A Modified Wavelet Energy Rate Based Damage Identification Method for Steel Bridges

Abstract

Strain is sensitive to damage, especially in steel structures. However, a traditional strain gauge does not t bridge damage identi cation because it only provides the strain information of the point, where it is set up. While traditional strain gauges su er from drawbacks, a long-gage FBG strain sensor is capable of providing the strain information of a certain range, in which all the damage information within the sensing range can be re ected by the strain information provided by FBG sensors. The wavelet transform is a new way to analyze the signals, capable of providing multiple levels of details and approximations of the signal. In this paper, a wavelet packet transform-based damage identi cation is proposed to identify the steel bridge damage numerically and with experimentally to validate the proposed method. The strain data obtained via long-gage FBG strain sensors are transformed into a modi ed wavelet packet energy rate index rst to identify the location and severity of damage. The results of numerical simulations show that the proposed damage index is a good candidate that is capable of identifying both the location and severity of damage under noise e ect.

Real‐time system identification using hierarchical interhealing model classes

“…The deck has a uniform cross section as shown in Figure 2b. The area is 5.64 m 2 , and the weak-axis second moment of inertia is 3.58 m 4 . On the other hand, the two piers have the same circular cross-section with radius 1 m and moment of inertia 0.785 m 4 .…”

“…The area is 5.64 m 2 , and the weak-axis second moment of inertia is 3.58 m 4 . On the other hand, the two piers have the same circular cross-section with radius 1 m and moment of inertia 0.785 m 4 . For the entire bridge, the mass density is 2,500 kg/m 3 , and the elasticity modulus is 30 GPa.…”

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.…”

A Comparison of Three Different Methods for the Identification of Hysterically Degrading Structures Using BWBN Model

“…A toolbox for computing the parameters of BWBN hysteresis model using multi-objective optimization evolutionary algorithms was also developed by SourceForge, an Open Source community (Bouc Wen Baber Noori Model of Hysteresis, Source Forge). Foliente showed Bouc-Wen-Baber-Noori (BWBN) model could produce previously observed inelastic behavior of wood joints and structural systems using BWBN smooth hysteresis model (Foliente, 1995;Zhao et al, 2017a,b;Noori et al, 2018). Deb et al developed a toolbox that identifies structural parameters of Bouc-Wen-Baber-Noori hysteresis model through a noval multi-objective optimization evolutionary algorithms (MOBEAs) (Deb et al, 2002;Deb, 2013).…”