DETERMINANTS OF SUCCESSFUL WORKER-AUTONOMY TEAMING IN FUTURE CONSTRUCTION WORKPLACES

Construction is inherently a team-based industry, where completing complex tasks safely and effectively depends on workers collaborating in teams, establishing mutual trust, and maintaining seamless communication. As the industry undergoes a technological transformation with the integration of autonomous technologies—such as robots, drones, artificial intelligence (AI), and automation—this team-based nature will persist, evolving into a new paradigm of worker–autonomy teaming. Therefore, ensuring the success of worker-autonomy teaming in future construction becomes an important issue that must be tackled. There remains a pressing need for a comprehensive understanding of how workers will team up with auto-agents in future construction and a solution to enable the success of worker-autonomy teaming. To address this need, the overarching goal of this doctoral dissertation was to understand the determinants of successful human-autonomy teaming within construction and enable human-centered, safe, and inclusive future construction sites, facilitating the era of Construction 5.0.

To achieve this goal, this doctoral research conducted consecutive and correlated investigations related to trust-building, communication, safety, and inclusivity, which laid the foundation for developing a human-centered system that helps workers become successful in future construction. The results demonstrated that (i) workers might build inappropriate levels of trust in autonomy, (ii) workers might experience multifaceted mental demands during communication with autonomy, (iii) workers might not maintain proper situational awareness of dynamic auto-agents, and (iv) workers with Attention-deficit/hyperactivity disorder (ADHD) might over-prioritize their attention to the primary task, sacrificing their safety performance. All the findings highlighted a need for a human-centered solution to help workers succeed during interaction with autonomy. To address this need, this dissertation proposed and developed an AI-based human digital twin (HDT) framework that leverages wearable sensing to interpret workers states and provide tailored feedback in real-time to ensure their safety and well-being during human-autonomy teaming. Through the VR-based evaluation, the framework exhibited its capabilities to enhance workers’ situational awareness of drones by offering multi-modal alerts, and to improve their well-being by initiating drones’ adaptive behaviors. The proposed solution completed the loop of the discussions by enabling successful worker-autonomy teaming.

This dissertation contributes to the body of knowledge and practical implications through comprehensive consideration of multi-components (i.e., trust-building, communication, and safety), multi-entities (i.e., workers, autonomy, and construction sites), and multimodal data (i.e., subjective, behavioral, and psychophysiological responses) into the discussion of successful worker-autonomy teaming. Beyond, this dissertation made broader impacts on (i) the industry by promoting a human-centered paradigm aligned with Industry 5.0, (ii) the economy by proposing human-centered solutions that enhance workers’ safety and well-being, reduce operational costs, and broaden employment opportunities—especially for neurodiverse individuals, (iii) the society by advancing accessibility and inclusion for workers with neurodiversity, particularly ADHD, through supportive ecosystems that enable their meaningful participation in safety-critical industries like construction, and (iv) education by identifying critical competencies needed to prepare current and future workforces for successful worker–autonomy teaming.