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Industry4.0 Applications for Full Lifecycle Integration of Buildings Proceedings of the 21st International Conference on Construction Applications of Virtual Reality
Abstract
Welcome to the 21st International Conference on Construction Applications of Virtual Reality (CONVR 2021). This year we are meeting on-line due to the current Coronavirus pandemic. The overarching theme for CONVR2021 is " Industry4.0 Applications for Full Lifecycle Integration of Buildings".
CONVR is one of the world-leading conferences in the areas of virtual reality, augmented reality and building information modelling. Each year, more than 100 participants from all around the globe meet to discuss and exchange the latest developments and applications of virtual technologies in the architectural, engineering, construction and operation industry (AECO). The conference is also known for having a unique blend of participants from both academia and industry.
This year, with all the difficulties of replicating a real face to face meetings, we are carefully planning the conference to ensure that all participants have a perfect experience. We have a group of leading keynote speakers from industry and academia who are covering up to date hot topics and are enthusiastic and keen to share their knowledge with you. CONVR participants are very loyal to the conference and have attended most of the editions over the last eighteen editions. This year we are welcoming numerous first timers and we aim to help them make the most of the conference by introducing them to other participants.
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Low back disorder continues to be prevalent amongst construction workers, especially the rebar workers who are often engaged in repetitive stooping postures. Wearable robots, exoskeletons, are recent ergonomic interventions currently explored in the construction industry that have potentials of reducing the risks of low back pain by augmenting users’ body parts and reducing demands on the back. This paper presents the assessment of a commercially available passive wearable robot, BackX, designed for reducing low back disorder amongst rebar workers. The study evaluated the exoskeleton in terms of task performance and physiological conditions. Outcome measures such as completion time were employed to evaluate the effect of the exoskeleton on task performance, while activations of Erector Spinae and Latissimus Dorsi muscles, and perceived discomfort across body parts were employed to assess the physiological effects of the exoskeleton. The results indicated mixed effects of the exoskeleton on muscle activations. Although the results revealed that the exoskeleton can reduce muscle activations across the Latissimus Dorsi, mixed effects were observed for the Erector Spinae especially during the forward bending tasks. The exoskeleton reduced completion time by 50% during the rebar tasks. There was also a 100% reduction in perceived discomfort on the back, but discomfort was tripled at the chest region when the exoskeleton was worn. This study reveals the potentials of the exoskeleton for reducing low back disorder and improving productivity amongst the rebar workers. However, the unintended consequences such as increased discomfort at the chest region and activations of the muscles highlight the need for improving existing exoskeleton designs for construction work.
With the growing rate of adoption of sensing technologies in the construction industry, there is an increased need for technically skilled workforce to successfully deploy these technologies on construction projects. Inspired by opportunities offered by mixed reality, this paper presents the development and evaluation of a holographic learning environment that can afford learners an experiential opportunity to acquire competencies for implementing sensing systems on construction projects. To develop the content of the learning environment, construction industry practitioners and instructors were surveyed, and construction industry case studies on the applications of sensing technologies were explored. Findings of the surveys revealed sensing technologies domain-specific skill gap in the construction industry. Further, the findings informed the requirements of the learning environment. Based on these requirements, key characteristics of the learning environment are identified and employed in designing the environment. Still, a formative evaluation is important for developing an effective mixed reality learning environment for teaching domain-specific competencies. Thus, it is imperative to understand the quality, appropriateness, and representativeness of the content of the learning environment. This paper also presents a learnability assessment of the developed mixed reality learning environment. The assessment was conducted utilizing a focus group discussion with construction industry practitioners. Feedback was sought from the participants regarding the reflectiveness of the layout of the virtual environment of an actual construction site and the appropriateness of the represented construction applications. This study contributes to the definition of the type of domain-specific skills required of the future workforce for implementing sensing technologies in the construction industry and how such skills can be developed and enhanced within a mixed reality learning environment.
