基于BN的软岩隧道大变形风险机理与评估

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摘要 研究目的: 在高地应力隧道中,软岩大变形的发生具有极大的不确定性,是风险评估中的重难点。为了对隧道软岩大变形风险进行准确有效的评估,本文基于贝叶斯网络(BN)提出一种针对大变形分级评估的方法,并依托成兰铁路云屯堡隧道,建立软岩大变形风险数据库,对六大风险源进行辨识和推理。
研究结论: (1)隧道软岩大变形是多因素耦合作用下的结果,通过建立云屯堡隧道软岩大变形风险数据库,将机理分为围岩稳定性及支护稳定性两方面,确立了贝叶斯网络模型及先验概率;(2)通过贝叶斯敏感性分析,影响软岩大变形的关键因素由主及次分别为围岩完整性、支护强度、地应力、岩石强度、施工工法及洞型;(3)通过在施工中对风险数据库的更新,贝叶斯网络能够良好地预测施工期隧道软岩大变形发生概率。

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	杨建辉
	沈恺
	舒禄山
	薛亚东
	王建江

关键词 ：软岩隧道, 大变形, 风险评估, 应力强度比, 贝叶斯网络

Abstract：Research purposes: In high geostress tunnels, the occurrence of large deformation of soft rock has great uncertainty, which is a major difficulty in risk assessment. In order to accurately and effectively assess the large deformation risk of soft rock tunnels, a hierarchical evaluation method for large deformation is proposed based on Bayesian network (BN). Relied on the Yuntunbao tunnel, soft rock large deformation risk database is established and the six major risk sources are identified and reasoned.
Research conclusions: (1) The large deformation of soft rock in tunnel is the result of multi factor coupling. By establishing the risk database, the mechanism is divided into two aspects: surrounding rock stability and support stability, and the Bayesian network model and a priori probability are established; (2) Through Bayesian sensitivity analysis, the key factors about the large deformation in soft rock tunnel are integrity of surrounding rock, support strength, ground stress, rock strength, construction method and tunnel shape; (3) Through the update of the risk database during the construction, the Bayesian network can well predict the probability of large deformation of the soft rock during the construction period.

Key words： soft rock tunnel large deformation risk assessment stress-strength ratio Bayesian network

收稿日期: 2021-09-08

PACS:

U459.2

基金资助:国家自然科学基金(52078377)

作者简介: ^**杨建辉,1965年出生,男,教授。

引用本文:

杨建辉, 沈恺, 舒禄山, 薛亚东, 王建江. 基于BN的软岩隧道大变形风险机理与评估[J]. 铁道工程学报, 2022, 39(3): 81-86.
YANG Jianhui, SHEN Kai, SHU Lushan, XUE Yadong, WANG Jianjiang. Risk Mechanism and Assessment of Large Deformation in Soft Rock Tunnel. Journal of Railway Engineering Society, 2022, 39(3): 81-86.

链接本文:

https://tdgcxb.crec.cn/CN/ 或 https://tdgcxb.crec.cn/CN/Y2022/V39/I3/81

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