Physics-Informed Neural Network-based Reliability Analysis of Buried Pipelines
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| Format: | Preprint |
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2025
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| _version_ | 1866911266346369024 |
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| author | Taraghi, Pouya Li, Yong Adeeb, Samer |
| author_facet | Taraghi, Pouya Li, Yong Adeeb, Samer |
| contents | Buried pipelines transporting oil and gas across geohazard-prone regions are exposed to potential ground movement, leading to the risk of significant strain demand and structural failure. Reliability analysis, which determines the probability of failure after accounting for pertinent uncertainties, is essential for ensuring the safety of pipeline systems. However, traditional reliability analysis methods involving computationally intensive numerical models, such as finite element simulations of pipeline subjected to ground movement, have limited applications; this is partly because stochastic sampling approaches require repeated simulations over a large number of samples for the uncertain variables when estimating low probabilities. This study introduces Physics-Informed Neural Network for Reliability Analysis (PINN-RA) for buried pipelines subjected to ground movement, which integrates PINN-based surrogate model with Monte Carlo Simulation (MCS) to achieve efficient reliability assessment. To enable its application under uncertain variables associated with soil properties and ground movement, the PINN-based surrogate model is extended to solve a parametric differential equation system, namely the governing equation of pipelines embedded in soil with different properties. The findings demonstrate that PINN-RA significantly reduces the computational effort required and thus accelerates reliability analysis. By eliminating the need for repetitive numerical evaluations of pipeline subjected to permanent ground movement, the proposed approach provides an efficient and scalable tool for pipeline reliability assessment, enabling rapid decision-making in geohazard-prone regions. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_11613 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-Informed Neural Network-based Reliability Analysis of Buried Pipelines Taraghi, Pouya Li, Yong Adeeb, Samer Computational Engineering, Finance, and Science Machine Learning Data Analysis, Statistics and Probability Applications Buried pipelines transporting oil and gas across geohazard-prone regions are exposed to potential ground movement, leading to the risk of significant strain demand and structural failure. Reliability analysis, which determines the probability of failure after accounting for pertinent uncertainties, is essential for ensuring the safety of pipeline systems. However, traditional reliability analysis methods involving computationally intensive numerical models, such as finite element simulations of pipeline subjected to ground movement, have limited applications; this is partly because stochastic sampling approaches require repeated simulations over a large number of samples for the uncertain variables when estimating low probabilities. This study introduces Physics-Informed Neural Network for Reliability Analysis (PINN-RA) for buried pipelines subjected to ground movement, which integrates PINN-based surrogate model with Monte Carlo Simulation (MCS) to achieve efficient reliability assessment. To enable its application under uncertain variables associated with soil properties and ground movement, the PINN-based surrogate model is extended to solve a parametric differential equation system, namely the governing equation of pipelines embedded in soil with different properties. The findings demonstrate that PINN-RA significantly reduces the computational effort required and thus accelerates reliability analysis. By eliminating the need for repetitive numerical evaluations of pipeline subjected to permanent ground movement, the proposed approach provides an efficient and scalable tool for pipeline reliability assessment, enabling rapid decision-making in geohazard-prone regions. |
| title | Physics-Informed Neural Network-based Reliability Analysis of Buried Pipelines |
| topic | Computational Engineering, Finance, and Science Machine Learning Data Analysis, Statistics and Probability Applications |
| url | https://arxiv.org/abs/2511.11613 |