloud computing is an innovation delivery enabler for other emerging technologies (building information modelling, internet of things, virtual reality, augmented reality, big data analytics) in the construction industry. As such, this paper brings to the fore, current and future application areas of cloud computing in the construction industry. The paper also identifies barriers to broader adoption of cloud computing in the construction industry and discusses strategies for overcoming these barriers.
As the Architecture, Engineering and Construction sector is embracing the digital age, the processes involved in the design, construction and operation of built assets are more and more influenced by technologies dealing with value-added monitoring of data from sensor networks, management of this data in secure and resilient storage systems underpinned by semantic models, as well as the simulation and optimisation of engineering systems. Aside from enhancing the efficiency of the value chain, such information-intensive models and associated technologies play a decisive role in minimising the lifecycle impacts of our buildings. While Building Information Modelling provides procedures, technologies and data schemas enabling a standardised semantic representation of building components and systems, the concept of a Digital Twin conveys a more holistic socio-technical and process-oriented characterisation of the complex artefacts involved by leveraging the synchronicity of the cyber-physical bi-directional data flows. Moreover, BIM lacks semantic completeness in areas such as control systems, including sensor networks, social systems, and urban artefacts beyond the scope of buildings, thus requiring a holistic, scalable semantic approach that factors in dynamic data at different levels. The paper reviews the multi-faceted applications of BIM during the construction stage and highlights limits and requirements, paving the way to the concept of a Construction Digital Twin. A definition of such a concept is then given, described in terms of underpinning research themes, while elaborating on areas for future research.
Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.
The application of additive manufacturing (AM) in construction has been increasingly studied in recent years. Large robotic arm- and gantry-systems have been created to print building parts using aggregate-based materials, metals, or polymers. Significant benefits of AM are the automation of the production process, a high degree of design freedom, and the resulting potential for optimization. However, the building components and 3D-printing processes need to be modeled appropriately. In this paper, the current state of AM in construction is reviewed. AM processes and systems as well as their application in research and construction projects are presented. Moreover, digital methods for planning 3D-printed building parts and AM processes are described.
The digital revolution is expected to play a decisive role in the transformation of the construction industry, opening new markets, creating new products and boosting its productivity and efficiency. The rapid expansion of advanced technologies, such as Internet of Things (IoT), Building Information Modeling (BIM) and other digital systems are about to update the construction sector. The construction industry can take advantage on new technologies, materials and processes to promote customization and safety, reducing construction waste, time and costs. Advanced technologies and innovative processes commonly used in the manufacturing area are being exported for construction and architectural applications. This paper focuses on the digital transformation of the construction sector, particularly the concept of additive manufacturing (AM) in construction. It describes the main AM technologies being developed for construction, as well its major challenges and opportunities. It concludes that the development of new construction materials for AM requires further research in terms of materials testing and characterization standards, as well the incorporation of building codes and standards.
Keywords: Additive manufacturing; Building Information Modeling; Construction industry; Digitization; Construction 4.0; 3D printing; Concrete printing; Digital construction
Visualizations—visual representations of information, depicted in graphics—are studied by researchers in numerous ways, ranging from the study of the basic principles of creating visualizations, to the cognitive processes underlying their use, as well as how visualizations communicate complex information (such as in medical risk or spatial patterns). However, findings from different domains are rarely shared across domains though there may be domain-general principles underlying visualizations and their use. The limited cross-domain communication may be due to a lack of a unifying cognitive framework. This review aims to address this gap by proposing an integrative model that is grounded in models of visualization comprehension and a dual-process account of decision making. We review empirical studies of decision making with static two-dimensional visualizations motivated by a wide range of research goals and find significant direct and indirect support for a dual-process account of decision making with visualizations. Consistent with a dual-process model, the first type of visualization decision mechanism produces fast, easy, and computationally light decisions with visualizations. The second facilitates slower, more contemplative, and effortful decisions with visualizations. We illustrate the utility of a dual-process account of decision making with visualizations using four cross-domain findings that may constitute universal visualization principles. Further, we offer guidance for future research, including novel areas of exploration and practical recommendations for visualization designers based on cognitive theory and empirical findings.
