Pearson K-Mean Multi-Head Attention Model for Deformation Prediction of Super-High Dams in First Impoundments

Abstract

The first impoundment of a super-high dam is a crucial period from dam construction to operation, in which the prediction of the dam deformation is vital for the continued safety of the dam. Therefore, a multi-head attention model based on Pearson K-means clustering is proposed, which is shortened to PKMA. The inputs of the PKMA include measurements of the displacements of plumb lines, water levels, air temperatures, dam body temperatures, water temperatures, and foundation temperatures. Among these inputs, variables related to displacements are regarded as the dominant explanatory factors. Hence, the K-means clustering based on the Pearson index is utilised to increase the weights of displacements in the PKMA. To involve the interactions between inputs, the MA mechanism of neural networks is used to simulate the relationship between inputs and deformation targets. The PKMA model had a maximum MSE of 1.2518 and a maximum MAE of 0.9017 for the model performance metrics at the study measurement points. Compared to the comparison models MA, HST, and LSTM, the performance metrics of the PKMA model are an improvement of an average of 87.02%, 72.42%, and 69.24%.