Comprehensive numerical analyses of the seismic performance of natural gas pipelines crossing earthquake faults

Zhang, Wenyang; Ayello, Francois; Honegger, Doug; Taciroğlu, Ertuǧrul; Bozorgnia, Yousef

doi:10.1177/87552930221087749

Cited by 6 publications

(4 citation statements)

References 38 publications

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“…Figure 2 displays the distributions of all input features mentioned above. Please refer to our previous study 46 for more details concerning the selection procedure of input parameters. We deliberately divided the database into two classes since sand‐ and clay‐type soils adopted different equations for soil springs’ coefficients in FEAs.…”

Section: Pipeline Strain Response Databasementioning

confidence: 99%

“…[37][38][39][40][41][42][43][44][45] The FEA can provide high-fidelity but generally is accompanied by high computational costs. In our previous study, 46 we considered 217,480 cases selected from detailed sensitivity analyses, which examined the relative importance among all realistic parameters, including (i) pipe dimensions and materials, (ii) soil properties, and (iii) style of faulting and characteristics of the fault movements. The nonlinear pipe-soil spring models (see Figure 1) were built based on the practical seismic guidelines for the design and analysis of natural gas pipelines.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning‐based prediction of the seismic response of fault‐crossing natural gas pipelines

Zhang¹,

Ayello²,

Honegger

et al. 2023

Earthq Engng Struct Dyn

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Herein, we utilized machine‐learning (ML) and data‐driven (regression) techniques to tackle a critical infrastructure engineering problem—namely, predicting the seismic response of natural gas pipelines crossing earthquake faults. Such a 3D nonlinear problem can take up to 10 h to solve by performing finite element analysis (FEA), considering the length of the pipeline and a large number of pipe and soil elements. However, the ML and data‐driven techniques can learn the projection rule of input‐output and predict the pipeline response instantaneously given a set of input features. In addition, the well‐trained ML model can be implemented for regional‐scale risk and rapid post‐event damage assessments. In this study, the input for ML comprised approximately 217K nonlinear FEAs, which covered a wide range of combinations of soil, structural and fault properties and yielded critical pipe strain responses under fault‐rupture displacements. We adopted various regression models and physics‐constrained neural networks, which can accurately and rapidly predict the tensile and compressive strains for a broad range of probable fault‐rupture displacements. Performances of various ML and conventional statistical models were systematically examined. Not surprisingly, neural networks exhibited the best performance for this multi‐output regression problem, in which R2 > 0.95 was achieved for a wide range of fault displacement (FD) levels. Further, we used the trained neural network with 14.5 million Monte‐Carlo‐generated input samples to predict the maximum tensile and compressive strain curves of pipelines. This new dataset aimed at filling the missing input‐output points from the 217K FEAs, and improved the accuracy of the prediction of probability of failure for natural gas pipelines under FD hazards.

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Section: Pipeline Strain Response Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning‐based prediction of the seismic response of fault‐crossing natural gas pipelines

Zhang¹,

Ayello²,

Honegger

et al. 2023

Earthq Engng Struct Dyn

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, when introducing multiple response indices, the number of samples becomes inadequate to obtain JFP under different seismic intensities. To address this issue, researchers have proposed various data simulation methods, 37–39 with the Copula function proving to be effective. The significant advantage of Copula functions lies in their requirement for only the knowledge of the marginal cumulative distribution function (CDF) of each variable derived from the limited original samples.…”

Section: Introductionmentioning

confidence: 99%

Improved seismic risk evaluation for high‐voltage switchgear equipment: A copula‐based framework considering joint failure modes

Wen,

Li,

Zhu

2023

Earthq Engng Struct Dyn

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Traditional seismic evaluations for high‐voltage switchgear equipment, the critical power facilities, consider only structural failure mode and may underestimate the actual seismic risk. By additionally introducing a probable functional failure mode caused by the excessive deformation of the switchable blade of equipment, a novel copula‐based framework is proposed to utilize limited samples to generate simulation data and evaluate the joint failure probability (JFP). The correlation index and Akaike Information Criterion are employed to optimize the Copula function. For illustration, a disconnector, representative switchgear equipment, was selected as an example and modeled with the experiment validation. Model analysis results demonstrate the Clayton type is the most competent for evaluation. Neglecting functional failure can significantly underestimate the actual failure probability at various seismic intensities, up to 50%. Furthermore, it was found that the current equipment material strength level already meets the requirements, and enhancing strength alone does not effectively improve reliability under strong earthquakes. For PGA intensities ≥0.7 g, the blade deformation capacity plays the primary reliability constraint, independent of material stress. A cost‐effective seismic design should optimize the combination of allowable material stress and blade deformation based on the JFP considering seismic intensity and targeted reliability.

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“…The study used the [11] approach for characterizing probabilistic fault displacement. It combined the probabilistic fault displacement hazard, the pipeline's mechanical response due to fault displacement loads and the empirical fragility functions to derive the annual exceedance rate of pipeline failure.Reference [29] conducted a comprehensive set of finite element simulations on 217,000 pipe-soil models using a supercomputer. They came up with estimated probabilistic axial strains for a large number of scenarios considering variations in pipeline properties, soil properties and fault movement characteristics.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Seismic Risk Analysis of Buried Pipelines Due to Permanent Ground Deformation for Victoria, BC

Dey

Tesfamariam

2022

Geotechnics

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Buried continuous pipelines are prone to failure due to permanent ground deformation as a result of fault rupture. Since the failure mode is dependent on a number of factors, a probabilistic approach is necessary to correctly compute the seismic risk. In this study, a novel method to estimate regional seismic risk to buried continuous pipelines is presented. The seismic risk assessment method is thereafter illustrated for buried gas pipelines in the City of Victoria, British Columbia. The illustrated example considers seismic hazard from the Leech River Valley Fault Zone (LRVFZ). The risk assessment approach considers uncertainties of earthquake rupture, soil properties at the site concerned, geometric properties of pipes and operating conditions. Major improvements in this method over existing comparable studies include the use of stochastic earthquake source modeling and analytical Okada solutions to generate regional ground deformation, probabilistically. Previous studies used regression equations to define probabilistic ground deformations along a fault. Secondly, in the current study, experimentally evaluated 3D shell and continuum pipe–soil finite element models were used to compute pipeline responses. Earlier investigations used simple soil spring–beam element pipe models to evaluate the pipeline response. Finally, the current approach uses the multi-fidelity Gaussian process surrogate model to ensure efficiency and limit required computational resources. The developed multi-fidelity Gaussian process surrogate model was successfully cross-validated with high coefficients of determination of 0.92 and 0.96. A fragility curve was generated based on failure criteria from ALA strain limits. The seismic risks of pipeline failure due to compressive buckling and tensile rupture at the given site considered were computed to be 1.5 percent and 0.6 percent in 50 years, respectively.

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Comprehensive numerical analyses of the seismic performance of natural gas pipelines crossing earthquake faults

Cited by 6 publications

References 38 publications

Machine learning‐based prediction of the seismic response of fault‐crossing natural gas pipelines

Machine learning‐based prediction of the seismic response of fault‐crossing natural gas pipelines

Improved seismic risk evaluation for high‐voltage switchgear equipment: A copula‐based framework considering joint failure modes

Probabilistic Seismic Risk Analysis of Buried Pipelines Due to Permanent Ground Deformation for Victoria, BC

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