Seismic Damage Identification Method for Curved Beam Bridges Based on Wavelet Packet Norm Entropy

Abstract

Curved beam bridges, whose line type is flexible and beautiful, are an indispensable bridge type in modern traffic engineering. Nevertheless, compared with linear bridges, curved beam bridges have more complex internal forces and deformation due to the curvature; therefore, this type of bridge is more likely to suffer damage in strong earthquakes. The occurrence of damage reduces the safety of bridges, and can even cause casualties and property loss. For this reason, it is of great significance to study the identification of seismic damage in curved beam bridges. However, there is currently little research on curved beam bridges. For this reason, this paper proposes a damage identification method based on wavelet packet norm entropy (WPNE) under seismic excitation. In this method, wavelet packet transform is adopted to highlight the damage singularity information, the norm entropy of wavelet coefficient is taken as a damage characteristic factor, and then the occurrence of damage is characterized by changes in the damage index. To verify the feasibility and effectiveness of this method, a finite element model of Curved Continuous Rigid-Frame Bridges (CCRFB) is established for the purposes of numerical simulation. The results show that the damage index based on WPNE can accurately identify the damage location and characterize the severity of damage; moreover, WPNE is more capable of performing damage location and providing early warning than the method based on wavelet packet energy. In addition, noise resistance analysis shows that WPNE is immune to noise interference to a certain extent. As long as a series of frequency bands with larger correlation coefficients are selected for WPNE calculation, independent noise reduction can be achieved.

Research on Wavelet Transform Modulus Maxima and OTSU in Edge Detection

“…Information entropy is used as a measure of uncertainty, that is, the probability of discrete random events occurring. Briefly , the more chaotic things are, the higher the information entropy, and conversely, the lower the information entropy [29,30,31,32,33,34].…”

Optimized Wavelet and Wavelet Packet Transform Techniques for Assessing Crack Behavior in Curved Segments of Arched Beam Bridges Spanning Rivers

“…If n stands for the total number of all sensors distributed in the structure, a total of n-WPERIs can be obtained after performing the wavelet packet decomposition. As the mean values and the standard deviations of the WPERIs are expressed as µ WPERI and σ WPERI , the one-side (1−α) upper confidence limit for the WPERI can be governed via the equation used in [39][40][41][42]:…”

A Feature of Mechanics-Driven Statistical Moments of Wavelet Transform-Processed Dynamic Responses for Damage Detection in Beam-Type Structures

“…The measured dynamic response inevitably introduces noise due to environmental noise or equipment limitations. How to accurately detect the damage locations in noisy (or even high noise) conditions is a key issue that must be addressed in the field of structural damage identification [40]. In order to test the noise immunity of the CWT-SSSM feature-based method, the strain response is superimposed with Gaussian white noise, and the response signal containing noise is used for the damage identification study.…”