Mode shape-based damage identification for a reinforced concrete beam using wavelet coefficient differences and multiresolution analysis

Abstract

In this paper, the structural mode shapes extracted from the finite element model of a simply supported reinforced concrete beam are employed for damage identification using different types of wavelets. To start with, the parity of signals, wavelets, and their convolution, that is, wavelet transform properties, are verified. In light of the mathematical modeling complexity of modal frequency, which relates to the localization and quantification of damage in the reinforced concrete beam, the maximum curves based on multiresolution wavelet transform coefficient differences and the corresponding theoretical assumptions are described and analyzed. It is concluded that the maximum curve reaches a peak value at a specific scale for a specific case, based upon which, a new mode shape based algorithm and damage index are proposed for damage identification. The accuracy of localization as well as the sensitivity of quantification is further discussed.

Applying Neural Networks for Tire Pressure Monitoring Systems

“…An artificial neural network (ANN) is a machine learning algorithm used to solve advanced nonlinear problems such as handwriting or speech recognition [36][37][38][39][40][41][42][43][44]. Neural networks connect computational nodes together to form a singular network, where each computational node is performing a calculation on its input and outputting the result to all outgoing connections.…”

Identification of cracks in an Euler–Bernoulli beam using Bayesian inference and closed-form solution of vibration modes

“…A series of theoretical and analytical methods have been developed to describe the static and dynamic deformation of beams, such as Euler–Bernoulli, Timoshenko, and Reddy beam theories. 41–45 Cracks in a beam are usually modeled as massless rotational springs that connect the beam sections. 46 Based on this commonly used model, the closed-form solution is also given using generalized functions.…”

Bayesian estimation approach based on modified SCAM algorithm and its application in structural damage identification

“…The deterministic methods treat the characteristic parameters of the damage as a deterministic quantity, study the deterministic mapping relation between the characteristic parameters of the damage and the damage itself, and identify the damage through deterministic calculations and reasoning based on the mapping relation 1 . Commonly used deterministic methods include the following: (1) static and dynamic fingerprint methods based on natural frequency, 2 mode shape, 3 modal strain energy, 4,5 and modal curvature 6 ; (2) damage detection methods based on model updating 7,8 ; (3) identification methods based on measured time‐domain signals, including the Hilbert‐Huang transform, 9 and wavelet analysis 10,11 ; and (4) artificial neural network‐based identification methods 12,13 . However, in actual engineering projects, there are uncertain factors, such as complex properties of the structure itself and the impact of ambient noise in the damage problems 14 .…”