Damage Identification of Concrete Arch Dams Based on Wavelet Packets and Neural Networks

Abstract

A dam may be damaged by occasional extreme loads such as major earthquakes or terrorist attacks during its service. According to the needs of emergency assessment, this paper studies a rapid damage identification method for damage location and damage degree in concrete arch dams which is based on the dynamic characteristics of concrete arch dam data, using wavelet transform, wavelet packet decomposition, a BP neural network and D-S evidence theory for damage identification and related experimental verification. The results show that the relative difference of the curvature mode (δφk), the wavelet coefficient (Wfk) and the relative difference of the wavelet packet energy (δKk) can effectively identify the damage position of the arch dam, and δφk in the first four modalities has the best overall recognition effect; Wfk requires a high number of measurement points, which should be at least 64 or as close as possible; δKk has a better damage recognition effect than the first two at the same number of measurement points. D-S evidence theory significantly improves the damage identification effect and reduces the misjudgment of the single-damage method. The trained neural network can effectively identify the damage degree based on the data of one measuring point when there is a single damage instance, and the number of measuring points should be no fewer than two when there is double damage. The test results verify the feasibility of the method in this paper, which can provide a theoretical basis for a post-disaster emergency assessment information system of concrete arch dams.