Manual material handling tasks have the potential to be highly unsafe from an ergonomic viewpoint. Safety inspections to monitor body postures can help mitigate ergonomic risks of material handling. However, the real effect of awkward muscle movements, strains, and excessive forces that may result in an injury may not be identified by external cues. This paper evaluates the ability of surface electromyogram (EMG)-based systems together with machine learning algorithms to automatically detect body movements that may harm muscles in material handling. The analysis utilized a lifting equation developed by the U.S. National Institute for Occupational Safety and Health (NIOSH). This equation determines a Recommended Weight Limit, which suggests the maximum acceptable weight that a healthy worker can lift and carry, as well as a Lifting Index value to assess the risk extent. Four different machine learning models, namely Decision Tree, Support Vector Machine, K-Nearest Neighbor, and Random Forest are developed to classify the risk assessments calculated based on the NIOSH lifting equation. The sensitivity of the models to various parameters is also evaluated to find the best performance using each algorithm. Results indicate that Decision Tree models have the potential to predict the risk level with close to 99.35% accuracy.
Purpose
The massive number of pavements and buildings coupled with the limited inspection resources, both monetary and human, to detect distresses and recommend maintenance actions lead to rapid deterioration, decreased service life, lower level of service and increased community disruption. Therefore, this paper aims at providing a state-of-the-art review of the literature with respect to deep learning techniques for detecting distress in both pavements and buildings; research advancements per asset/structure type; and future recommendations in deep learning applications for distress detection.
Design/methodology/approach
A critical analysis was conducted on 181 papers of deep learning-based cracks detection. A structured analysis was adopted so that major articles were analyzed according to their focus of study, used methods, findings and limitations.
Findings
The utilization of deep learning to detect pavement cracks is advanced compared to assess and evaluate the structural health of buildings. There is a need for studies that compare different convolutional neural network models to foster the development of an integrated solution that considers the data collection method. Further research is required to examine the setup, implementation and running costs, frequency of capturing data and deep learning tool. In conclusion, the future of applying deep learning algorithms in lieu of manual inspection for detecting distresses has shown promising results.
Practical implications
The availability of previous research and the required improvements in the proposed computational tools and models (e.g. artificial intelligence, deep learning, etc.) are triggering researchers and practitioners to enhance the distresses’ inspection process and make better use of their limited resources.
Originality/value
A critical and structured analysis of deep learning-based crack detection for pavement and buildings is conducted for the first time to enable novice researchers to highlight the knowledge gap in each article, as well as building a knowledge base from the findings of other research to support developing future workable solutions.
Background: This paper focuses on the characteristics of lower limb EMG signals for common movements. Methods: We obtained length data for lower limb muscles during gait motion using software named OpenSim; statistical product and service solutions (SPSS) were utilized to study the correlation between each muscle, based on gait data. Low-correlation muscles in different regions were selected; inertial measurement unit (IMU) and EMG sensors were used to measure the lower limb angles and EMG signals when on seven kinds of slope, in five kinds of gait (walking on flat ground, uphill, downhill, up-step and down-step) and four kinds of movement (squat, lunge, raised leg and standing up). Results: After data denoising and feature extraction, we designed a double hidden-layer BP neural network to recognize the above motions according to EMG signals. Results show that EMG signals of selected muscles have a certain periodicity in the process of movement that can be used to identify lower limb movements. Conclusions: It can be seen, after the recognition of different proportions of training and testing sets that the average recognition rate of the BP neural network is 86.49% for seven gradients, 93.76% for five kinds of gait and 86.07% for four kinds of movements.
