Phan, H.C. and Dhar, A.S. and Bui, N.D. (2023) Reliability assessment of pipelines crossing strike-slip faults considering modeling uncertainties using ANN models. Reliability Engineering and System Safety, 237. ISSN 09518320
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Buried pipelines are crucial infrastructure that sometimes suffers from the adverse effects of ground movements resulting from the earthquake. A pipe crossing strike-slip fault can experience high tensile or compressive strains due to fault displacement during the earthquake. Various analytical and numerical methods are available in the literature to calculate the pipe wall strain through modeling the pipe-soil interaction. These methods are often too complex and not suitable for performing extensive parametric studies. In this research, Artificial Neural Network (ANN) based methods were developed for calculating the peak tensile and peak compressive strains. Due to the lack of field data, finite element analysis was performed to investigate the complex behavior of pipe under the fault movements and generate a database to develop the ANN models. The study revealed the weakness of ANN models that although a very high coefficient of determination (i.e., R2 ≈ 1) and low error can be obtained, the strains predicted using these models can significantly be different from the actual strains due to model uncertainty. To this end, the random strains were predicted by adding the calculated strains with the randomized model errors. Using the random strains, calculating the probability of exceedance of strains was proposed. Finally, a framework is developed for failure probability assessment of pipelines subjected to fault movements accounting to the uncertainty of ANN models. © 2023
Item Type: | Article |
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Divisions: | Institutes > Institute of Techniques for Special Engineering |
Identification Number: | 10.1016/j.ress.2023.109371 |
Uncontrolled Keywords: | Complex networks; Earthquakes; Fault slips; Numerical methods; Pipelines; Random errors; Reliability analysis; Strain; Strike-slip faults; Uncertainty analysis, Adverse effect; Artificial neural network modeling; Buried pipelines; Compressive strain; Fault movement; Ground movement; Modeling uncertainties; Pipeline crossing; Reliability assessments; Strike-slip fault movement, Neural networks |
URI: | http://eprints.lqdtu.edu.vn/id/eprint/10834 |