管道(软件)
管道运输
人工神经网络
非线性系统
航程(航空)
断层(地质)
工程类
蒙特卡罗方法
流离失所(心理学)
结构工程
有限元法
非线性回归
计算机科学
算法
回归分析
机器学习
地质学
数学
地震学
统计
机械工程
心理治疗师
航空航天工程
物理
量子力学
心理学
作者
Wenyang Zhang,Francois Ayello,Doug Honegger,Yousef Bozorgnia,Ertugrul Taciroglu
摘要
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 R 2 > 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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