At present, the capability to collect information from different sources [building information modeling (BIM), sensors, assets database] for asset management (AM) use has generated significant opportunities for asset owners and facility managers. BIM, in particular, is considered as a potentially effective data pool for storing and managing project information during the building life cycle, providing a common data environment for stakeholder data and guaranteeing information availability and credibility at the handover stage. However, this data integration in a BIM environment has issues related to the establishment of an effective process to extract, store, manage, integrate, and distribute data to ensure interoperability. Furthermore, for the interoperability issue, an important challenge is the identification of which data are relevant, reliable, useful, and can add value to the AM processes. This research explored the asset owner requirements from BIM in the operation and maintenance stage from an AM perspective. The research aim was twofold: first, to synthesize the nongeometric BIM data required for AM and develop a relevant taxonomy; and second, to develop an application programming interface (API) plug-in for Autodesk Revit to implement the proposed taxonomy. To achieve the aims of this study, a critical review of previous literature and face-to-face and focus group interviews with BIM and facilities management (FM) experts were conducted. The main output was a 60-parameter asset-consuming energy information management (ACE-IM) taxonomy and the relevant API plug-in that can help BIM professionals to identify the required data to be submitted to facility managers to improve AM processes.
Investigating the drivers for building information modelling (BIM) adoption by organisations has attracted a significant interest in recent years. However, there are still some important limitations to overcome in this areas: (1) drivers and factors for BIM adoption are disjointedly identified and dispersed across many studies−this is caused by both the specialised theoretical lenses embraced by researchers and the use of non-systematic approaches for identifying pertinent studies; and (2) inadequate attention to the meaning of key terms and concepts (i.e. readiness, implementation, diffusion, adoption) underpinning this area of investigation and their overlap. This paper addresses these shortcomings and presents an exhaustive set of the drivers (i.e. innovation characteristics, internal environment characteristics, and external environment characteristics), key factors and their potential determinants that affect BIM adoption decisions. These findings can be used to: (a) investigate the power or influence exerted by the different drivers, factors and determinants on the organisational adoption of BIM within markets with different macro diffusion dynamics−e.g. with and without BIM mandate; (b) facilitate BIM diffusion planning by setting actions that target specific drivers and factors; and (c) develop a conceptual model for BIM adoption by organisations.
Driven by a plethora of external and internal influences, the construction industry has independently embraced lean principles and green initiatives. Prima facie significant synergies have been reported between these two paradigms. It is foreseen that when tapped and adopted in unison, these paradigms may yield additional benefits for the construction projects. This synergy is investigated in this research. Further this study identifies and proposes Building Information Modelling (BIM) as an enabler for gaining lean and green project outcomes. The study uses crisp set qualitative comparative analysis (csQCA) method for exploring the causal combinations of different BIM capabilities and asserts that causal combinations of four BIM capabilities: MEP system modelling, energy and environment analysis, constructability analysis and structural analysis, when implemented on construction projects can lead to lean and green outcomes. With the help of sixteen cases it is shown that adoption of BIM leads to improved project outcomes especially ones targeting lean and green aspects.
Traditionally, production control on construction sites has been challenging, and still remains challenging. The ad-hoc production control methods that are usually used, most of which are informal, foster uncertainty that prevents smooth production flow. Lean construction methods such as the Last Planner System have partially tackled this problem by involving site teams into the decision making process and having them report back to the production management system. However, such systems have relatively long "lookahead" planning cycles to respond to the dynamic production requirements of construction, where daily, if not hourly control is needed. New solutions have been proposed such as VisiLean, KanBIM, etc., but again these types of construction management systems require the proximity and availability of computer devices to workers. Through this paper, the authors investigate how the communication framework underlying such construction management systems can be further improved so as to fully or partially automate various communication functions across the construction project lifecycle (e.g., to enable lean and close to real-time reporting of production control information). To this end, the present paper provides evidences of how the Internet of Things (IoT) and related standards can contribute to such an improvement. The paper then provides first insights - through various construction scenarios - into how the proposed communication framework can be beneficial for various actors and core business perspectives, from lean construction management to the management of the entire building lifecycle.
