A Review of Finite Element Analysis and Artificial Neural Networks as Failure Pressure Prediction Tools for Corroded Pipelines

Abstract

This paper discusses the capabilities of artificial neural networks (ANNs) when integrated with the finite element method (FEM) and utilized as prediction tools to predict the failure pressure of corroded pipelines. The use of conventional residual strength assessment methods has proven to produce predictions that are conservative, and this, in turn, costs companies by leading to premature maintenance and replacement. ANNs and FEM have proven to be strong failure pressure prediction tools, and they are being utilized to replace the time-consuming methods and conventional codes. FEM is widely used to evaluate the structural integrity of corroded pipelines, and the integration of ANNs into this process greatly reduces the time taken to obtain accurate results.

Method for Parameter Tuning of Hybrid Optimization Algorithms for Problems with High Computational Costs of Objective Function Evaluations

“…Once the artificial neural network is trained on a given set of data, the estimation of the objective function using unknown vectors is carried out using the optimized weights and biases from the training procedure. The implementation of ANN and FEA in problems similar to the one presented in this paper was described in [33], where the critical loads for the structures were investigated. The authors created the ANN metamodel using the python programming language (PyTorch) framework, which enables efficient parallelization, assisting in the reduction in model training time significantly.…”