Intelligent Construction Risk Management Through Transfer Learning: Trends, Challenges, and Future Strategies

Abstract

Construction risk management has evolved significantly by integrating artificial intelligence (AI) technologies, particularly machine learning (ML), to enhance predictive capabilities. Transfer learning (TL), a promising subfield of ML, has the potential to further revolutionize construction safety by enabling models trained in one domain to be adapted for related tasks in construction risk scenarios. This systematic review explores the current trends in applying TL to construction risk management, identifies key challenges, and highlights future opportunities for advancement. The review first assesses TL's ability to mitigate common issues such as data scarcity, overfitting, and lengthy model training times, which often hinder traditional ML approaches. Key challenges include the complexity of domain adaptation, the lack of standardized datasets, and the need for robust validation methods. Despite these barriers, the potential for TL to improve predictive accuracy, efficiency, and cross-project learning makes it a transformative tool. Finally, future research directions are proposed to optimize TL techniques for real-time, intelligent construction risk management systems.