Physics-Assisted Deep Learning Model for Improved Construction Performance Monitoring of Cutter Suction Dredger

Construction monitoring of cutter suction dredgers (CSD) is of great significance in ensuring dredging efficiency. However, existing models have not taken into account the physical constraints in the physical system of a CSD, which limits further improvements in prediction accuracy. To this end, this paper proposes a physics-assisted deep learning model for improved construction performance monitoring of CSD. The data-driven Loss_u and the physics-driven Loss_r are combined to form an improved physics-assisted loss function (PALF). And then, a physics-assisted deep learning (PADL) model incorporating PALF is developed to predict the construction productivity. In the case application, evaluation across three deep learning models confirms the feasibility and effectiveness of PALF for productivity prediction. The results show that the PALF-based PADL model achieves markedly improved prediction accuracy, reducing the mean absolute error by 20.33–54.33%. Across six training-set sizes (1000–11,000 samples), the improvement is larger in small-data scenarios, highlighting PADL’s strong low-sample robustness. The proposed model can effectively complement physical sensors in monitoring construction parameters and provide reliable decision support for assessing the operational state of CSDs.