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The integration of robots, particularly drones, into future construction sites introduces new safety challenges requiring enhanced situational awareness (SA) among workers. To address these challenges, this study explores the effectiveness of an AI-driven assistant designed to inform workers about dynamic environmental changes via auditory and visual channels. A mixed-reality bricklaying experiment was developed, simulating worker-drone interactions across three interaction levels: coexistence, cooperation, and collaboration. One hundred five construction-background students participated in tasks with and without the AI assistant, during which their eye-tracking data, productivity, and subjective perceptions were collected. Results indicated that the AI assistant significantly expedited workers’ awareness of approaching drones but concurrently reduced bricklaying productivity. Although participants reported high perceived usefulness and low distraction by the AI assistant itself, findings revealed a trade-off: improved SA toward drones came at the cost of decreased task performance, likely due to increased attentional shifts toward drones. Furthermore, the effectiveness of the assistant varied depending on the interaction level with drones. This study highlights both the opportunities and challenges of applying AI-driven informational systems in future construction environments, offering critical insights for designing human-centered AI technologies that balance safety enhancement with productivity maintenance. 

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. 

While drones have exhibited considerable potential to revolutionize the construction industry, previous studies across domains have proposed that human-drone interaction could cause adverse impacts on humans (e.g., collision and discomfort). Given that construction has been recognized as a hazardous and high-stress workplace, it deserves deep exploration regarding how newly introduced drones will influence worker well-being during the interaction. However, there is a paucity of research on worker stress when communicating with drones in construction. Successful human-drone interaction must necessitate seamless and comfortable communication between workers and drones. Therefore, this study investigates the impact of physically demanding response levels on construction workers’ mental and physical well-being throughout the communication cycle. Three levels of physical responses (low, medium, and high) required for drone communication were simulated in an extended reality roofing experiment. During the communication process, real-time stress levels were assessed through participants’ electrodermal activity. The results indicated that a higher physical level of communication significantly increased workers’ higher stress levels in both the response and decoding phases. Additionally, providing drones’ feedback in verbal human-drone communication is especially important to reduce workers’ confusion and mental stress. This study highlights the critical need for worker-centric design and communication strategies in drone integration within construction. 

While unmanned aerial vehicles (a.k.a. drones) have been recognized as potential robotic teammates that could be incorporated into the construction industry, communication between workers and drones may impose additional mental demands and workloads that could lead to workers’ mental overload on construction jobsites. To address this concern, this study examines and quantifies workers’ mental demands while communicating with drones at different human-drone interaction levels—coexistence, cooperation, and collaboration. During a futuristic bricklaying experiment wherein workers needed to communicate with drones at different interaction levels, psychophysiological sensors measured electrodermal activity, brain activation, and eye movements to assess whether the respective interactions affected workers’ mental demands. The results indicate that coexistence requires workers’ visual attention, whereas cooperation imposes affective and perceptual demands since workers were frustrated and confused when decoding and responding to messages from the drone. Moreover, higher levels of mental demands were identified in collaborative communications because sharing an object with nearby drones raised workers’ safety concerns. This research contributes to the body of knowledge by demonstrating workers experience varying dimensions of mental demands during communication with drones, and the study suggests strategies to enhance effortless worker-drone communication at coexistence, cooperation, and collaboration levels to improve worker well-being in future construction. 

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. 

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. 

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.