Fragmentation of practices is one of the key issues in the built environment. However, with advances in Information and Communication Technology (ICT), particularly cloud computing, the fragmentation of working practices can be potentially overcome. The technology could enhance communication and information flow in various stages along a project life cycle. Due to the increasing demands and the newly developed cloud computing applications, it is critical to review and identify the appropriate cloud computing applications in the built environment. A total of forty two cloud computing applications consisting of general cloud applications, Building Information Modelling (BIM), and project management cloud applications were selected and critically reviewed. A decision-making model was also developed to assist parties in selecting a suitable application. The explanatory case study has discovered numerous possible cloud computing applications in various disciplines, for example, Google Apps, Autodesk BIM 360, and Viewpoint are the applications with the most features. The findings contribute to creating a certain awareness and an insight to reduce the fragmented working practices in the built environment.
The construction industry has been facing a paradigm shift to (i) increase; productivity, efficiency, infrastructure value, quality and sustainability, (ii) reduce; lifecycle costs, lead times and duplications, via effective collaboration and communication of stakeholders in construction projects. Digital construction is a political initiative to address low productivity in the sector. This seeks to integrate processes throughout the entire lifecycle by utilising building information modelling (BIM) systems. The focus is to create and reuse consistent digital information by the stakeholders throughout the lifecycle. However, implementation and use of BIM systems requires dramatic changes in the current business practices, bring new challenges for stakeholders e.g., the emerging knowledge and skill gap. This paper reviews and discusses the status of implementation of the BIM systems around the globe and their implications to the industry. Moreover, based on the lessons learnt, it will provide a guide to tackle these challenges and to facilitate successful transition towards utilizing BIM systems in construction projects.
Purpose
The purpose of this article was to identify and evaluate the benefits of adopting digital technologies (DTs) in the Nigerian construction industry, to ensure that construction projects are efficiently and effectively executed.
Design/methodology/approach
A scoping literature review was conducted through which benefits of implementing DTs were identified, which helped in the formulation of a questionnaire survey. Data were obtained from construction professionals including architects, builders, engineers and quantity surveyors. Retrieved data were analyzed using several statistical tools such as percentages, frequencies, mean item scores and exploratory factor analyses.
Findings
Based on the mean item score ranking results, the top five significant benefits of adopting DTs were increased operational efficiency, increased productivity, increased speed of executing tasks, seamless working methodology and accurate documentation of data. Findings from the factor analysis revealed six clusters of benefits namely an improved framework for operations, improved innovativeness, improved safety practices, increased competitive edge, greater accuracy and increased productivity.
Practical implications
This study expands on the existing discussions surrounding the digital transformation of the construction sector in developing countries, particularly in Nigeria, a region where the awareness and implementation of smart and innovative technologies are still at an infant stage.
Originality/value
Although several studies on the benefits of adopting DTs in the construction industry of developing countries exist, very few have been conducted in the Nigerian context. Thus, this is a significant knowledge gap that must be filled. Thus, the findings of this study can serve as a basis for construction organizations in Nigeria and similar countries seeking the digital transformation of their activities.
Localization is critical to but also challenging for an efficient first response during indoor emergency situations. This paper describes an economical indoor localization system with a low setup cost based on the visual Simultaneous Localization and Mapping (vSLAM). In addition, threshold for key features per image frame is studied for acceptable localization accuracy. The amount of image input users should collect is also analyzed for reliable localization. To address matching and accuracy issues under varying lighting conditions, a comprehensive approach for accurate localization utilizing vSLAM is also proposed. An actual building was tested for the proposed approach with an average localization success rate over 94%.
With advances being made in digital technologies, asset owners are requiring at hand-over a digital twin of their constructed facility that can be used in real-time to support operations and maintenance processes. While there has been considerable focus on ‘why’ organizations operating in the construction industry need to adopt digital technologies to enable Building Information Modelling, Internet of Things and Industry 4.0 and thus deliver assets more effectively and efficiently, there has been limited attention given to ‘how’ they can realize their expected benefits and simultaneously generate value. In light of the drive for organizations to engage with digital technology we bring to the fore in this paper the need for them to legitimize a process of benefits management prior to making a financial investment to understand ‘how’ digital technologies can coalesce to generate business value and improve their competitiveness. As part of a benefits management strategy, a business dependency network (BDN) can enable investment objectives and their resulting benefits to be linked in a structured manner with an organization’s capabilities and changes required to ensure they are realized. Relying on empirical findings derived from nine projects that examined the efficiency gains and advantages of using a digital systems information model from an engineering to asset management perspective, we construct a generic BDN to visualise the structure of multiple cause-effect relationships that are used to organize the capabilities, changes and benefits that need to be considered prior to adoption. The insights and experiences that have emerged from this research provide a frame of reference for construction organizations and asset owners to navigate the benefits realization and change management process, which can be used to ensure their investments in digital technology can effectively respond to business drivers and generate value.