Since the arrival of the internet and affordable access to technologies, digital technologies have occupied a growing place in industries, propelling us towards a 4th industrial revolution: Industry 4.0. In today’s era of digital upheaval, enterprises are increasingly undergoing transformations that are leading to their digitalization. The traditional manufacturing industry is in the throes of a digital transformation that is accelerated by exponentially growing technologies (e.g., intelligent robots, Internet of Things, sensors, 3D printing). Around the world, enterprises are in a frantic race to implement solutions based on IoT to improve their productivity, innovation, and reduce costs and improve their markets on the international scene. Considering the immense transformative potential that IoTs and big data have to bring to the industrial sector, the adoption of IoT in all industrial systems is a challenge to remain competitive and thus transform the industry into a smart factory. This paper presents the description of the innovation and digitalization process, following the Industry 4.0 paradigm to implement a successful enterprise IoT strategy.
Although smart technologies associated with the Fourth Industrial Revolution have the potential to improve the performance of industries by optimizing work processes and enhancing the work environment, the adoption of these technologies in the construction industry is still in its nascent stage. Hence, this study aims to (1) investigate the challenges in adopting smart technologies, (2) propose effective strategies to promote adoption of smart technologies, and (3) investigate whether there are significant differences in the perceptions of the challenges and strategies by organizations' sizes and years of experience in the construction industry. To achieve the objectives, a comprehensive literature review and pilot interviews with industry experts were first conducted, followed by a survey and postsurvey interviews. The findings revealed that the top challenges faced are data and information sharing, regulatory compliance, and data ownership, while the most effective strategies are training of skilled construction workforce, provision of government incentives, and communication and change management. Furthermore, the criticality of regulatory challenges was perceived differently between organizations of different sizes. The findings provide a better understanding of the challenges faced and the strategies that have been perceived as effective by industry practitioners, which are essential starting points to develop a data-driven roadmap to drive the adoption of smart technologies to facilitate the digital transformation of the construction industry.
A review on the reference values of the acoustical parameters that have the greatest influence on students' performance at different ages has been completed in this study. Published studies from 2002 to 2020 were summarized, which focus on testing learning attainments and cognitive skills, speech intelligibility, and subjective perceptions under different classroom acoustic conditions. Only 38 papers out of the 56 containing empirical findings on the influence of acoustical parameters on students’ performance were considered, as the remaining 18 lacked ecological validity or did not respond to the selection criteria. Studies were only included if they considered normal hearing subjects and typical lesson settings provided in classrooms without an amplification system. Thus, the studies selected involved subjects between 5 and 40 years old. The values of the acoustical parameters that led to better or worse learning performance were tabulated and the distribution of occurrences was created. The median and interquartile range for the distribution of occurrences were used to describe the central tendency and dispersion and analyzed considering two different age groups. The results obtained were used to develop classroom acoustic guidelines for better learning performance for students under or over 12 years of age, and identified the thresholds that lead to worse performances.
Buildings are supposed to create a healthy and comfortable environment for people. Considering subjective response, the problem of multi-objective optimization of building environment control becomes more complicated. The weight method can achieve the unification of optimization goals, but the core challenge is to obtain accurate weights based on a small amount of measured data. Information theory provides a way to quantify the importance of parameters. This paper established a method to calculate the weights of subjective and objective parameters quickly based on the information gain ratio, and took the difference in occupants' feedback under satisfied and unsatisfied conditions into account. Then this method was applied to a case study, two office buildings located in Shanghai, China, in which 120 samples (satisfied 104; unsatisfied 16) in Case 1 and 715 samples (satisfied 537; unsatisfied 178) in Case 2 were collected. The weight calculation time was less than 0.1s, and thus the weight of all parameters can be updated quickly when data is accumulated. Among all the parameters, the thermal satisfaction (with a weight of 0.145 in Case 1 and 0.243 in Case 2) and light satisfaction (with a weight of 0.146 in Case 1 and 0.171 in Case 2) were found to have the greatest impact on overall satisfaction. The weight calculation results under satisfied (0.57 and 0.90 of subjective parameters; 0.43 and 0.10 of objective parameters) and unsatisfied conditions (0.55 and 0.70 of subjective parameters; 0.45 and 0.30 of objective parameters) were found to be different, thus, comprehensive consideration of the difference is of great significance to engineering applications.
