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1. Impacts of Physical and Informational Failures on Worker–Autonomy Trust in Future Construction

   https://doi.org/10.1061/JCEMD4.COENG-15241  

   Chang, Woei-Chyi; Esmaeili, Behzad; Hasanzadeh, Sogand (April 2025, Journal of Construction Engineering and Management)

   Autonomous agents are increasingly becoming construction workers’ teammates, making them an integral part of tomorrow’s construction industry. Although many expect that worker–autonomy teaming will enhance construction efficiency, the presence of auto-agents, or robots necessitates an appropriate level of trust-building between workers and their autonomous counterparts, especially because these auto-agents’ perfection still cannot be guaranteed. Although researchers have widely explored human–autonomy trust in various domains—such as manufacturing and the military—discussion of this teaming dynamic within the construction sector is still nascent. To address this gap, this paper simulated a futuristic bricklaying task to (1) examine whether identifying autonomous agents’ physical and informational failures and risk perception affect workers’ trust levels, and (2) investigate workers’ neuropsychophysiological responses as a measure of trust levels toward robots, especially when autonomous agents are faulty. Results indicate that (1) identification of both types of failures and high-risk perception significantly reduce workers’ trust in autonomous agents, and the nuances of workers’ responses to both types of failures were discerned; and (2) brain activation correlates with trust changes. The findings suggest that workers’ unfamiliarity with autonomous technologies, coupled with fast-growing interest in adopting them, may leave workers at risk of improper trust transfer or overtrust in the autonomous agents. This study contributes to an expanding exploration of worker–autonomy trust in construction and calls for further investigations into effective approaches for auto-agents to communicate their physical and informational failures and to help workers recover and repair trust. 

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2. Industry perception of competencies for human—robot collaboration in the construction industry: A Delphi study

   https://doi.org/10.1007/s42524-025-4224-x  

   Olukanni, Ebenezer; Akanmu, Abiola; Jebelli, Houtan (June 2025, Frontiers of Engineering Management)

   Robots present an innovative solution to the construction industry’s challenges, including safety concerns, skilled worker shortages, and productivity issues. Successfully collaborating with robots requires new competencies to ensure safety, smooth interaction, and accelerated adoption of robotic technologies. However, limited research exists on the specific competencies needed for human—robot collaboration in construction. Moreover, the perspectives of construction industry professionals on these competencies remain underexplored. This study examines the perceptions of construction industry professionals regarding the knowledge, skills, and abilities necessary for the effective implementation of human—robot collaboration in construction. A two-round Delphi survey was conducted with expert panel members from the construction industry to assess their views on the competencies for human—robot collaboration. The results reveal that the most critical competencies include knowledge areas such as human—robot interface, construction robot applications, human—robot collaboration safety and standards, task planning and robot control system; skills such as task planning, safety management, technical expertise, human—robot interface, and communication; and abilities such as safety awareness, continuous learning, problemsolving, critical thinking, and spatial awareness. This study contributes to knowledge by identifying the most significant competencies for human—robot collaboration in construction and highlighting their relative importance. These competencies could inform the design of educational and training programs and facilitate the integration of robotic technologies in construction. The findings also provide a foundation for future research to further explore and enhance these competencies, ultimately supporting safer, more efficient, and more productive construction practices. 

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3. Attributing responsibility for performance failure on worker-robot trust in construction collaborative tasks

   https://doi.org/10.35490/EC3.2023.205  

   Chang, Woei-Chyi; Ryan, Sophia Marie; Hasanzadeh, Sogand; Esmaeili, Behzad (July 2023, European Council on Computing in Construction)

   Recent advances in construction automation increased the need for cooperation between workers and robots, where workers have to face both success and failure in human-robot collaborative work, ultimately affecting their trust in robots. This study simulated a worker-robot bricklaying collaborative task to examine the impacts of blame targets (responsibility attributions) on trust and trust transfer in multi-robots-human interaction. The findings showed that workers’ responsibility attributions to themselves or robots significantly affect their trust in the robot. Further, in a multi-robots-human interaction, observing one robot’s failure to complete the task will affect the trust in the other devices, aka., trust transfer. 

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4. Deep Learning–Based Prediction of Human–Robot Trust Dynamics in Future Construction Using Worker Neuropsychophysiological Responses

   https://doi.org/10.1061/JCCEE5.CPENG-6302  

   Chang, Woei-Chyi; Gonzalez_Garcia, Nestor F; Hasanzadeh, Sogand (July 2025, Journal of Computing in Civil Engineering)

   Because current construction activities are safety-critical and physically demanding, the incorporation of such autonomous technologies as robots and drones via worker–robot teaming has drawn interest from researchers and practitioners alike. However, this teaming relationship may impose additional safety concerns for future jobsites due to workers’ inappropriate trust—overtrust and/or distrust—in robots. The literature has highlighted that trust is a complicated and dynamic concept that fluctuates over time, highlighting the need to continuously understand workers’ trust levels in real-time by collecting and interpreting workers’ psychophysiological signals. Consequently, deep learning (DL) has been deployed in various projects to identify trust-related psychophysiological patterns and to predict trust. However, current implementations suffer from three limitations: (1) focusing only on static settings, (2) manually extracting features, and (3) disregarding the trust continuum. Therefore, this study presents a DL model that automatically extracts important features from multiple psychophysiological signals and predicts workers’ increasing or decreasing trust within such dynamic workplaces as construction sites. The developed model can achieve accuracy, recall, precision, and 𝐹⁢1 score all above 70%. This study also provides insights into a cost-effective strategy to prioritize data with high importance to trust prediction. Thus, the primary innovations of this research are (1) the consideration of the dynamic nature of construction sites, variability among workers, and trust continuum during model development; and (2) how pivotal knowledge about workers’ real-time trust can be harnessed to facilitate the development of human-centered robots in the future. 

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5. The Importance of Situational Awareness in Future Construction Work: Toward the Effects of Faulty Robot, Trust, and Time Pressure

   Chang, W.C. (July 2023, Reston, VA: American Society of Civil Engineers)

   Introducing robots to future construction sites will impose extra uncertainties and necessitate workers’ situational awareness (SA) of them. While previous literature has suggested that system errors, trust changes, and time pressure may affect SA, the linkage between these factors and workers’ SA in the future construction industry is understudied. Therefore, this study aimed to fill the research gap by simulating a future bricklaying worker-robot collaborative task where participants experienced robot errors and time pressure during the interaction. The results indicated that robot errors significantly impacted subjects’ trust in robots. However, under time pressure in time-critical construction tasks, workers tended to recover their reduced trust in the faulty robots (sometimes over-trust) and reduce their situational awareness. The contributions of this study lie in providing insights into the importance of SA in future jobsites and the need for investigating effective strategies for better preparing future workers. 

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