A Multi-Input Multi-Output Considering Correlation and Hysteresis Prediction Method for Gravity Dam Displacement with Interpretative Functions

The displacement of a concrete gravity dam is a direct manifestation of its deformation. It provides an intuitive reflection of the dam’s overall operational behavior and serves as a key indicator of the dam’s safe operating condition. In this paper, we propose a factor set that considers the hysteresis effects of temperature on displacement and ranks the importance of the features to select the optimal factor sets at different measurement points by the ReliefF method. Then, we realize the simultaneous prediction of the displacements at multiple measurement points by the multi-input multi-output least-squares support vector machine with particle swarm optimization (MIMO-PSO-LSSVM). The case study demonstrates that this method effectively enhances the accuracy and efficiency of gravity dam displacement prediction, thereby providing a novel reference for dam safety monitoring and health service diagnosis.