Wavelet-fractal diagnosis model and its criterion for concrete dam crack status

Abstract

The methods on fractal, phase space reconstruction and wavelet are integrated to analyze the observed data of concrete dam cracks. The diagnosis model and appropriate criterion for evolution characteristics of concrete dam crack are developed. Firstly, a wavelet threshold value de-noising algorithm is introduced to process the crack observations of concrete dam. The phase space reconstruction for data series after noise reduction is investigated and the parameter selection method is presented. Then the correlation dimension and Kolmogorov entropy-based models are built to evaluate the crack status of concrete dam. The corresponding criteria are given. Finally, the identification capability of the proposed approach is demonstrated with an actual case. It is indicated that the noise reduction of observed data has the important effect on improving the diagnosis accuracy of crack status. The observed behavior of dam crack has obvious fractal characteristics. The effective diagnosis for crack status of concrete dam can be implemented using the correlation dimension and Kolmogorov entropy-based criteria.