Data visualization is the process of interpreting data and presenting it in a pictorial or graphical format. Currently, we are living in the era of big data, where data has been described as a raw material for business. The volume of data used in businesses, industries, research organizations, and technological development is massive, and it is rapidly growing every day. The more data we collect and analyze, the more capable we can be in making critical business decisions. However, with the enormous growth of data, it has become harder for businesses to extract crucial information from the available data. That is where the importance of data visualization becomes clear. Data visualization helps people understand the significance of data by summarizing and presenting a huge amount of data in a simple and easy-to-understand format in order to communicate the information clearly and effectively.
Advances in robotics now permit humans to work collaboratively with robots. However, humans often feel unsafe working alongside robots. Our knowledge of how to help humans overcome this issue is limited by two challenges. One, it is difficult, expensive and time consuming to prototype robots and set up various work situations needed to conduct studies in this area. Two, we lack strong theoretical models to predict and explain perceived safety and its influence on human-robot work collaboration (HRWC). To address these issues, we introduce the Robot Acceptance Safety Model (RASM) and employ immersive virtual environments (IVEs) to examine perceived safety of working on tasks alongside a robot. Results from a between-subjects experiment done in an IVE show that separation of work areas between robots and humans increases perceived safety by promoting team identification and trust in the robot. In addition, the more participants felt it was safe to work with the robot, the more willing they were to work alongside the robot in the future.
Improvisation is a spontaneous and impulsive, but rational, decision-making process. Although unpopular, given that it indicates
deviation from plans, improvisation is unavoidable in construction planning because it helps address emerging nonstandard issues or unexpected
problems. While the results of improvisation are understandable, the decision process is spontaneous. Therefore, a construction project
will benefit from applying an adaptive planning system that uses improvisation. After conducting a review on practices of improvisation, a
decision-making model illustrating how construction employees can properly improvise and reach their desired decisions or actions was proposed.
Aiming to investigate the practice of improvisation in construction, this study considered 28 large-sized projects, in which 120 surveys
were conducted via semistructured interviews with blue- and white-collar employees. Thereafter, statistical analyses and hypotheses testing
were performed to highlight important insights. The study would greatly benefit construction companies and professionals because it offers
recommendations that influence improvisational outcomes and enhance the corresponding decision-making process
Crack detection is a crucial task in periodic pavement survey. This study establishes and compares the performance of two intelligent approaches for automatic recognition of pavement cracks. The first model relies on edge detection approaches of the Sobel and Canny algorithms. Since the implementation of the two edge detectors require the setting of threshold values, Differential Flower Pollination, as a metaheuristic, is employed to fine-tune the model parameters. The second model is constructed by the implementation of the Convolution Neural Network (CNN) – a deep learning algorithm. CNN has the advantage of performing the feature extraction and the prediction of crack/non-crack condition in an integrated and fully automated manner. Experimental results show that the model based on CNN achieves a good prediction performance of Classification Accuracy Rate (CAR) = 92.08%. This performance is significantly better than the method based on the edge detection algorithms (CAR = 79.99%). Accordingly, the proposed CNN based crack detection model is a promising alternative to support transportation agencies in the task of periodic pavement inspection.
Building performance simulations and models of human visual comfort allow us to predict daylight-caused glare using digital building models and climate data. Unfortunately, the simulation tools currently available cannot produce results fast enough for interactive use during design ideation. We developed software with the ability to predict visual discomfort in real time. However, we know little about how users react to simulation feedback presented in real time. In our study, 40 subjects with backgrounds in building design and technology completed two shading design exercises to balance glare reduction and annual daylight availability in two open office arrangements using two simulation tools with differing system response times. Subjects with access to real-time simulation feedback tested more design options, reported higher confidence in design performance and increased satisfaction with the design task, and produced better-performing final designs with respect to spatial daylight autonomy and enhanced simplified daylight glare probability.
