Wearable sensing devices (WSDs) are increasingly helping workers stay safe and healthy in several industries. However, workers, especially in the construction industry, have shown some aversion towards the use of WSDs due to their ability to capture specific information that may be considered personal and private. However, this revered information may provide some critical insight needed by management to plan and optimize worksite safety and support technology adoption in decision making. Therefore, there is a need to develop personalized WSD systems that are mutually beneficial to workers and management to ensure successful WSD integration. The present study aims to contribute to knowledge and practice by filling this critical gap using insight from 330 construction workers with experience using WSDs. The results from this study indicate that all 11 WSD functions identified through this study play a vital role in improving worker safety and health and that approximately two out of three workers are open to sharing the physiological and environmental information captured using these WSDs with their management. However, functions for detecting workers’ proximity to workplace hazards, specifically energized electrical materials, toxic gas, and fire/smoke, were the most critical functions that had mutual value to workers and management. Finally, the present study proposed and evaluated a phased personalized WSD system that should encourage successful WSD integration.

Purpose: The 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/approach: The 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.Findings: Results 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/implications: Suggested 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/value: This 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.

Technology use in the construction industry fosters improvements in schedule, safety, cost, productivity, and quality. In this domain, the construction technologies adoption highly depends on stakeholders, who may exhibit some resistance to operational use. This underscores the importance of determining technology integration success using effective methods such as predictive and explanatory modelling. Although existing literature has provided some critical insight into the use of these models and theories, there is no domain-based synthesis on the utility of these models and theories as tools to facilitate the integration of emerging construction technologies. Therefore, this paper provides a systematic review and content analysis showcasing different methods and theories for investigating technology acceptance and generates insights expected to guide future technology acceptance studies. Using a three-phase systematic review process, 35 relevant articles were identified and analysed. This review identified perceived ease of use, perceived usefulness, social norm, attitude, perceived behavioural control, and facilitating conditions as key constructs impacting workers’ intention to accept a construction technology. TAM, TPB, and UTAUT were identified as popular choices for developing hybrid models, while UTAUT provided a relatively higher predictive power. Finally, seven areas for further exploration were discussed. This study contributes to construction knowledge by providing a better understanding of technology acceptance research and generating fundamental insights needed to develop robust and effective predictive and explanatory models for advancing technology acceptance research which would support successful technology integration.

The use of robotics and automation (RA) within construction operations to combat stagnant productivity, high rates of injuries and fatalities, and a declining workforce is increasing. However, utilizing RA for construction operations creates human-robot interactions (HRIs) that can lead to the emergence of novel hazards or exacerbate existing safety risk levels. These hazards, if ignored, could worsen worker safety performance. This paper presents a study intended to identify critical HRI safety risks and develop a pre-task planning protocol for the assessment of safety risks associated with the use of RA. To achieve this goal, the authors utilized a sequential multi-phase approach relying on information obtained from a literature review, a 3-round Delphi process consisting of a panel of experts from industry and academia, and interviews with safety experts. Forty hazards associated with RA and 20 potential strategies for the mitigation of HRI safety risks were identified and evaluated. This information was used to develop a safety risk assessment tool that yields an overall risk rating of using RA. This study contributes to knowledge and practice by identifying and quantifying critical safety risks associated with HRI and developing a tool to assess and mitigate unique HRI safety risks. Utilizing insights developed in this study could improve construction worker safety and health and aid the industry’s increasing push towards a smart and safe workforce.

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.

The need to enhance worker safety in construction has often studied recently due to the high rates of fatalities and injuries in the construction industry. To abate this scourge, various control methods including the use of technology have been adopted to improve workplace safety performance in construction. However, not all of the technologies adopted by industry personnel for safety management have achieved the desired outcome or have proved to be effective. More research is needed regarding the adoption of safety technologies in the construction industry. Importantly, industry stakeholders need to evaluate their readiness to adopt safety technologies. The present study develops a decision-making tool that can aid in the adoption of safety technology in the construction industry. The safety technology decision-making adoption tool is developed using the fuzzy synthetic evaluation technique and consists of three categories of safety technology predictors: external, organization, and technology-related. Technology-based predictors have emerged as the most impactful predictor. Subsequently, a technology adoption assessment protocol was developed and applied to a case study to assess an organization’s readiness to adopt wearable sensing devices. The present work contributes to the body of knowledge by identifying, quantifying, and categorizing predictors of technology adoption and then integrating the predictors into an evaluation protocol to assess the adoption of safety technologies in the construction industry. This contribution is translated into practice by developing a user-friendly tool called the Construction Safety Technology Adoption Index (C-STAI). The developed tool is expected to help construction professionals and practitioners make informed decisions regarding the adoption of safety technology. Improved technology adoption in construction is expected to lead to enhanced safety and reduce injuries on construction projects.