Temporal-aware dam displacement prediction using Bayesian-augmented Gaussian process with double clustering analysis

Abstract

Accurate displacement prediction is critical for the operation and maintenance of concrete dams. Existing methods overemphasize the global pattern of displacement behavior while neglecting the temporal association in monitoring sequences. Therefore, we propose a Bayesian-augmented Gaussian process (BaGP) integrated with double clustering analysis for temporal-aware dam displacement prediction. First, a double clustering approach is employed to thoroughly explore environmental patterns and further consider temporal association in displacement sequence evolution. Specifically, a deep clustering algorithm is introduced for the first time to extract high-quality data patterns within multidimensional nonlinear environment data. Subsequently, temporal-aware similarity among displacement sequences is quantified using dynamic time warping, and an improved K-means clustering algorithm is employed to identify temporally similar displacement phases within each environment cluster, enabling cluster-wise division. A nonparametric BaGP model is developed by augmenting standard GPs through Markov chain Monte Carlo simulation, Bayesian evidence evaluation, and model selection, to comprehensively address model structural flexibility and parameter adaptability across diverse data patterns. Each cluster’s data is sequentially fed into the BaGP model, with ensemble strategies used to generate integrated, cluster-wise predictions. Validation on real-world dam monitoring datasets demonstrates that our method achieves an average R of 0.973, outperforming models that ignore temporal association. Two additional cases also confirm its generalizability, thus providing a novel tool for structural health monitoring.