Damage Identification of Simply-Supported Beams Using Dynamic Analysis: Experimental and Theoretical Aspects

2009 ◽  
Author(s):  
F. Garcés ◽  
P. Garciia ◽  
C. Genatios ◽  
A. Mébarki ◽  
M. Lafuente
Keyword(s):  
Dynamic Analysis ◽  
Damage Identification ◽  
Simply Supported

A Novel Two-Stage Structural Damage Identification Method Based on Superposition of Modal Flexibility Curvature and Whale Optimization Algorithm

2021 ◽  
pp. 2150169
Author(s):  
Minshui Huang ◽  
Xihao Cheng ◽  
Zhigang Zhu ◽  
Jin Luo ◽  
Jianfeng Gu
Keyword(s):  
Optimization Algorithm ◽  
Damage Identification ◽  
Whale Optimization Algorithm ◽  
Two Stage ◽  
Simply Supported ◽  
Damage Location ◽  
Flexibility Matrix ◽  
Modal Flexibility ◽  
Whale Optimization ◽  
The Impact

A novel two-stage method is proposed to properly identify the location and severity of damage in plate structures. In the first stage, a superposition of modal flexibility curvature (SMFC) is adopted to locate the damage accurately, and the identification index of modal flexibility matrix is improved. The low-order modal parameters are used and a new column matrix is formed based on the modal flexibility matrix before and after the structure is damaged. The difference of modal flexibility matrix is obtained, which is used as a damage identification index. Meanwhile, based on SMFC, a method of weakening the “vicinity effect” is proposed to eliminate the impact of the surrounding elements to the damaged elements when damage identification is carried out for the plate-type structure. In the second stage, the objective function based on the flexibility matrix is constructed, and according to the damage location identified in the first stage, the actual damage severity is determined by the enhanced whale optimization algorithm (EWOA). In addition, the effects of 3% and 10% noise on damage location and severity estimation are also studied. By taking a simply supported beam and a four-side simply supported plate as examples, the results show that the method can accurately estimate the damage location and quantify the damage severity without noise. When considering noise, the increase of noise level will not affect the assessment of damage location, but the error of quantifying damage severity will increase. In addition, damage identification of a steel-concrete composite bridge (I-40 Bridge) under four damage cases is carried out, and the results show that the modified method can evaluate the damage location and quantify 5%–92% of the damage severity.

Damage Identification for Simply-Supported Bridge Based on SVM Optimized by PSO (PSO-SVM)

2011 ◽  
Vol 80-81 ◽  
pp. 490-494 ◽  
Author(s):  
Han Bing Liu ◽  
Yu Bo Jiao ◽  
Ya Feng Gong ◽  
Hai Peng Bi ◽  
Yan Yi Sun
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Damage Identification ◽  
Fitness Function ◽  
Support Vector ◽  
Detection Accuracy ◽  
Damage Localization ◽  
Swarm Optimization ◽  
Simply Supported ◽  
Beam Bridge

A support vector machine (SVM) optimized by particle swarm optimization (PSO)-based damage identification method is proposed in this paper. The classification accuracy of the damage localization and the detection accuracy of severity are used as the fitness function, respectively. The best and can be obtained through velocity and position updating of PSO. A simply supported beam bridge with five girders is provided as numerical example, damage cases with single and multiple suspicious damage elements are established to verify the feasibility of the proposed method. Numerical results indicate that the SVM optimized by PSO method can effectively identify the damage locations and severity.

APPLICATION OF GENETIC ALGORITHM-SUPPORT VECTOR MACHINE (GA-SVM) FOR DAMAGE IDENTIFICATION OF BRIDGE

2011 ◽  
Vol 10 (04) ◽  
pp. 383-397 ◽  
Author(s):  
HAN-BING LIU ◽  
YU-BO JIAO
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Damage Identification ◽  
Back Propagation ◽  
Support Vector ◽  
Model Parameters ◽  
Simply Supported ◽  
Relative Errors ◽  
Shape Ratio ◽  
Crossover And Mutation

A support vector machine (SVM) optimized by genetic algorithm (GA)-based damage identification method is proposed in this paper. The best kernel parameters are obtained by GA from selection, crossover and mutation, and utilized as the model parameters of SVM. The combined vector of mode shape ratio and frequency rate is used as the input variable. A numerical example for a simply supported bridge with five girders is provided to verify the feasibility of the method. Numerical simulation shows that the maximal relative errors of GA-SVM for the damage identification of single, two and three suspicious damaged elements is 1.84%. Meanwhile, comparative analyzes between GA-SVM and radical basis function (RBF), back propagation networks optimized by GA (GA-BP) were conducted, the maximal relative errors of RBF and GA-BP are 6.91% and 5.52%, respectively. It indicates that GA-SVM can assess the damage conditions with better accuracy.

Correlation-Based Damage Identification and Quantification Using Modal Kinetic Energy Change

2020 ◽  
Vol 20 (10) ◽  
pp. 2042007
Author(s):  
J. T. Joseph ◽  
T. H. T. Chan ◽  
K.-D. Nguyen
Keyword(s):  
Kinetic Energy ◽  
Damage Detection ◽  
Damage Identification ◽  
Numerical Study ◽  
Traditional Approach ◽  
Mass Change ◽  
Algorithm Optimization ◽  
Damage Scenarios ◽  
Simply Supported ◽  
Modal Properties

Many existing damage identification or quantification methods can be employed only if the internal and external mass changes are negligible when tested at two different states of a structure. This paper presents a new Modal Kinetic Energy (MKE)-based method to detect and quantify damage using modal properties of structures, which can be employed even in situations when mass change is not more than a certain extent. A new damage sensitivity parameter has been developed using measured modal characteristics of baseline structure. The MKE change (MKEC) concept is then employed to locate damage and to estimate relative perturbation at each element. The relative damage extent vector is estimated by searching the best correlation between the analytical and experimental MKEC vectors with the help of genetic algorithm optimization tool. The extent of damage is calculated after computing damage scaling coefficient using measured eigenvalue change vector. A numerical study is carried out on a simply supported single span beam to confirm its performance under various test conditions. The robustness of the proposed MKE method and the significance of mass variation in the damage detection approach are evaluated by comparing the damage quantification results with a traditional approach. Finally, the proposed damage detection method is applied on a two-span simply supported beam for single and multiple damage scenarios by extracting the modal properties experimentally. The results revealed that the proposed approach is capable of detecting and estimating single and multiple damages with reasonable accuracy even in moderate noise contaminated and mass change environments.

Dynamic analysis of a simply supported beam with LaSMP patches

2019 ◽  
Vol 230 (6) ◽  
pp. 2031-2042 ◽  
Author(s):  
Yunze Yang ◽  
Jihai Yuan ◽  
Mu Fan
Keyword(s):  
Dynamic Analysis ◽  
Simply Supported Beam ◽  
Simply Supported
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