Similarity relationship and principal component feature-considered deformation safety monitoring for the observation point group of in-service ultra-high arch dams

Abstract

Given the deficiencies of existing methods, this work investigates a novel methodology for monitoring the deformation safety of in-service ultra-high arch dams. Regarding observation point group (OPG) with similar spatiotemporal deformation pattern as the modeling object, the deformation principal component (DPC) analysis model is constructed. Adaptive adjustment approach, Gaussian mutation disturbance, and Tent chaotic disturbance are introduced to optimize the search ability of sparrow search algorithm (SSA). Using the improved SSA, the parameter optimization approach of the DPC model is established. The four-level monitoring criteria are proposed by comprehensively considering the deviation degree between DPC and elastic state and the combined control limit of DPCs. Using the radial deformation observation data of the studied dam, the effectiveness of the proposed methodology is verified. The following findings are obtained. The established DPC analysis model can effectively reveal the main deformation patterns of OPGs. The improved SSA shortens the computation time, avoids premature convergence, and improves the DPC analysis model performance. The probabilistic and physical significance of the established four-level criteria is stricter than that of confidence interval criteria. Compared with confidence ellipsoid method, the proposed criteria are more conducive to practical engineering applications. The research provides technical support for the improvement of safety monitoring capability, disaster prevention, and lifetime extension of in-service ultra-high arch dams.