Using cased based reasoning for automated safety risk management in construction industry

Abstract

The purpose of this study is to develop a CBR-based platform that integrates all steps of safety risk management (SRM) for the realization of automation in construction safety risk management. To realize this purpose, accident data from 2015 to 2020 in China has been collected and analyzed. Based on the collected data, this study adopts system thinking and stakeholder theory to establish an accident attribute system for accurate and comprehensive case representation, which was usually ignored in the previous study. Following case representation, the development process of case-based reasoning (CBR) is introduced in detail. The core of CBR—case retrieval is designed with the k-Nearest Neighbor (k-NN) algorithm and rough set theory to improve retrieval accuracy. In terms of case reuse and revise, a hybrid risk response model based on historical experience and risk factor control is proposed. Finally, the practical application of this study is illustrated with a specific construction project. The findings of this study confirm the feasibility of machine learning in improving safety performance at construction sites. Construction accidents can be effectively prevented by identifying the potential safety risks, and employing on-site management.