Deep learning-based data analytics for safety in construction

Abstract

Deep learning has been acknowledged as being robust in managing and controlling the performance of construction safety. However, there is an absence of state-of-the-art review that examines its developments and applications from the perspective of data utilization. Our review aims to fill this void and addresses the following research question: what developments in deep learning for data mining have been made to manage safety in construction? We systematically review the extant literature of deep-learning-based data analytics for construction safety management, including: (1) image/video-based; (2) text-based; (3) non-visual sensor-based; and (4) multi-modal-based. The review revealed three challenges of existing research in the construction industry: (1) lack of high-quality database; (2) inadequate ability of deep learning models; and (3) limited application scenarios. Based on our observations for the prevailing literature and practice, we identify that future research on safety management is needed and focused on the: (1) development of dynamic multi-modal knowledge graph; and (2) knowledge graph-based decision-making for safety. The application of deep learning is an emerging line of inquiry in construction, and this study not only identifies new research opportunities to support safety management, but also facilitates practicing deep learning for construction projects.