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Advanced Risk Assessment for Deep Excavation in Karst Regions Using Improved Dempster–Shafer and Dynamic Bayesian Networks

Authors :
Zhenyu Lei
Yanhong Wang
Yu Zhang
Feng Gu
Zihui Zan
Yuan Mei
Wenzhan Liu
Dongbo Zhou
Source :
Buildings, Vol 14, Iss 9, p 3022 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

This study presents a novel risk-assessment methodology for deep foundation pit projects in karst regions, aimed at enhancing project safety and decision-making processes. This approach amalgamates fuzzy dynamic Bayesian networks with a refined Dempster–Shafer (DS) evidence theory to tackle the intricate uncertainties present in such contexts. A comprehensive risk index system, derived from historical accident cases, relevant standards, and the literature, encompasses environmental, design, construction, and management factors. Initial probabilities for each risk factor are determined through the integration of expert knowledge and fuzzy theory. The enhanced Dempster–Shafer theory is utilized to fuse diverse information sources, culminating in a robust and dynamic risk evaluation model. This model leverages real-time monitoring data to dynamically assess and adjust risk levels throughout the construction process. The validation of the proposed method is demonstrated through a detailed case study of the Guangzhou Tangxi Section 1 deep foundation pit project, which effectively identified critical risk factors and facilitated proactive construction strategy adjustments. To further evaluate the reliability of the methodology, comparisons were made with three alternative methods, and applications were conducted on three additional deep foundation pit projects. These comparative analyses confirm the superior reliability and applicability of the proposed methodology across varied scenarios.

Details

Language :
English
ISSN :
14093022 and 20755309
Volume :
14
Issue :
9
Database :
Directory of Open Access Journals
Journal :
Buildings
Publication Type :
Academic Journal
Accession number :
edsdoj.47f7da536da544589177454f56ead85b
Document Type :
article
Full Text :
https://doi.org/10.3390/buildings14093022