摘要：Residual strength prediction of defective pipelines is critical to pipeline reliability assessment, which can affect the remaining useful life of pipelines. In this paper, we propose a novel method for predicting residual strength of defective pipelines based on deep extreme learning machine (DELM). To obtain the high accuracy, the hybrid teaching-learning-based optimization (HTLBO) algorithm with multiple adjustment strategies is designed to improve the DELM model. The experimental data of pipeline burst pressure is selected for the training and validation of proposed method. The interactions of input parameters on residual strength are investigated using response surface method. After comparisons of key model parameters, the optimal model is established to ensure the prediction accuracy. Through the validation of benchmark functions, the HTLBO performs well in conver-gence and optimization performance. The prediction results show that the proposed method has higher precision than other models, and it can predict the residual strength within the relative error of 6%. This study can provide a basis for reliability engineering and transportation safety of defective pipelines.

关键字：Residual strength prediction; Defective pipelines; DELM; HTLBO; Burst pressure

ISSN号：0951-8320

卷、期、页：卷: 237

发表日期：2023-09-01

影响因子：0.000000

期刊分区（SCI为中科院分区）：一区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：RELIABILITY ENGINEERING & SYSTEM SAFETY

通讯作者：苗兴园

第一作者：赵弘

论文类型：期刊论文

论文概要：苗兴园,赵弘，Novel method for residual strength prediction of defective pipelines based on HTLBO-DELM model *，RELIABILITY ENGINEERING & SYSTEM SAFETY，2023，卷: 237

论文题目：Novel method for residual strength prediction of defective pipelines based on HTLBO-DELM model *