Damage detection based on improved particle swarm optimization using vibration data

Kang, Fei; Li, Junjie; Xu, Qingyu

doi:10.1016/j.asoc.2012.03.050

Cited by 160 publications

(65 citation statements)

References 36 publications

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“…The feasibility and efficiency of IEPSO are compared with the basic PSO. Results show that this method is efficient on determining the place and the extents of structure damages [28].…”

Section: Introductionmentioning

confidence: 92%

A New Damage Detection Method: Big Bang-Big Crunch (BB-BC) Algorithm

Tabrizian

Afshari

Amiri

et al. 2013

Shock and Vibration

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Abstract. The present paper aims to explore damage assessment methodology based on the changes in dynamic parameters properties of vibration of a structural system. The finite-element model is used to apply at an element level. Reduction of the element stiffness is considered for structural damage. A procedure for locating and quantifying damaged areas of the structure based on the innovative Big Bang-Big Crunch (BB-BC) optimization method is developed for continuous variable optimization. For verifying the method a number of damage scenarios for simulated structures have been considered. For the purpose of damage location and severity assessment the approach is applied in three examples by using complete and incomplete modal data. The effect of noise on the accuracy of the results is investigated in some cases.A great unbraced frame with a lot of damaged element is considered to prove the ability of proposed method. More over BB-BC optimization method in damage detection is compared with particle swarm optimizer with passive congregation (PSOPC) algorithm.This work shows that BB-BC optimization method is a feasible methodology to detect damage location and severity while introducing numerous advantages compared to referred method.

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“…The feasibility and efficiency of IEPSO are compared with the basic PSO. Results show that this method is efficient on determining the place and the extents of structure damages [28].…”

Section: Introductionmentioning

confidence: 92%

A New Damage Detection Method: Big Bang-Big Crunch (BB-BC) Algorithm

Tabrizian

Afshari

Amiri

et al. 2013

Shock and Vibration

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“…In recent years, there has been significant interest in the use of EAs to solve MOPs, with success of various EAs, such as genetic algorithms, particle swarm optimisation, ant colony optimisation, and artificial bee colony, in diverse application domains [4,18,19,20,21,25,46]. There are two main advantages of EAs in the context of MOPs (usually called EMO algorithms).…”

Section: Evolutionary Multi-objective Optimisationmentioning

confidence: 99%

Multi-objective optimisation for regression testing

Zheng

Hierons

et al. 2016

Information Sciences

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Regression testing is the process of retesting a system after it or its environment has changed. Many techniques aim to find the cheapest subset of the regression test suite that achieves full coverage. More recently, it has been observed that the tester might want to have a range of solutions providing different trade-offs between cost and one or more forms of coverage, this being a multi-objective optimisation problem. This paper further develops the multi-objective agenda by adapting a decomposition-based multi-objective evolutionary algorithm (MOEA/D). Experiments evaluated four approaches: a classic greedy algorithm; non-dominated sorting genetic algorithm II (NSGA-II); MOEA/D with a fixed value for a parameter c; and MOEA/D in which tuning was used to choose the value of c. These used six programs from the SIR repository and one larger program, VoidAuth. In all of the experiments MOEA/D with tuning was the most effective technique. The relative performance of the other techniques varied, although MOEA/D with fixed c outperformed NSGA-II on the larger programs (Space and VoidAuth).

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“…Some previous researches implemented other evolutionary computing techniques such as ant colony optimization [17] and particle swarm optimization [18,19] for the structural damage detection. Moreover, there are a number of works [20][21][22] that formulated the structural damage detection as multiobjective optimization problems having a number of objectives evaluated from the differences between measured and theoretically computed vibration characteristics.…”

Section: Advances In Mechanical Engineeringmentioning

confidence: 99%

Vibration-Based Damage Detection in Beams by Cooperative Coevolutionary Genetic Algorithm

Boonlong

2014

Advances in Mechanical Engineering

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Vibration-based damage detection, a nondestructive method, is based on the fact that vibration characteristics such as natural frequencies and mode shapes of structures are changed when the damage happens. This paper presents cooperative coevolutionary genetic algorithm (CCGA), which is capable for an optimization problem with a large number of decision variables, as the optimizer for the vibration-based damage detection in beams. In the CCGA, a minimized objective function is a numerical indicator of differences between vibration characteristics of the actual damage and those of the anticipated damage. The damage detection in a uniform cross-section cantilever beam, a uniform strength cantilever beam, and a uniform cross-section simply supported beam is used as the test problems. Random noise in the vibration characteristics is also considered in the damage detection. In the simulation analysis, the CCGA provides the superior solutions to those that use standard genetic algorithms presented in previous works, although it uses less numbers of the generated solutions in solution search. The simulation results reveal that the CCGA can efficiently identify the occurred damage in beams for all test problems including the damage detection in a beam with a large number of divided elements such as 300 elements.

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Damage detection based on improved particle swarm optimization using vibration data

Cited by 160 publications

References 36 publications

A New Damage Detection Method: Big Bang-Big Crunch (BB-BC) Algorithm

A New Damage Detection Method: Big Bang-Big Crunch (BB-BC) Algorithm

Multi-objective optimisation for regression testing

Vibration-Based Damage Detection in Beams by Cooperative Coevolutionary Genetic Algorithm

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