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

Abstract

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.

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

“…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][38][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.…”

Utilizing Artificial Neural Networks and Random Forests to Forecast the Dynamic Amplification Factors of Non-Structural Components

“…In the domain of structural dynamics and earthquake engineering, several researchers [43][44][45][46][47][48] have extensively utilized ML techniques to predict both the linear and nonlinear behavior of structures. At the moment, the adoption of machine learning methods such as artificial neural networks (ANNs) and random forest (RF) is on the rise for the development of prediction models [49][50][51][52][53][54][55][56]. Artificial neural networks (ANNs) and random forest (RF) models are often categorized as contemporary AI methodologies [53].…”