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Enhanced fragility analysis of buried pipelines through Lasso regression

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Abstract

Buried pipelines are one of the critical lifeline structures, and recently, efforts have been directed toward their probabilistic risk assessment. This paper explores the fragility analysis of buried pipelines due to permanent fault displacement. Although several studies have been carried out for the fragility analysis of buried pipelines, they are conditioned only on one significant input parameter. Unlike previous studies, the fragility curves presented in this paper are multi-dimensional, i.e., conditioned on all the significant input parameters. The fragility curves are generated using a machine learning technique called Lasso regression. This paper also explores the relative importance of various uncertain parameters on the fragility estimates. The fragility analysis results suggest that the fault displacement and fault–pipe crossing angle are the most important parameters.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 51608461), the Fundamental Research Funds for the Central Universities (Grant No. 2682016CX019), the open research fund of MOE Key Laboratory of High-speed Railway Engineering, Southwest Jiaotong University, and the State Key Laboratory for GeoMechanics and Deep Underground Engineering, China University of Mining & Technology (Grant No. SKLGDUEK1726).

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Authors and Affiliations

  1. School of Civil and Environmental Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Pengpeng Ni

  2. MOE Key Laboratory of High-speed Railway Engineering, Southwest Jiaotong University, Chengdu, 610031, China

    Pengpeng Ni & Kaiwen Liu

  3. Department of Civil and Environmental Engineering, University of California, Los Angeles, CA, 90095, USA

    Sujith Mangalathu

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  1. Pengpeng Ni
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Correspondence to Sujith Mangalathu.

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Ni, P., Mangalathu, S. & Liu, K. Enhanced fragility analysis of buried pipelines through Lasso regression. Acta Geotech. 15, 471–487 (2020). https://doi.org/10.1007/s11440-018-0719-5

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