Probabilistic back analysis for rainfall-induced slope failure using MLS-SVR and Bayesian analysis

概率逻辑 复制 边坡稳定性 安全系数 边坡破坏 贝叶斯概率 统计 环境科学 数学 岩土工程 工程类
作者
Himanshu Rana,G. L. Sivakumar Babu
出处
期刊:Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards [Taylor & Francis]
卷期号:18 (1): 107-120 被引量:14
标识
DOI:10.1080/17499518.2022.2084555
摘要

Measurement and model uncertainties in soil parameters account for the difference between slope behaviour in the field and expected behaviour. The probabilistic back analysis is an effective approach to quantify these uncertainties in soil parameters. A new methodology for probabilistic back analysis is proposed to evaluate the uncertainties in soil parameters for observed data for slope. The proposed methodology implements multi-output least square support vector regression (MLS-SVR) to replicate the numerical model for slope under precipitation. This methodology also utilises a multi-objective genetic algorithm and Bayesian analysis to estimate updated statistics of soil parameters for observed data for slope. The rainfall-induced slope failure at Malin, Pune, India, in 2014 is used as a case study to validate the proposed methodology. The mean values of soil parameters are updated using multi-objective genetic algorithm for the expected values of safety factor. The uncertainties in soil parameters are estimated using Bayesian analysis. The updated statistics of input parameters suggest that matric suction governs the slope behaviour under rainfall precipitation. The results of the study suggest that continuous updating of the observations reduces the uncertainties involved in soil parameters. It is noted that the values of safety factor calculated using updated parameters are consistent with the slope failure observed in the field. Hence, results of the study can be used for the reliability-based design of slopes and the provision of remedial measures.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
高挑的寒松完成签到,获得积分10
1秒前
1秒前
阿M啊啊完成签到,获得积分10
2秒前
奇异喵完成签到,获得积分10
2秒前
hui完成签到,获得积分20
2秒前
SSS水鱼发布了新的文献求助30
4秒前
顺shun完成签到 ,获得积分10
4秒前
情怀应助huayi采纳,获得10
5秒前
djiwisksk66应助南佳采纳,获得10
5秒前
6秒前
Albertxkcj发布了新的文献求助10
6秒前
hui发布了新的文献求助10
6秒前
无花果应助奇异喵采纳,获得10
7秒前
7秒前
Gudeguy完成签到 ,获得积分10
9秒前
10秒前
10秒前
点点完成签到,获得积分10
11秒前
顺利纸飞机完成签到,获得积分10
11秒前
粽粽发布了新的文献求助20
12秒前
脑洞疼应助LL采纳,获得10
13秒前
无限的可乐完成签到,获得积分10
13秒前
orixero应助zhang005on采纳,获得10
14秒前
Orange应助leon采纳,获得10
14秒前
Lu发布了新的文献求助10
14秒前
cherry发布了新的文献求助10
16秒前
小二郎应助hui采纳,获得10
16秒前
Akim应助163采纳,获得10
16秒前
隐形曼青应助lcs24201002032采纳,获得10
16秒前
16秒前
xiangoak发布了新的文献求助20
17秒前
豆花发布了新的文献求助10
17秒前
田様应助ZCC采纳,获得10
18秒前
18秒前
19秒前
19秒前
斤斤发布了新的文献求助10
20秒前
hailey53完成签到,获得积分10
20秒前
5441588关注了科研通微信公众号
21秒前
21秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3969557
求助须知:如何正确求助?哪些是违规求助? 3514377
关于积分的说明 11173836
捐赠科研通 3249692
什么是DOI,文献DOI怎么找? 1794979
邀请新用户注册赠送积分活动 875537
科研通“疑难数据库(出版商)”最低求助积分说明 804836