Advanced prediction of pipeline vertical deformation and axial strain via multi-source data fusion and multi-task deep learning

Vertical deformation and axial strain are crucial indicators for evaluating pipeline performance. Accurate prediction of these indicators provides essential support for early warning and proactive maintenance. To achieve this, a multi-sensor monitoring system was constructed by combining an embedded static leveling instrument with a long-gauge fiber-optic grating sensor system to sequentially monitor vertical deformation and axial strain. A novel multi-task prediction model, HO-CNN-BiGRU-AM-HMTL, was proposed to capture the spatiotemporal correlations between deformation and strain. The sensitivity of the model’s hyperparameters was examined, and the effects of dataset size (DS) and prediction horizon on model performance were analyzed. The model’s performance was evaluated by comparing it with existing single-task learning (STL) and multi-task learning (MTL) models in terms of prediction accuracy and computational efficiency. The results indicated that hyperparameter tuning of the hippopotamus optimization module enhanced the model’s performance. The DS showed an initial improvement followed by a decline in performance, while the prediction horizon demonstrated a gradual decrease, then a sharp drop. Maintaining the DS between 4 and 5 months and the prediction horizon within 7 days is recommended. The HO-CNN-BiGRU-AM-HMTL model outperformed existing STL and MTL models in prediction accuracy, reducing computation time by 70.13%–88.48% compared to STL and improving it by 4.05%–40.54% over MTL. Although there was some computational overhead, the overall runtime of the model remained under 16 s, meeting the requirements for engineering applications. This study integrates pipeline engineering with deep learning techniques to enable advanced prediction of deformation and strain, providing crucial support for risk warning and proactive maintenance.