The construction industry continues to seek innovative ways to safely, timely and cost-effectively deliver construction projects. Several efforts have been made to automate construction processes but marginial success has been achieved in effectively reducing the long standing risks suffered by the industry. While industry 4.0 promises to improve project efficiency, reduce waste and improve productivity, the transition to this will depend on the successful adoption of many emerging technologies such as virtual design modeling technologies, sensing technologies, data analysis, storage and communication technologies, human-computer interaction technologies, and robotics. To accelerate innovation, digital twins and cyber-physical systems will be a necessity to advance automation and real-time control with these technologies. While digital twin represents a digital replica of the asplanned and as-built facility, cyber physical systems involve integration of physical systems with their digital replica through sensors and actuators. Despite evidence of the efficacy of cyber-physical systems and digital twins for reducing non-fatal injuries, enhancing safety management, improving progress monitoring and enhancing performance monitoring and control of facilities, their adoption in the construction industry is still in its infancy. This paper sheds light on the opportunities offered by cyber-physical systems and digital twins in other industry sectors and advocates for their increased deployment in the construction industry. This paper describes cyber-physical integration of emerging technologies with the physical construction or constructed facility as the next generation digital twin and cyber-physical systems. Potential scenarios of next generation cyber physical system and digital twin for improving workforce productivity, health, and safety, lifecycle management of building systems, and workforce competency are presented.

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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.

PurposeThe physically-demanding and repetitive nature of construction work often exposes workers to work-related musculoskeletal injuries. Real-time information about the ergonomic consequences of workers' postures can enhance their ability to control or self-manage their exposures. This study proposes a digital twin framework to improve self-management ergonomic exposures through bi-directional mapping between workers' postures and their corresponding virtual replica.Design/methodology/approachThe viability of the proposed approach was demonstrated by implementing the digital twin framework on a simulated floor-framing task. The proposed framework uses wearable sensors to track the kinematics of workers' body segments and communicates the ergonomic risks via an augmented virtual replica within the worker's field of view. Sequence-to-sequence long short-term memory (LSTM) network is employed to adapt the virtual feedback to workers' performance.FindingsResults show promise for reducing ergonomic risks of the construction workforce through improved awareness. The experimental study demonstrates feasibility of the proposed approach for reducing overexertion of the trunk. Performance of the LSTM network improved when trained with augmented data but at a high computational cost.Research limitations/implicationsSuggested actionable feedback is currently based on actual work postures. The study is experimental and will need to be scaled up prior to field deployment.Originality/valueThis study reveals the potentials of digital twins for personalized posture training and sets precedence for further investigations into opportunities offered by digital twins for improving health and wellbeing of the construction workforce.

PurposeConstruction action recognition is essential to efficiently manage productivity, health and safety risks. These can be achieved by tracking and monitoring construction work. This study aims to examine the performance of a variant of deep convolutional neural networks (CNNs) for recognizing actions of construction workers from images of signals of time-series data.Design/methodology/approachThis paper adopts Inception v1 to classify actions involved in carpentry and painting activities from images of motion data. Augmented time-series data from wearable sensors attached to worker's lower arms are converted to signal images to train an Inception v1 network. Performance of Inception v1 is compared with the highest performing supervised learning classifier, k-nearest neighbor (KNN).FindingsResults show that the performance of Inception v1 network improved when trained with signal images of the augmented data but at a high computational cost. Inception v1 network and KNN achieved an accuracy of 95.2% and 99.8%, respectively when trained with 50-fold augmented carpentry dataset. The accuracy of Inception v1 and KNN with 10-fold painting augmented dataset is 95.3% and 97.1%, respectively.Research limitations/implicationsOnly acceleration data of the lower arm of the two trades were used for action recognition. Each signal image comprises 20 datasets.Originality/valueLittle has been reported on recognizing construction workers' actions from signal images. This study adds value to the existing literature, in particular by providing insights into the extent to which a deep CNN can classify subtasks from patterns in signal images compared to a traditional best performing shallow network.