首页|A reliability analysis framework coupled with statistical uncertainty characterization for geotechnical engineering

A reliability analysis framework coupled with statistical uncertainty characterization for geotechnical engineering

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  • 维普
Reliability analysis plays an important role in the risk management of geotechnical engineering.For the random field-based method,it is expected that the uncertainty characterization of geo-material param-eters and the realization of random field can be integrated effectively.Moreover,as the increase in mea-sured data size is generally difficult in the field investigation of geotechnical engineering due to limitation of budget and time etc.,the statistical uncertainty resulting from sparse data should be paid great attention.Therefore,taking the determination of hyper-parameters for Bayesian-based conditional random field as the breakthrough,this study proposed a reliability analysis framework to achieve the expectation above.In this proposed reliability analysis framework,the present characterization method of statistical uncertainty is improved by setting the lognormal distribution as the prior distribution of scale of fluctuation(SOF).Subsequently,the performance of statistical uncertainty characterization method is tested by a set of unconfined compressive strength(UCS)database about rocks.Then,a case study about the stability analysis of slope is employed to demonstrate the beneficial effect of the pro-posed reliability analysis framework.It is found that the uncertainty in both the realization of random field and the reliability analysis results can be significantly mitigated by the proposed reliability analysis framework.

Reliability analysisStatistical uncertaintyBayesian inferenceConditional random fieldGeotechnical engineering

Liang Han、Wengang Zhang、Lin Wang、Jia Fu、Liang Xu、Yu Wang

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School of Civil Engineering,Suzhou University of Science and Technology,Suzhou 215011,China

National Engineering Research Center of Gas Hydrate Exploration and Development,Guangzhou Marine Geological Survey,Guangzhou 511458,China

College of Civil and Transportation Engineering,Hohai University,Nanjing 210024,China

School of Civil Engineering,Chongqing University,Chongqing 400045,China

School of National Safety and Emergency Management,Beijing Normal University,Zhuhai 519087,China

Center of International Cooperation and Innovation for the Digital Economics,Chongqing University of Posts and Telecommunications,Chongqing 400065,China

Department of Architecture and Civil Engineering,City University of Hong Kong,Tat Chee Avenue,Kowloon,Hong Kong,China

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2024

10.1016/j.gsf.2024.101913

地学前缘(英文版)
中国地质大学（北京） 北京大学

地学前缘(英文版)

CSTPCD
影响因子：0.576
ISSN：1674-9871
年,卷(期)：2024.15(6)