A robust optimization for damage detection using multiobjective genetic algorithm, neural network and fuzzy decision making

Alexandrino, Patrícia da Silva Lopes; Gomes, Guilherme Ferreira; Cunha, Sebastião Simões da

doi:10.1080/17415977.2019.1583225

Cited by 48 publications

(10 citation statements)

References 22 publications

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“…Its outstanding feature is that it contains steps very similar to biological genetics and evolution [ 20 ]. The main advantages of GA are simple, universal, strong robustness, suitable for parallel distributed processing and wide application range [ 21 ]. The search performance of genetic algorithm is mainly realized by three operators: selection, crossover, and mutation, especially the mutation operator.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Bank Credit Risk Evaluation Model Based on BP Neural Network

Wang

2022

Computational Intelligence and Neuroscience

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Commercial banks are of great value to social and economic development. Therefore, how to accurately evaluate their credit risk and establish a credit risk prevention system has important theoretical and practical significance. This paper combines BP neural network with a mutation genetic algorithm, focuses on the credit risk assessment of commercial banks, applies neural network as the main modeling tool of the credit risk assessment of commercial banks, and uses the mutation genetic algorithm to optimize the main parameter combination of neural network, so as to give better play to the efficiency of neural network. After verification of various evaluation models, the accuracy of the evaluation model designed in this paper is more than 65%, while the acceptability of the evaluation results optimized by the mutation genetic algorithm is more than 85%. Compared with the accuracy of about 50% of the traditional credit scoring method, the accuracy of the credit risk evaluation using neural network technology is improved by more than 10%. It is proved that the performance of the optimized algorithm is better than that of the traditional neural network algorithm. It has important theoretical and practical significance for the establishment of the credit risk prevention system of commercial banks.

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Section: Related Workmentioning

confidence: 99%

Analysis of Bank Credit Risk Evaluation Model Based on BP Neural Network

Wang

2022

Computational Intelligence and Neuroscience

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“…Optimization can be defined as a process of searching for the best solution within a set of possible solutions (Alexandrino et al, 2019). Optimization objectives can be diverse, such as minimizing energy consumption and costs, and maximizing profit, production, performance and efficiency.…”

Section: Lichtenberg Algorithmmentioning

confidence: 99%

Lichtenberg optimization algorithm applied to crack tip identification in thin plate-like structures

Pereira

Chuman

Cunha

et al. 2020

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Purpose This study aims to develop a numerical identification and characterization of crack propagation through the use of a new optimization metaheuristics called Lichtenberg optimization. Design/methodology/approach The damage-identification problem is treated as an inverse problem, which combines finite element methods with intelligent computational methods to obtain the best possible response. To optimize the objectives, the Lichtenberg algorithm is applied, which includes concepts of random cluster growth in nature. Findings The simulations show that it is possible to determine the Lichtenberg spectrum algorithm a part of the structure to be removed and replaced in this case to stop the propagation. Originality/value The results show a very good crack identification in plates-like structures using the Lichtenberg algorithm (LA) based only in strain fields. Although many studies have reported on damage-identification-based optimization methods, very few have focused on the crack tip modeling and LA as the main solver.

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“…Deterministic approaches only update the parameter of a single model using one set of measured data [8], yielding a unique solution. In the scope of deterministic approaches, swarm intelligence optimization methods have been widely used to seek the optimal parameters for structural damage identification-see, for example, the whale optimization algorithm and its variants [11][12][13], bat optimization algorithm and its modifications [14,15], particle swarm optimization [16,17], moth-flame optimization [18,19] and genetic algorithm [20,21]. However, civil engineering applications by deterministic approaches are still limited in practice due to the unavailability of uncertainties induced by measurement noise and modeling errors.…”

Section: Introductionmentioning

confidence: 99%

Stiffness Modification-Based Bayesian Finite Element Model Updating to Solve Coupling Effect of Structural Parameters: Formulations

Zeng

Kim

2021

Applied Sciences

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The Bayesian model updating approach (BMUA) benefits from identifying the most probable values of structural parameters and providing uncertainty quantification. However, the traditional BMUA is often used to update stiffness only with the assumption of well-known mass, which allows unidentifiable cases induced by the coupling effect of mass and stiffness to be circumvented and may not be optimal for structures experiencing damages in both mass and stiffness. In this paper, the new BMUA tailored to estimating both mass and stiffness is presented by using two measurement states (original and modified systems). A new eigenequation with a stiffness-modified system is formulated to address the coupling effect of mass and stiffness. The posterior function is treated using an asymptotic approximation method, giving the new objective functions with stiffness modification. Analytical formulations of modal parameters and structural parameters are then derived by a linear optimization method. In addition, the covariance matrix of uncertain parameters is determined by the inverse of the Hessian matrix of the objective function. The performance of the proposed BMUA is evaluated through two numerical examples in this study; a probabilistic damage estimation is also implemented. The results show the proposed BMUA is superior to the traditional one in mass and stiffness updating.

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A robust optimization for damage detection using multiobjective genetic algorithm, neural network and fuzzy decision making

Cited by 48 publications

References 22 publications

Analysis of Bank Credit Risk Evaluation Model Based on BP Neural Network

Analysis of Bank Credit Risk Evaluation Model Based on BP Neural Network

Lichtenberg optimization algorithm applied to crack tip identification in thin plate-like structures

Stiffness Modification-Based Bayesian Finite Element Model Updating to Solve Coupling Effect of Structural Parameters: Formulations

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