A statistical approach for evaluating crack defects in structures under dynamic responses

This article presents a new approach to processing data in identifying and evaluating defects of structures with deformation measurement signals. By using sensitive characteristics observed in the old statistical parameters such as mean values, variances, and standard deviations, we propose three new parameters including kurtosis, skewness of signals, and statistical density function. In addition, we apply cumulative function in statistical and data analysis. High reliability and accuracy of the present approach has been verified through 38 bridges with over 300 span structures in Ho Chi Minh City (HCMC). Furthermore, we have conducted experiments with a small-scale model at the Laboratory of Applied Mechanics (LAM) to compare computational results with ones derived from practical data. Obtained results show promising applicability of sensitive parameters extracted from statistical features in identifying and evaluating defects in structures.