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.
Digital fabrication represents innovative, computer-controlled processes and technologies with the potential to expand the boundaries of conventional construction. Their use in construction is currently restricted to complex and iconic structures, but the growth potential is large. This paper aims to investigate the environmental opportunities of digital fabrication methods, particularly when applied to complex concrete geometries. A case study of a novel robotic additive process that is applied to a wall structure is evaluated with the Life Cycle Assessment (LCA) method. The results of the assessment demonstrate that digital fabrication provides environmental benefits when applied to complex structures. The results also confirm that additional complexity is achieved through digital fabrication without additional environmental costs. This study provides a quantitative argument to position digital fabrication at the beginning of a new era, which is often called the Digital Age in many other disciplines.
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.
The ability to process large amounts of data and to extract useful insights from data has revolutionised society. This phenomenon—dubbed as Big Data—has applications for a wide assortment of industries, including the construction industry. The construction industry already deals with large volumes of heterogeneous data; which is expected to increase exponentially as technologies such as sensor networks and the Internet of Things are commoditised. In this paper, we present a detailed survey of the literature, investigating the application of Big Data techniques in the construction industry. We reviewed related works published in the databases of American Association of Civil Engineers (ASCE), Institute of Electrical and Electronics Engineers (IEEE), Association of Computing Machinery (ACM), and Elsevier Science Direct Digital Library. While the application of data analytics in the construction industry is not new, the adoption of Big Data technologies in this industry remains at a nascent stage and lags the broad uptake of these technologies in other fields. To the best of our knowledge, there is currently no comprehensive survey of Big Data techniques in the context of the construction industry. This paper fills the void and presents a wide-ranging interdisciplinary review of literature of fields such as statistics, data mining and warehousing, machine learning, and Big Data Analytics in the context of the construction industry. We discuss the current state of adoption of Big Data in the construction industry and discuss the future potential of such technologies across the multiple domain-specific sub-areas of the construction industry. We also propose open issues and directions for future work along with potential pitfalls associated with Big Data adoption in the industry.
With the rapid development of urbanization in China, the number and size of underground space development projects are increasing quickly. At the same time, more and more accidents are causing underground construction to increasingly become a focus of social attention. Therefore, this research presents a real-time safety early warning system to prevent accidents and improve safety management in underground construction, based on the “internet of things” (IoT) technology. The proposed system seamlessly integrates a fiber Bragg grating (FBG) sensor system and a RFID (radio frequency identification)-based labor tracking system. This system has been validated and verified through a real-world application at the cross passage construction site in the Yangtze Riverbed Metro Tunnel project in Wuhan, China. The system's application results show that it can effectively promote underground construction safety management efficiency, specifically in the real time detection, monitoring and early warning of safety risks.
Smooth flow of production in construction is hampered by disparity between individual trade teams' goals and the goals of stable production flow for the project as a whole. This is exacerbated by the difficulty of visualizing the flow of work in a construction project. While the addresses some of the issues in Building information modeling provides a powerful platform for visualizing work flow in control systems that also enable pull flow and deeper collaboration between teams on and off site. The requirements for implementation of a BIM-enabled pull flow construction management software system based on the Last Planner System™, called ‘KanBIM’, have been specified, and a set of functional mock-ups of the proposed system has been implemented and evaluated in a series of three focus group workshops. The requirements cover the areas of maintenance of work flow stability, enabling negotiation and commitment between teams, lean production planning with sophisticated pull flow control, and effective communication and visualization of flow. The evaluation results show that the system holds the potential to improve work flow and reduce waste by providing both process and product visualization at the work face.
What is digital construction management? RTF | Rethinking The Future
- G Patel
4D BIM Services - 4D BIM construction scheduling and simulation. TopBIM Company
- Topbimadmin
Enabling BIM-driven robotic construction workflows with closed-loop digital twins
- X Wang
- H Yu
- W Mcgee
- C C Menassa
- V R Kamat