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1. Assessing Trust in Construction AI-Powered Collaborative Robots Using Structural Equation Modeling

   https://doi.org/10.1061/JCCEE5.CPENG-5660  

   Emaminejad, Newsha; Kath, Lisa; Akhavian, Reza (May 2024, Journal of Computing in Civil Engineering)

   This study aimed to investigate the key technical and psychological factors that impact the architecture, engineering, and construction (AEC) professionals’ trust in collaborative robots (cobots) powered by artificial intelligence (AI). This study seeks to address the critical knowledge gaps surrounding the establishment and reinforcement of trust among AEC professionals in their collaboration with AI-powered cobots. In the context of the construction industry, where the complexities of tasks often necessitate human–robot teamwork, understanding the technical and psychological factors influencing trust is paramount. Such trust dynamics play a pivotal role in determining the effectiveness of human–robot collaboration on construction sites. This research employed a nationwide survey of 600 AEC industry practitioners to shed light on these influential factors, providing valuable insights to calibrate trust levels and facilitate the seamless integration of AI-powered cobots into the AEC industry. Additionally, it aimed to gather insights into opportunities for promoting the adoption, cultivation, and training of a skilled workforce to effectively leverage this technology. A structural equation modeling (SEM) analysis revealed that safety and reliability are significant factors for the adoption of AI-powered cobots in construction. Fear of being replaced resulting from the use of cobots can have a substantial effect on the mental health of the affected workers. A lower error rate in jobs involving cobots, safety measurements, and security of data collected by cobots from jobsites significantly impact reliability, and the transparency of cobots’ inner workings can benefit accuracy, robustness, security, privacy, and communication and result in higher levels of automation, all of which demonstrated as contributors to trust. The study’s findings provide critical insights into the perceptions and experiences of AEC professionals toward adoption of cobots in construction and help project teams determine the adoption approach that aligns with the company’s goals workers’ welfare. 

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2. Toward a Framework for Trust Building between Humans and Robots in the Construction Industry: A Systematic Review of Current Research and Future Directions

   https://doi.org/10.1061/JCCEE5.CPENG-5656  

   Chang, Woei-Chyi; Hasanzadeh, Sogand (May 2024, Journal of Computing in Civil Engineering)

   With the construction sector primed to incorporate such advanced technologies as artificial intelligence (AI), robots, and machines, these advanced tools will require a deep understanding of human–robot trust dynamics to support safety and productivity. Although other disciplines have broadly investigated human trust-building with robots, the discussion within the construction domain is still nascent, raising concerns because construction workers are increasingly expected to work alongside robots or cobots, and to communicate and interact with drones. Without a better understanding of how construction workers can appropriately develop and calibrate their trust in their robotic counterparts, the implementation of advanced technologies may raise safety and productivity issues within these already-hazardous jobsites. Consequently, this study conducted a systematic review of the human–robot trust literature to (1) understand human–robot trust-building in construction and other domains; and (2) establish a roadmap for investigating and fostering worker–robot trust in the construction industry. The proposed worker–robot trust-building roadmap includes three phases: static trust based on the factors related to workers, robots, and construction sites; dynamic trust understood via measuring, modeling, and interpreting real-time trust behaviors; and adaptive trust, wherein adaptive calibration strategies and adaptive training facilitate appropriate trust-building. This roadmap sheds light on a progressive procedure to uncover the appropriate trust-building between workers and robots in the construction industry. 

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3. 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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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. Collaborative or Simply Uncaged? Understanding Human-Cobot Interactions in Automation

   https://doi.org/10.1145/3313831.3376547  

   Michaelis, Joseph E.; Siebert-Evenstone, Amanda; Shaffer, David Williamson; Mutlu, Bilge (April 2020, CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems)

   Collaborative robots, or cobots, represent a breakthrough technology designed for high-level (e.g., collaborative) interactions between workers and robots with capabilities for flexible deployment in industries such as manufacturing. Understanding how workers and companies use and integrate cobots is important to inform the future design of cobot systems and educational technologies that facilitate effective worker-cobot interaction. Yet, little is known about typical training for collaboration and the application of cobots in manufacturing. To close this gap, we interviewed nine experts in manufacturing about their experience with cobots. Our thematic analysis revealed that, contrary to the envisioned use, experts described most cobot applications as only low-level (e.g., pressing start/stop buttons) interactions with little flexible deployment, and experts felt traditional robotics skills were needed for collaborative and flexible interaction with cobots. We conclude with design recommendations for improved future robots, including programming and interface designs, and educational technologies to support collaborative use. 

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