@Article{sdhm.2026.077489,
AUTHOR = {Zixuan Zong, Tingting Long, Huaqing Dong, Guoqiang Huang, Xiao Meng, Mohammadamin Azimi},
TITLE = {Residual Strength Prediction of Corroded Pipelines Based on Sparrow Search Algorithm-Optimized Kernel Extreme Learning Machine},
JOURNAL = {Structural Durability \& Health Monitoring},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/sdhm/online/detail/26620},
ISSN = {1930-2991},
ABSTRACT = {This paper proposes a novel approach for predicting the residual strength of corroded pipelines by combining the Kernel Extreme Learning Machine (KELM) with Sparrow Search Algorithm (SSA) optimization. The proposed SSA-KELM model addresses the limitations of traditional evaluation methods and single machine learning models in residual strength prediction. A dataset comprising 80 samples from burst tests and finite element simulations was used to validate the model. Results demonstrate that the SSA-KELM model achieves superior prediction accuracy with a maximum relative error of 13.54% and minimum relative error of 0.20%. The model’s mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are 0.658%, 0.780%, and 4.38%, respectively, significantly outperforming conventional machine learning models and traditional assessment methods. This research provides a reliable tool for evaluating pipeline integrity and maintenance planning.},
DOI = {10.32604/sdhm.2026.077489}
}