中国安全科学学报 ›› 2025, Vol. 35 ›› Issue (S2): 43-49.doi: 10.16265/j.cnki.issn1003-3033.2025.S2.0009

• 安全工程技术 • 上一篇    下一篇

基于RLE与SF的蠕滑型滑坡失稳风险评价

杜岩1,2(), 吕梦镓1, 王麒凯2, 谢谟文1, 蒋宇静3, 刘敬楠3   

  1. 1 北京科技大学 城市地下空间工程北京市重点实验室, 北京 100083
    2 北京科技大学 未来城市学院, 北京 100083
    3 长崎大学 综合生产科学研究科, 日本 长崎 852-8521
  • 收稿日期:2025-07-10 出版日期:2026-02-04
  • 作者简介:

    杜 岩 (1985—),男,河南新乡人,博士,副教授,主要从事土木工程防灾减灾方面的研究。E-mail:

    谢谟文 教授

    蒋宇静 教授

  • 基金资助:
    国家重点研发计划项目(2023YFC3081400); 国家自然科学基金资助(42477167); 河北省自然科学基金资助(D2017203274); 高原山地环境下设施破坏机制与防护重庆市重点实验室开放基金资助(LQ24KFJJ09)

Research on risk assessment model of creep landslide instability based on RLE and SF

DU Yan1,2(), LYU Mengjia1, WANG Qikai2, XIE Mowen1, JIANG Yujing3, LIU Jingnan3   

  1. 1 Beijing Key Laboratory of Urban Underground Space Engineering, University of Science and Technology Beijing, Beijing 100083, China
    2 School of Future Cities, University of Science and Technology Beijing, Beijing 100083, China
    3 Department of Integrated Science and Technology Nagasaki University, Nagasaki 852-8521, Japan
  • Received:2025-07-10 Published:2026-02-04

摘要:

为解决受蓄水影响的蠕滑型滑坡分布广、规模大、危险性高等问题,引入滑坡风险等级特征值(RLE)指标,建立适用于工程实际的蠕滑型滑坡失稳风险动态评价模型。基于可拓学理论构建风险评价体系,综合考虑内部物理力学参数(黏聚力、内摩擦角、沉降速率、倾角变化率等)及外部环境因素(降雨强度、相对库水位及变化量等),采用主客观组合赋权法确定指标权重,利用RLE量化结果,实现对边坡失稳风险的动态识别与实时预警,并以白鹤滩水电站上游王家山滑坡为实例,利用多源现场监测数据验证模型的有效性与适用性。结果表明:RLE值由蓄水初期的1.50(Ⅱ级)上升至10月9日的2.92(Ⅲ级),与滑坡变形速率及现场风险演化趋势一致,并能在稳定性系数(SF)变化不显著阶段反映失稳风险变化;RLE与SF双指标联合模型可为蠕滑型滑坡提供多元动态风险信息支持,更好实现蠕滑型滑坡等工程现场不良地质体的科学风险管控。

关键词: 风险等级特征值(RLE), 稳定性系数(SF), 蠕滑型滑坡, 风险评价, 监测预警

Abstract:

The creep landslides induced by reservoir impoundment are widely distributed. They are large in scale and highly hazardous. To address these problems, a dynamic risk evaluation model for creep landslide instability, applicable to engineering practice, was established by introducing the RLE indicator. Based on extension theory, a comprehensive risk evaluation system was constructed by integrating internal physical and mechanical parameters such as cohesion, internal friction angle, settlement rate, and inclination change rate with external environmental factors including rainfall intensity, relative reservoir water level, and its variation. The weights of the evaluation indices were determined through a combined subjective and objective weighting method, and the RLE quantitative results were used to achieve dynamic identification and real-time early warning of slope instability risk. The Wangjiashan Landslide, upstream of the Baihetan Hydropower Station, was used as a case study, and multi-source field monitoring data were employed to verify the validity and applicability of the proposed model. The results indicate that the RLE value increases from 1.50 (Level Ⅱ) at the initial stage of impoundment to 2.92 (Level Ⅲ) on October 9, which is consistent with the deformation rate and the observed evolution trend of slope risk. The RLE is found to capture instability risk variations even when the SF shows insignificant changes. The RLE and SF dual-index joint model can provide multi-dimensional dynamic risk information support for creep landslides, better enabling scientific risk management of poor geological bodies at engineering sites.

Key words: risk level eigenvalue (RLE), stability factor (SF), creep landslide, risk assessment, monitoring and early warning

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