Revolutionizing Safety Practices: Integrating Neuroscience into Predictive Analytics for the Construction Site Stress Reduction

The construction industry's dynamic and hazardous work environment necessitates continuous innovation to improve safety and efficiency. Traditional safety management practices struggle to address the dynamic nature of stressors and hazards as they often rely on static procedures and outdated protocols, which are inadequate for handling the ever-changing risks and complexities of modern construction projects. This is especially important as technology advances and optimization improvements become increasingly necessary to maintain high safety standards. This research aims to develop a novel framework integrating neuroscience principles with advanced predictive safety analytics to proactively anticipate and prevent potential safety issues. To this end, the authors re-identified problems and reviewed established and emerging technologies, thereby proposing the framework focusing on customizable and adaptive integration of data from multiple sources (e.g., Internet of Things (IoT) sensors, surveillance cameras, and biometric sensors). Challenges, such as data integration complexity, privacy concerns, and user acceptance, are addressed, with an emphasis on constructing reliable and interpretable algorithmic models. The framework is expected to benefit construction managers, companies, contractors, regulatory bodies, and technology providers by facilitating more efficient construction site operations and fostering safer work environments. By utilizing neurobiological models, the framework enhances the accuracy and reliability of machine learning models in predicting safety-related incidents. This research contributes to the advancement of construction safety practices by combining neuroscience-based stress detection with predictive analytics, and finally promoting a safer and more efficient construction industry.