Combined prediction model of concrete arch dam displacement based on cluster analysis considering signal residual correction

Abstract

Displacement is a crucial indicator reflecting the safety operational state of concrete arch dams. Consequently, the application of displacement monitoring models for arch dam structural health assessment has become widely prevalent. However, conventional singular displacement monitoring methods exhibit limited information extraction capabilities and lack precision, resulting in underexplored residual sequences from the monitoring model and unexcavated valuable information. To address this, this paper integrated cluster analysis with Long Short-Term Memory (LSTM), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Least Squares Support Vector Machine (LSSVM), and Particle Swarm Optimization (PSO) methods to establish a combined prediction model of concrete arch dam displacement based on cluster analysis considering signal residual correction. Firstly, a model combining cluster analysis k-means++ with LSTM (k-LSTM), was proposed for displacement prediction of arch dams. Secondly, CEEMDAN was employed to adaptively decompose the residual sequences, where Pearson correlation coefficient (PCC) was introduced to filter and reconstruct intrinsic mode components. Subsequently, PSO-LSSVM was utilized to predict the reconstructed sequences and obtain residual correction values. Finally, based on the summation of residual correction values and k-LSTM model predictions, the arch dam displacement combination prediction model was constructed. Engineering case demonstrates the efficiency of this model in arch dam displacement prediction. By effectively mining and utilizing valuable information from residual sequences, this combined model exhibits superior generalization capabilities over traditional singular models, thereby providing technical support and novel research perspectives for concrete arch dam health monitoring and operational management.