scholarly journals Study of Structural Damage Detection with Multi-objective Function Genetic Algorithms

2011 ◽  
Vol 12 ◽  
pp. 80-86 ◽  
Author(s):  
Hui Liu ◽  
Kegui Xin ◽  
Quanquan Qi
Keyword(s):  
Genetic Algorithms ◽  
Damage Detection ◽  
Objective Function ◽  
Structural Damage ◽  
Structural Damage Detection ◽  
Multi Objective

Flexibility-Based Objective Functions for Constrained Optimization Problems on Structural Damage Detection

2011 ◽  
Vol 186 ◽  
pp. 383-387 ◽  
Author(s):  
Xi Chen ◽  
Ling Yu
Keyword(s):  
Damage Detection ◽  
Constrained Optimization ◽  
Objective Function ◽  
Structural Damage ◽  
Optimization Problems ◽  
Objective Functions ◽  
Structural Damage Detection ◽  
Constrained Optimization Problems ◽  
Modal Assurance Criterion ◽  
Damage Scenarios

Based on concepts of structural modal flexibility and modal assurance criterion (MAC), a new objective function is defined and studied for constrained optimization problems (COP) on structural damage detection (SDD) in this paper. Compared with traditionally objective function, which is defined based on natural frequencies and MAC, effect of objective functions on robustness of SDD calculation is evaluated through numerical simulation of a 2-storey rigid frame. Structural damages are identified by solving the COP on SDD based on an improved particle swarm optimization (IPSO) algorithm. Weak and multiple damage scenarios are mainly considered in various noise conditions. Some illustrated results show that the newly defined objective function is better than the traditional ones. It can be used to identify the damage locations but also to quantify the severity of weak and multiple damages in measurement noise conditions.

Genetic Algorithms in Structural Identification and Damage Detection

2007 ◽  
pp. 316-341 ◽  
Author(s):  
Chan Koh
Keyword(s):  
Genetic Algorithms ◽  
Damage Detection ◽  
Structural Damage ◽  
Structural Identification ◽  
Search Space ◽  
Degree Of Freedom ◽  
Structural Damage Detection ◽  
Space Reduction ◽  
Numerical Studies ◽  
Before And After

Genetic algorithms (GA) have proved to be a robust, efficient search technique for many problems. In this chapter, the latest developments by the authors in the area of structural identification and structural damage detection using genetic algorithms are presented. A GA strategy involving a search space reduction method (SSRM) using a modified genetic algorithm based on migration and artificial selection (MGAMAS) is first used to identify structural properties in multiple degree-of-freedom systems. The SSRM is then incorporated in a structural damage detection strategy using response measurements both before and after damage has taken place. Numerical studies on 10 and 20 degree-of-freedom systems show that a small damage of only 2.5% can be accurately and consistently identified from incomplete acceleration measurements in the presence of 5% input and output noise.

Structural Damage Detection by Genetic Algorithms

AIAA Journal ◽  
2002 ◽  
Vol 40 (7) ◽  
pp. 1395-1401 ◽  
Author(s):  
Kaazem Moslem ◽  
Ramin Nafaspour
Keyword(s):  
Genetic Algorithms ◽  
Damage Detection ◽  
Structural Damage ◽  
Structural Damage Detection

A hybrid ant lion optimizer with improved Nelder–Mead algorithm for structural damage detection by improving weighted trace lasso regularization

2019 ◽  
Vol 23 (3) ◽  
pp. 468-484 ◽  
Author(s):  
Chengbin Chen ◽  
Ling Yu
Keyword(s):  
Damage Detection ◽  
Objective Function ◽  
Hybrid Algorithm ◽  
Structural Damage ◽  
Heuristic Algorithms ◽  
Structural Damage Detection ◽  
Ant Lion Optimizer ◽  
Computing Performance ◽  
Ant Lion ◽  
Nelder Mead Algorithm

Structural damage detection is the kernel technique in deploying structural health monitoring. The structural damage–detection technique using heuristic algorithms has been developed at an astounding pace over the past years. However, some existing structural damage–detection methods are prone to easily fall into the local optimum and to be unstable when they are applied to complex structures. In order to make full use of advantages of heuristic algorithms and overcome abovementioned shortcomings, a hybrid algorithm, which combines the ant lion optimizer with an improved Nelder–Mead algorithm, is proposed to solve the constrained optimization problem of complex structural damage detection. First, an objective function is established for damage identification using structural modal parameters, that is, frequencies and mode shapes. The solution to the objective function is accurately attained by a newly improved weighted trace lasso which can improve the computing performance and stability of procedure and reduce randomness of weighted coefficients. After assessing the computing performance of the proposed hybrid algorithm using three classical mathematical benchmark functions, two structural damage–detection numerical simulations and a laboratory verification are then conducted to fully assess the structural damage–detection capability of the proposed method. Meanwhile, the equivalent element stiffness-reduction model is introduced to estimate the real damage severities of cracks which are created in laboratory structures and to compare with the structural damage–detection results by the proposed method. The illustrated results show that the proposed hybrid algorithm can locate damage and quantify damage severity more accurately and stably with a good robustness to noise.

Structural damage detection by genetic algorithms

AIAA Journal ◽  
2002 ◽  
Vol 40 ◽  
pp. 1395-1401 ◽  
Author(s):  
K. Moslem ◽  
R. Nafaspour
Keyword(s):  
Genetic Algorithms ◽  
Damage Detection ◽  
Structural Damage ◽  
Structural Damage Detection
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