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基于云模型和Dempster Shafer证据理论融合多源数据进行基坑风险评估

Fusing multiple source data for foundation excavation risk assessment based on cloud model and Dempster Shafer evidence theory.

作者信息

An Yue, Li Liuyang, Gao Haoyuan, Luo Zhihao, He Yuefang

机构信息

School of Investment and Construction Management, Dongbei University of Finance and Economics, Dalian, Liaoning Province, China.

China Construction Eighth Engineering Division Co., Ltd., Shanghai, 200135, China.

出版信息

Sci Rep. 2025 Mar 22;15(1):9894. doi: 10.1038/s41598-025-94628-4.

Abstract

Limited data from single source pose a significant constraint on the accuracy of risk assessment conducted during the pre-excavation feasibility analysis stage. In order to address this issue, a risk assessment method that integrates cloud model (CM) with Dempster-Shafer (D-S) evidence theory is proposed. This method carefully selects multi-source risk evaluation indicators from project information and three-dimensional finite element numerical results to create project database and numerical database. By leveraging these databases, the CM generates the membership degrees for various evaluation indicators across different risk levels, which are then transformed into basic probability assignments within the D-S evidence theory. This allows for effective fusion of multi-source risk evaluation indicators achieving comprehensive quantitative evaluation of excavation. A narrow and elongated foundation project is assessed with a risk level of IV (safety) by the proposed approach. The outcomes provide a scientific basis for formulating preventive strategies and managerial decisions about foundation excavation during the pre-excavation.

摘要

来自单一来源的有限数据对开挖前可行性分析阶段进行的风险评估准确性构成了重大限制。为了解决这个问题,提出了一种将云模型(CM)与Dempster-Shafer(D-S)证据理论相结合的风险评估方法。该方法从项目信息和三维有限元数值结果中精心挑选多源风险评估指标,以创建项目数据库和数值数据库。通过利用这些数据库,云模型生成不同风险水平下各种评估指标的隶属度,然后将其转换为D-S证据理论中的基本概率分配。这使得多源风险评估指标能够有效融合,实现对开挖的全面定量评估。采用所提出的方法对一个狭长型基础工程进行评估,风险等级为IV(安全)。研究结果为开挖前制定基础开挖的预防策略和管理决策提供了科学依据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11de/11929853/e1af472d656f/41598_2025_94628_Fig1_HTML.jpg

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