A Multi-Task Temporal Fusion Framework for 48 h-Ahead Joint Prediction of Dam Crack Responses and Rebar Stress from Multi-Source Monitoring Data

Crack opening and reinforcement stress are two complementary indicators of the service state of reinforced concrete hydraulic structures, yet they are often predicted separately.This study develops a data-driven multi-task temporal fusion framework for joint 48 hahead prediction of dam crack responses and rebar stress using multi-source monitoring data. The measured data comprise five crack-monitoring series, five rebar-stress series,local temperature channels, reservoir water level, antecedent rainfall, and an auxiliary environmental signal from 2021-03-11 to 2025-03-06. Target responses are aligned only at commonmeasuredtimestamps; no synthetic target observations are introduced. A residual multi-task temporal fusion network (MTTF-Net) is proposed with a shared Transformer 10 encoder, attention pooling, task-specific decoders, and a response-continuity regularization term. The model is compared with persistence, Ridge regression, random forest, Extra Trees, XGBoost, and GRU baselines under a chronological train/validation/test split. On the independent test period, Ridge regression obtains the lowest overall RMSE (2.2968), whereas MTTF-Net provides the lowest crack RMSE (0.0141), the lowest overall MAE (1.0035), and the second-best overall RMSE (2.3813). These results indicate that the monitoring data contain a strong linear autoregressive component, while multi-task temporal fusion improves nonlinear crack-response prediction and remains competitive for stress forecasting. The source code is prepared as a public implementation package, whereas the measured monitoring dataset is subject to data-owner restrictions.