Changchao Qi¹, Lingdi Fu¹, Ming Wen¹, Hao Qian², Shuai Zhao^1,*

Structural Durability & Health Monitoring, Vol.19, No.4, pp. 1073-1087, 2025, DOI:10.32604/sdhm.2025.061898 - 30 June 2025

Abstract The accuracy and reliability of non-destructive testing (NDT) approaches in detecting interior corrosion problems are critical, yet research in this field is limited. This work describes a novel way to monitor the structural integrity of steel gas pipelines that uses advanced numerical modeling techniques to anticipate fracture development and corrosion effects. The objective is to increase pipeline dependability and safety through more precise, real-time health evaluations. Compared to previous approaches, our solution provides higher accuracy in fault detection and quantification, making it ideal for pipeline integrity monitoring in real-world applications. To solve this issue, statistical… More >

Yaojin Fan, Huaqing Dong^*, Zixuan Zong, Tingting Long, Qianglin Huang, Guoqiang Huang

Structural Durability & Health Monitoring, Vol.19, No.3, pp. 731-769, 2025, DOI:10.32604/sdhm.2025.061056 - 03 April 2025

Abstract This study presents a comprehensive investigation of residual strength in corroded pipelines within the Yichang-Qianjiang section of the Sichuan-East Gas Pipeline, integrating advanced numerical simulation with experimental validation. The research methodology incorporates three distinct parameter grouping approaches: a random group based on statistical analysis of 389 actual corrosion defects detected during 2023 MFL inspection, a deviation group representing historically documented failure scenarios, and a structural group examining systematic parameter variations. Using ABAQUS finite element software, we developed a dynamic implicit analysis model incorporating geometric nonlinearity and validated it through 1:12.7 scaled model testing, achieving prediction… More >

Qi Zhuang^1,*, Dong Liu², Zhuo Chen³

Energy Engineering, Vol.121, No.3, pp. 821-834, 2024, DOI:10.32604/ee.2023.044054 - 27 February 2024

Abstract Oil and gas pipelines are affected by many factors, such as pipe wall thinning and pipeline rupture. Accurate prediction of failure pressure of oil and gas pipelines can provide technical support for pipeline safety management. Aiming at the shortcomings of the BP Neural Network (BPNN) model, such as low learning efficiency, sensitivity to initial weights, and easy falling into a local optimal state, an Improved Sparrow Search Algorithm (ISSA) is adopted to optimize the initial weights and thresholds of BPNN, and an ISSA-BPNN failure pressure prediction model for corroded pipelines is established. Taking 61 sets More >