Long-span cable-stayed bridges, integral to traffic infrastructure, require robust structural health monitoring(SHM) systems to withstand multi-load environments like earthquakes and wind vibrations. Traditional monitoring systems encounter challenges such as redundant sensor data, reliance on sensor quantity and quality, and a scarcity of real damage data. This study introduces a hybrid framework combining finite element model updating with deep learning to overcome these issues. Utilizing the OpenSees platform, a finite element model of the bridge is developed and refined using the Bayesian Optimization to update four critical parameters. A structural response database is created under various excitations, such as white noise, ground motions, and wind loads. A multi-head attention bidirectional LSTM (MHA-BiLSTM) network is employed, using acceleration data from main beam sensor locations to accurately predict displacements of the main beam and tower under single and cross-load scenarios, achieving an average (coefficient of determination) of 0.98284 with an (root-mean-square error) of 0.385 for single loads and an average of 0.97407 with an of 0.56133 for multi-loads. This hybrid methodology provides a generalizable technical paradigm for multi-load response monitoring of analogous large-scale cable-supported bridges.
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