A comparative study of various metamodeling approaches in tunnel reliability analysis

IF 3 3区工程技术 Q2 ENGINEERING, MECHANICAL

Probabilistic Engineering Mechanics Pub Date : 2023-11-13 DOI:10.1016/j.probengmech.2023.103553

Axay Thapa , Atin Roy , Subrata Chakraborty

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引用次数: 0

Abstract

Various metamodeling approaches are applied in conjunction with Monte Carlo simulation and or the second moment-based method for reliability analyses of underground tunnels. However, there is no study regarding the suitability of such metamodels for reliability analyses of tunnels. An attempt is made here to make a comparative assessment of different metamodeling approaches for tunnel reliability analysis to comprehend the performances of various metamodels from the subset of machine learning methods. In doing so, the least square method based polynomial response surface method (RSM), mostly used in tunnel reliability analyses, and its improved version i.e., moving least square method-based RSM, are taken up for comparison. Further, the most successful empirical risk minimization-based Kriging model and the structural risk minimization principle-based support vector regression model are considered for comparison. Also, the sparse Bayesian regression found to be useful in solving various structural reliability analysis problems, is taken up for the present comparative study. Two numerical examples demonstrate the effectiveness of the selected metamodels in tunnel reliability analysis. It has been generally noted that the Kriging and SVR-based metamodels outperform in reliability estimates of underground tunnels.

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隧道可靠性分析中各种元建模方法的比较研究

各种元建模方法与蒙特卡罗模拟和基于二阶矩的方法相结合应用于地下隧道的可靠性分析。然而，这些元模型在隧道可靠性分析中的适用性尚无研究。本文试图对隧道可靠性分析的不同元模型方法进行比较评估，以从机器学习方法的子集中理解各种元模型的性能。为此，将隧道可靠性分析中常用的最小二乘法多项式响应面法(RSM)与改进后的基于移动最小二乘法的响应面法(RSM)进行比较。进一步，考虑最成功的基于经验风险最小化的Kriging模型和基于结构风险最小化原则的支持向量回归模型进行比较。此外，稀疏贝叶斯回归在解决各种结构可靠度分析问题中也很有用，本文对此进行了比较研究。两个算例验证了所选元模型在隧道可靠性分析中的有效性。人们普遍注意到，基于Kriging和svr的元模型在地下隧道可靠性估计方面表现优异。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Probabilistic Engineering Mechanics 工程技术-工程：机械

CiteScore

3.80

自引率

15.40%

发文量

审稿时长

13.5 months

期刊介绍： This journal provides a forum for scholarly work dealing primarily with probabilistic and statistical approaches to contemporary solid/structural and fluid mechanics problems encountered in diverse technical disciplines such as aerospace, civil, marine, mechanical, and nuclear engineering. The journal aims to maintain a healthy balance between general solution techniques and problem-specific results, encouraging a fruitful exchange of ideas among disparate engineering specialities.