# [1]肖斌,张恒宾,刘宏伟.改进PSO-BPNN算法在管道腐蚀预测中的应用[J].郑州大学学报(工学版),2022,43(01):27-33.[doi:10.13705/j.issn.1671-6833.2022.01.008] 　XIAO Bin,ZHANG Hengbin,LIU Hongwei.Application of Improved PSO-BPNN Algorithm in Corroded Pipelines Prediction[J].Journal of Zhengzhou University (Engineering Science),2022,43(01):27-33.[doi:10.13705/j.issn.1671-6833.2022.01.008] 点击复制 改进PSO-BPNN算法在管道腐蚀预测中的应用() 分享到： var jiathis_config = { data_track_clickback: true };

43卷

2022年01期

27-33

2022-01-09

## 文章信息/Info

Title:
Application of Improved PSO-BPNN Algorithm in Corroded Pipelines Prediction

Author(s):
School of Computer Science, Southwest Petroleum University, Chengdu 610500, China

Keywords:

TE973.6；TP183
DOI:
10.13705/j.issn.1671-6833.2022.01.008

A

Abstract:
Due to the impact of buried environment and medium transported, oil pipelines will be gradually corroded with the increasing of service life. Traditional methods for calculating the residual strength of corroded pipelines included formula calculation and finite element analysis, etc. Aiming at the problems of low calculation accuracy of formulas and too complicated finite element analysis (FEA) in the prediction of the residual strength of corroded pipelines, an improved particle swarm optimization neural network model (IPSO-BPNN) was proposed to predict the residual strength of corroded pipelines. Firstly, based on the traditional particle swarm optimization, a new nonlinear decreasing inertia weight was proposed to update the velocity and location of elements quickly, and the genetic crossover operator was introduced to increase the diversity of particles, then form an improved particle swarm optimization algorithm (IPSO). Secondly, the IPSO algorithm was used to optimize the weights and thresholds of the neural network, and initialize the neural network with optimized weights and thresholds to establish the IPSO-BPNN model. Finally, the linear regression(LR),FEA, back-propagation neural network(BPNN), particle swarm optimization back-propagation neural network(PSO-BPNN) and IPSO-BPNN model were experimented on real pipelines test blasting data sets to predict the residual strength of the corroded pipelines. MAE, RMSE and MAPE were used as indicators to evaluate the predic-tability of the models. The results on the test set of two data sets showed that the MAE of the IPSO-BPNN model was 0.525 4, 0.718 5; the MAPE was 3.77%, 2.68%; the RMSE was 0.672 6, 0.947 2, respectively. The three indicators were significantly improved compared to LR, FEA, BPNN and PSO-BPNN. It showed that this method could improve the accuracy of predicting the residual strength of corroded pipelines, and could provide a more accurate basis for pipeline inspection.

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