基于时变监测数据的降雨滑坡多目标随机反分析

doi:10.16285/j.rsm.2017.11.037

›› 2017, Vol. 38 ›› Issue (11): 3371-3377.doi: 10.16285/j.rsm.2017.11.037

基于时变监测数据的降雨滑坡多目标随机反分析

郑亚飞^{1, 2, 4}，张璐璐^{1, 2, 3}，张洁⁵，郑建国⁶，于永堂⁶

1. 上海交通大学海洋工程国家实验室，上海 200240；2. 上海交通大学船舶海洋与建筑工程学院，上海 200240； 3. 高新船舶与深海开发装备协同创新中心，上海 200240；4. 国网上海电力设计有限公司，上海 200001； 5. 同济大学地下工程系，上海 200240；6. 机械工业勘察设计研究院，陕西西安 710043

收稿日期:2016-12-05 出版日期:2017-11-10 发布日期:2018-06-05
通讯作者: 张璐璐，女，博士，教授，主要从事岩土工程灾害和风险控制的研究工作。E-mail: lulu_zhang@sjtu.edu.cn E-mail: rewarte@163.com
作者简介:郑亚飞，男，1990年生，硕士研究生，主要从事岩土工程理论分析方面的研究工作。
基金资助:
国家重点基础研究发展计划973项目（No. 2014CB049100）；国家自然科学基金项目（No. 51422905, No.41372275, No.51679135）；中组部青年拔尖人才计划。

Multi-objective probabilistic inverse analysis of rainfall-induced landslide based on time-varied data

ZHENG Ya-fei^{1, 2, 4}, ZHANG Lu-lu^{1, 2, 3}, ZHANG Jie⁵, ZHENG Jian-guo⁶, YU Yong-tang⁶

1. State Key Laboratory of Ocean Engineering, Shanghai Jiaotong University, Shanghai 200240, China; 2. School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiaotong University, Shanghai 200240, China; 3. Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration (CISSE), Shanghai 200240, China; 4. State Grid Shanghai Electric Power Design Co., Ltd, Shanghai 200001, China; 5. Department of Geotechnical Engineering, Tongji University, Shanghai 200240, China; 6. China Jikan Research Institute of Engineering Investigations and Design Co., Ltd, Xi’an, Shaanxi 710043, China

Received:2016-12-05 Online:2017-11-10 Published:2018-06-05
Supported by:
This work was supported by the National Basic Research Program of China, 973 Program (2014CB049100), the National Natural Science Foundation of China (51422905, 41372275, 51679135) and the Top Grade Young Research (Ba Jian) Talent Program by the Organization Department of Central Committee of the CPC.

摘要/Abstract

摘要： 基于非饱和土流-固耦合理论和贝叶斯理论，建立了边坡的非饱和土流-固耦合随机反演模型，提出了基于马尔科夫链的多目标随机反分析方法，利用位移和孔隙水压力时变监测数据进行多目标和单目标随机反演，并对反演结果进行比较分析。结果表明，多目标随机反分析参数后验分布标准差较单目标随机反分析明显减小。单目标随机反分析只对本目标进行优化，对其他目标的预测误差较大。多目标随机反分析能同时对所有目标进行优化，反演结果对所有目标误差均较小，95%置信区间较单目标明显收窄，采用不同类型监测数据的多目标随机反分析所得结果更为可靠，预测更为准确。

关键词: 边坡, 流-固耦合, 多目标, 随机反分析

Abstract: Based on the solid-fluid coupling theory and Bayesian theory, a coupled probabilistic back analysis model is developed for an unsaturated soil slope. A method of multi-objective probabilistic inverse analysis using time-varied data of displacement and pore water pressure is proposed based on Markov chain theory. The results of the multi-objective inverse analysis and single-objective inverse analysis found that the posterior standard deviations of the input parameters obtained by multi-objective inverse analysis are smaller than the single-objective cases. It is also found that the results obtained by single-objective agree with the measurement well but have an unsatisfied prediction on other objectives. Multi-objective inverse analysis could optimize all the objectives simultaneously and the results of inverse analysis can meet well with all the objectives. For the multi-objectives case, 95% uncertainty bounds are narrower than the single-objective cases, and the soil parameters obtained by the multi-objective probabilistic inverse analysis using different types of data are more reasonable, and the prediction using the results of the multi-objective probabilistic inverse analysis is more correct than the single-objective probabilistic inverse analysis results.

Key words: slope, fluid-solid coupling, multi-objectives, probabilistic back analysis

中图分类号:

TU 4

郑亚飞，张璐璐，张洁，郑建国，于永堂,. 基于时变监测数据的降雨滑坡多目标随机反分析[J]. , 2017, 38(11): 3371-3377.

ZHENG Ya-fei, ZHANG Lu-lu, ZHANG Jie, ZHENG Jian-guo, YU Yong-tang,. Multi-objective probabilistic inverse analysis of rainfall-induced landslide based on time-varied data[J]. , 2017, 38(11): 3371-3377.

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基于时变监测数据的降雨滑坡多目标随机反分析

Multi-objective probabilistic inverse analysis of rainfall-induced landslide based on time-varied data

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