Collapse Risk Analysis of Deep Foundation Pits in Metro Stations Using a Fuzzy Bayesian Network and a Fuzzy AHP

Guohua Zhang,Weimin Qin,Guoqiang Zhong,Wu Chen,Yuyong Jiao,Chengtang Wang,Hao Wang

doi:10.1155/2020/4214379

Abstract

Collapse risk analysis is of great significance for ensuring construction safety in foundation pits. This study proposes a comprehensive methodology for dynamic risk analysis of foundation pit collapse during construction based on a fuzzy Bayesian network (FBN) and a fuzzy analytical hierarchy process (FAHP). Firstly, the potential risk factors contributing to foundation pit collapse are identified based on the results of statistical analysis of foundation pit collapse cases, expert inquiry, and fault tree analysis. Then, a FAHP and improved expert elicitation considering a confidence index are adopted to elicit the probability parameters of the BN. On this basis, quantitative risk reasoning and sensitivity analysis of foundation pit collapse are achieved by means of fuzzy Bayesian inference. Finally, an actual deep foundation pit in a metro station was used to illustrate a specific application of this approach, and the results were in accordance with the field observations and numerical simulation results. The proposed approach can provide effective decision-making support for planners and engineers, which is vital to the prevention and control of the occurrence of the foundation pit collapse accidents.

Highlights

With the rapid development of urbanization around the world, metros have become a promising solution for relieving overground traffic in congested urban areas
Metro construction is exposed to large potential risks due to various potential risk events in an uncertain environment. erefore, the number of accidents is increasing in metro engineering, especially in developing countries such as China
These approaches include Monte Carlo simulations (MCS) [4, 5], event tree analysis (ETA) [6, 7], fault tree analysis (FTA) [8, 9], and decision trees (DT) [10]. e above-mentioned methods have played a vital role in improving the risk management and control ability of large construction projects [11, 12]; in many circumstances, these methods are incapable of coping well with uncertainties and giving satisfactory results on account of incompleteness or shortage of data [13]

Summary

Introduction

With the rapid development of urbanization around the world, metros have become a promising solution for relieving overground traffic in congested urban areas. Various risk assessment approaches developed based on probabilistic risk analysis (PRA) have been widely used in construction projects to avoid financial losses and personnel casualties These approaches include Monte Carlo simulations (MCS) [4, 5], event tree analysis (ETA) [6, 7], fault tree analysis (FTA) [8, 9], and decision trees (DT) [10]. E above-mentioned methods have played a vital role in improving the risk management and control ability of large construction projects [11, 12]; in many circumstances, these methods are incapable of coping well with uncertainties and giving satisfactory results on account of incompleteness or shortage of data [13] It is often difficult or Mathematical Problems in Engineering sometimes even impossible to obtain statistical data for PRA modeling, and this approach often relies on expert knowledge and experience. When related parameters such as hydrological and geological parameters are changed, the above-mentioned approaches cannot accurately handle the updated features of dynamic process in project construction. erefore, effective real-time safety measures cannot be implemented with a change in environment [25]

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Conclusion