Dynamic Finite Element Model Updating Using Simulated Annealing and Genetic Algorithms

Levin, RI; Lieven, Nicholas A J

doi:10.1006/mssp.1996.0136

Cited by 214 publications

(117 citation statements)

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“…These heuristic stochastic search techniques seem to be a promising alternative to traditional approaches. The SA and GA methods have been implemented to accurately describe the dynamic behavior of structures (Levin and Lieven (1998)). Cunha et al (1999) used GAs to identify the elastic constants of composite materials.…”

Section: Introductionmentioning

confidence: 99%

Identification of Structural Systems Using Particle Swarm Optimization

Xue

Tang

Zhou

2009

Journal of Asian Architecture and Building Engineering

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Particle swarm optimization (PSO) is a new heuristic method that has yielded promising results for solving complex optimization problems. Its advantages are a simple structure, ease of use, quality of solution, and robustness. This paper utilizes the PSO algorithm for parameter estimation of structural systems, which could be formulated as a multi-modal numerical optimization problem with high dimension. Simulation results for identifying the parameters of multiple degree-of-freedom (DOF) linear and nonlinear structural systems are presented to demonstrate the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Identification of Structural Systems Using Particle Swarm Optimization

Xue

Tang

Zhou

2009

Journal of Asian Architecture and Building Engineering

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“…Evolutionary algorithms (EAs) are part of GOAs, which deal with mechanisms inspired by biological evolution such as cross over, mutation and selection. In the context of model updating and damage detection, several studies have been conducted using different objective functions with GOAs such as genetic algorithm (GA) [15][16][17][18], simulated annealing (SA) [19] and particle swarm optimization (PSO) [20]. GA was applied to a simulated beam, as well as a frame structure using frequencies and MAC-based objective functions to efficiently detect damage [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, multiobjective multicriteria GA was studied for damage detection and estimation using MAC and flexibility-related objective functions [18]. A numerical beam and wing plate experimental structure was also investigated using both GA and SA with the frequency response function (FRF)-based objective function [19], and a new bended SA algorithm was also proposed for improvement of the results. Damage detection in thin plates based on modal data using a PSO-GA-based approach has been presented by Vaez and Fallah [22].…”

Section: Introductionmentioning

confidence: 99%

Structural Damage Detection with Different Objective Functions in Noisy Conditions Using an Evolutionary Algorithm

et al. 2017

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Dynamic properties such as natural frequencies and mode shapes are directly affected by damage in structures. In this paper, changes in natural frequencies and mode shapes were used as the input to various objective functions for damage detection. Objective functions related to natural frequencies, mode shapes, modal flexibility and modal strain energy have been used, and their performances have been analyzed in varying noise conditions. Three beams were analyzed: two of which were simulated beams with single and multiple damage scenarios and one was an experimental beam. In order to do this, SAP 2000 (v14, Computers and Structures Inc., Berkeley, CA, United States, 2009) is linked with MATLAB (r2015, The MathWorks, Inc., Natick, MA, United States, 2015). The genetic algorithm (GA), an evolutionary algorithm (EA), was used to update the damaged structure for damage detection. Due to the degradation of the performance of objective functions in varying noisy conditions, a modified objective function based on the concept of regularization has been proposed, which can be effectively used in combination with EA. All three beams were used to validate the proposed procedure. It has been found that the modified objective function gives better results even in noisy and actual experimental conditions.

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“…There are two approaches used in finite-element-model updating: direct methods and iterative methods. 26 Direct methods, which use the modal properties, are computationally efficient to implement and reproduce the measured modal data exactly, but they do not take into account the physical parameters that are updated. Iterative procedures use changes in physical parameters to update the finite element models and, thereby, generate models that are physically realistic.…”

Section: Introductionmentioning

confidence: 99%

“…32 Among iterative procedures, many stochastic optimization methods have been used in model updating and damage detection. [26][27][28][29][30][31][32][33] Levin and Lieven 26 introduced the genetic algorithm (GA) and simulated annealing for finite-element-model updating. Marwala [34][35][36] successfully applied three separate genetic algorithms to minimize the distance between the measured data and the finite-element predicted data.…”

Section: Introductionmentioning

confidence: 99%

Application of Improved Accelerated Random Search Algorithm for Structural Damage Detection

Boubakir¹,

Touat²,

Kharoubi³

et al. 2017

IJAV

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Finite element (FE) model updating technique belongs to the class of inverse problems in classical mechanics. According to the continuum damage mechanics, damage is represented by a reduction factor of the element stiffness and mass. The objective of the optimization problem is to minimize the difference between measured and numerical FE vibration data. In this study a new method is presented for structural damage detection called Improved Modified Accelerated Random Search algorithm (IMARS). The algorithm uses model updating procedure to detect damages in a decoupled fashion. First, detecting the location and the number of damaged elements is evaluated by multi-run process. Knowing damage number, the quantification step is then applied using simple computing procedure. The effectiveness of the algorithm is first tested on mathematical benchmark functions. The algorithm is then applied in damage detection of 2D beam structure and 2D truss structure. These two cases have different boundary conditions and different damage scenarios. The simulated experimental modal data have been taken as reference values. A real cantilever beam with experimental modal parameters is used to validate the proposed method for real single and double damages. Results show that the proposed method is accurate and robust in structural damage identification.

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Dynamic Finite Element Model Updating Using Simulated Annealing and Genetic Algorithms

Cited by 214 publications

References 0 publications

Identification of Structural Systems Using Particle Swarm Optimization

Identification of Structural Systems Using Particle Swarm Optimization

Structural Damage Detection with Different Objective Functions in Noisy Conditions Using an Evolutionary Algorithm

Application of Improved Accelerated Random Search Algorithm for Structural Damage Detection

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