TY  - EJOU
AU  - Zong, Zixuan 
AU  - Long, Tingting 
AU  - Dong, Huaqing 
AU  - Huang, Guoqiang 
AU  - Meng, Xiao 
AU  - Azimi, Mohammadamin 

TI  - Residual Strength Prediction of Corroded Pipelines Based on Sparrow Search Algorithm-Optimized Kernel Extreme Learning Machine
T2  - Structural Durability \& Health Monitoring

PY  - 
VL  - 
IS  - 
SN  - 1930-2991

AB  - 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.
KW  - Pipeline corrosion; residual strength prediction; kernel extreme learning machine; sparrow search algorithm; machine learning

DO  - 10.32604/sdhm.2026.077489