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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 𝐹1 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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This content will become publicly available on April 1, 2026
Impacts of Physical and Informational Failures on Worker–Autonomy Trust 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.
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- Award ID(s):
- 2128970
- PAR ID:
- 10656040
- Publisher / Repository:
- ASCE
- Date Published:
- Journal Name:
- Journal of Construction Engineering and Management
- Volume:
- 151
- Issue:
- 4
- ISSN:
- 0733-9364
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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