Risk assessment and management of excavation system based on fuzzy set theory and machine learning methods

This paper presents a brief review on major accidents and conducts bibliometric analysis of risk assessment methods for excavation system in recent year. The summarization of potential risks during excavation provides an important index for establishing an early warning system. The applications of fuzzy set theory and machine learning methods in risk assessment during excavation are presented. A case study of excavation in Guangzhou metro station is used to demonstrate the application of a machine learning method for risk evaluation. The large amount of data collected by 3S techniques (RS, GIS and GPS) and sensors increases accuracy of risk assessment levels in excavation. These procedures, integrated into building information modelling (BIM) management platform, can manipulate dynamic safety risk monitoring, control, and management. Finally, the processing and analysis of big data obtained from 3S techniques and sensors provide promising perspectives for establishing integrated technology system for excavation.

Automated summary

This paper provides a brief review and bibliometric analysis of major accidents and risk assessment methods for excavation system in recent years. The study highlights the importance of summarizing potential risks during excavation for establishing an early warning system. It also presents the applications of fuzzy set theory and machine learning methods in risk assessment, along with a case study demonstrating the use of machine learning for risk evaluation in excavation. Moreover, the integration of 3S techniques and sensors into building information modelling platform shows promising perspectives for dynamic safety risk monitoring, control, and management.