Application of a KAN-LSTM Fusion Model for Stress Prediction in Large-Diameter Pipelines

Accurately predicting stress in large-diameter sewage pipelines is critical for ensuring their structural reliability and safety. To meet the safety requirements of large-diameter concrete pipes, we propose a hybrid model that integrates Kolmogorov-Arnold Networks (KAN) with Long Short-Term Memory (LSTM) neural networks. The model is trained and validated using actual pipeline monitoring data, ensuring that it accurately captures both the temporal dependencies and nonlinear stress patterns inherent in such systems. By modifying the fully connected layer of the original LSTM model, we develop a novel LSTM-KAN model and evaluate its performance through comprehensive predictive analysis. Comparisons with a traditional LSTM model reveal that the LSTM-KAN model—in which the fully connected layer is replaced by a KAN layer—achieves significantly lower loss and higher accuracy with fewer training iterations. Specifically, the proposed model attains a mean absolute error (MAE) of 0.033, a root mean square error (RMSE) of 0.035, and a coefficient of determination (R2) of 0.92, underscoring its superior accuracy and efficiency, and it can be used for the long-term prediction of stress in large-diameter pipes. Moreover, the integration of KAN significantly improves the nonlinear modeling capacity of the conventional LSTM, enabling the hybrid model to effectively capture complex stress variations under variable operating conditions. This work not only provides novel technical support for the application of deep learning in pipeline stress prediction but also offers a robust framework adaptable to other structural health monitoring applications.