DP-GMM clustering-based ensemble learning prediction methodology for dam deformation considering spatiotemporal differentiation

摘要

The deformation behavior can effectively reflect the health status of a dam, which can suffer from dynamic and time-varying loading modes and material properties, exhibiting distinct spatiotemporal differentiation. The conventional dam health evaluations, however, focus on the displacement regularity of local monitoring points, ignoring the spatiotemporal diversity in deformation behavior, and thus cannot truly reflect the status of the entire dam. Moreover, most of the existing displacement predictions are conducted on a single-algorithm machine-learning-based model, which focuses on single-task regression and disregards the potential cross-relatedness among the deformation in adjacent areas of the dam, showing less competitiveness in complex input?output relationship learning for various spatiotemporal conditions. For more reliable deformation forecasting, an integrated displacement prediction methodology considering spatiotemporal differentiation is proposed based on the spatiotemporal clustering and machine learning algorithms, in which the minimum-densityentropy-optimized density peaks clustering method is embedded with Gaussian-mixture-model-based clustering to achieve the reliable spatiotemporal differentiation identification of dam behavior and a multioutput ensemble learning framework coupled with the extreme learning machine and support vector machine is designed to capture the complex mapping from environmental factors to deformation, considering the cross-relatedness and spatiotemporal diversity. Moreover, a synchronous optimization strategy based on improved grey wolf optimization is embedded to enhance the performance. Hence, the final deformation behavior is forecasted by the ensemble learning of various data features and mapping rules. Taking the displacement observations of an actual dam as an example, the results show that the proposed methodology achieves excellent performance in dam behavior forecasting.