Research on progressive failure process of Baishuihe landslide based on Monte Carlo model

被引:0
|
作者
Fasheng Miao
Yiping Wu
Yuanhua Xie
Feng Yu
Lijuan Peng
机构
[1] China University of Geosciences,Faculty of Engineering
[2] Ministry of Education,Three Gorges Research Center for Geo
关键词
Landslide; Monte Carlo; Numerical simulation; Progressive failure; Reservoir level; Rainfall;
D O I
暂无
中图分类号
学科分类号
摘要
To the progressive landslide, development of the internal deformation and failure situation can’t be accurately reflected by the overall stability of coefficients and failure probability. But this problem can be solved by utilizing the principle of progressive failure by slices. Taking the warning area of Baishuihe landslide as an example, 5 days accumulated rainfall in different reappearing period is computed by Gumbel model. The failure probability of each slice is calculated by progressive failure principle, which is based on Monte Carlo model. The following results can be revealed through calculation: Overall stability and failure probability can’t reflect real situation of Baishuihe landslide warning area. Through building the calculation of progressive failure model of each slice, the stability of each part in the Baishuihe landslide warning area is quite different. Unstable region mainly lies in vicinity of the middle and posterior warning area. The front of the warning area remains stable. Deformation characteristics of the warning area are consistent with the investigation report. The scope of unstable area increased gradually with rainfall and the decline of reservoir water. Under 5 day’s accumulated rainfall of 50 years, the poor stable and unstable region reached 75 %, there is a large possibility of local deformation slip. Under the joint action of rainfall and reservoir water level, the warning area of Baishuihe landslide shows a progressive failure mode from top to bottom.
引用
收藏
页码:1683 / 1696
页数:13
相关论文
共 50 条
  • [31] A Monte Carlo simulation model for vacuum membrane distillation process
    Imdakm, A. O.
    Khayet, M.
    Matsuura, T.
    JOURNAL OF MEMBRANE SCIENCE, 2007, 306 (1-2) : 341 - 348
  • [32] Application Research on Monte Carlo Simulation in Machining Process at Quality Control
    Li Xiaolei
    MANAGEMENT ENGINEERING AND APPLICATIONS, 2010, : 241 - 245
  • [33] VALIDATION OF THE MONTE CARLO MODEL OF THE GREEK RESEARCH REACTOR CORE
    Kontogeorgakos, D.
    Stamatelatos, I. E.
    NUCLEAR TECHNOLOGY, 2010, 170 (03) : 460 - 464
  • [34] Performance Analysis of a Process-based Stand Growth Model Using Monte Carlo Techniques
    Makela, Annikki
    SCANDINAVIAN JOURNAL OF FOREST RESEARCH, 1988, 3 (1-4) : 315 - 331
  • [35] The research for uncertainty heat transfer process of phase change thermal storage based on Monte Carlo method
    Li, Zhiyong
    Zhao, Yuqing
    Zou, Xue
    ADVANCES IN ENERGY SCIENCE AND TECHNOLOGY, PTS 1-4, 2013, 291-294 : 632 - 635
  • [36] Clinical implementation of a Monte Carlo based QA process for IMRT
    Popescu, IA
    Shaw, C
    Cranmer-Sargison, G
    Wells, D
    Zavgorodni, S
    Mann, R
    Beckham, W
    RADIOTHERAPY AND ONCOLOGY, 2004, 73 : S87 - S87
  • [37] Simulating particle collision process based on Monte Carlo method
    Zhang, Huang
    Liu, Qianfeng
    Qin, Benke
    Bo, Hanliang
    JOURNAL OF NUCLEAR SCIENCE AND TECHNOLOGY, 2015, 52 (11) : 1393 - 1401
  • [38] On Monte Carlo simulation for the HJM model based on jump
    Park, Kisoeb
    Kim, Moonseong
    Kim, Seki
    COMPUTATIONAL SCIENCE - ICCS 2006, PT 1, PROCEEDINGS, 2006, 3991 : 38 - 45
  • [39] Research on Ship Collision Probability Model Based on Monte Carlo Simulation and Bi-LSTM
    Vuksa, Srdan
    Vidan, Pero
    Bukljas, Mihaela
    Pavic, Stjepan
    JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2022, 10 (08)
  • [40] Monte Carlo Location Algorithm Based on Model Prediction
    Li, Peisong
    Zhang, Ying
    Zhang, Qiman
    2018 INTERNATIONAL SYMPOSIUM IN SENSING AND INSTRUMENTATION IN IOT ERA (ISSI), 2018,