Structural damage identification based on cuckoo search algorithm

Xu, H.J.; Liu, Jiankang; Lu, Zhong-Rong

doi:10.1177/1369433216630128

Cited by 46 publications

(19 citation statements)

References 32 publications

(25 reference statements)

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“…In this condition, the host birds will destroy the eggs or just abandon the nests. [42][43][44] The CS is used in a large range of real-world optimization problem, but it is seldom used for the damage identi¯cation, Xu et al 45,46 introduced a new formula in the CS to improve the convergence rate on discovering probability process, adopted tournament selection strategy to enhance global search ability of the certain algorithm, and carried out damage identi¯cation with a numerically simply supported beam and a laboratory model. In the CS, L evy°i ght is highly random, which enables the current searching range to be moved to a new¯eld.…”

Section: Hybrid Algorithm Using Pso and Csmentioning

confidence: 99%

Structural Damage Identification Under Temperature Variations Based on PSO–CS Hybrid Algorithm

Huang

Cheng

Zhang

et al. 2019

Int. J. Str. Stab. Dyn.

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Using the variations in parameters to detect structural damages has been widely used in damage identi¯cation of structures. When exposed to varying temperatures, not only the displacements and stresses of a structure will change, but also the elastic modulus of the materials, such as concrete and steel, of which the structure is made. Since the variation in elastic modulus will result in the variation of the sti®ness of the structure, a damage identi¯cation method without considering the temperature e®ects is, in principle, unacceptable. In this study, a damage identi¯cation method using the particle swarm optimization combined with the cuckoo search (PSO-CS) under the noise and temperature environment is proposed. First, the temperature variations are combined with the elastic modulus variation for addressing the temperature e®ects in¯nite element model. Second, a PSO-CS hybrid algorithm is adopted, which applies the updated mechanism of PSO in CS. Third, objective functions comprised of di®erent modal messages with diverse weight coe±cients are constructed for the damage identi¯cation and validated by numerical analysis of a simply supported beam. The results show that the performance of the PSO-CS is better than either PSO or CS individually. Finally, the PSO-CS is applied to the damage identi¯cation of ASCE Benchmark frame, for which the results indicate a satisfactory accuracy of the e®ectiveness of the proposed scheme.

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Section: Hybrid Algorithm Using Pso and Csmentioning

confidence: 99%

Structural Damage Identification Under Temperature Variations Based on PSO–CS Hybrid Algorithm

Huang

Cheng

Zhang

et al. 2019

Int. J. Str. Stab. Dyn.

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“…Also, GA tends to be trapped into local optimal, which causes the low inaccuracy of damage identification. Aiming to these drawbacks, some other optimization tools, such as Particle Swarm Optimization (PSO) [ 18 ], Cuckoo Search (CS) [ 19 ], Jaya algorithm [ 20 ], Artificial Fish Swarm Optimization (AFSWO) [ 21 ], and Artificial Bee Colony algorithm [ 22 ], were adopted in previous studies as well. Furthermore, based on the hybrid mechanism of different algorithms, better optimization performance can be achieved.…”

Section: Introductionmentioning

confidence: 99%

Bearing Damage Detection of a Reinforced Concrete Plate Based on Sensitivity Analysis and Chaotic Moth-Flame-Invasive Weed Optimization

Huang

Lei

2020

Sensors

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This article proposes a novel damage detection method based on the sensitivity analysis and chaotic moth-flame-invasive weed optimization (CMF-IWO), which is utilized to simultaneously identify the damage of structural elements and bearings. First, the sensitivity coefficients of eigenvalues to the damage factors of structural elements and bearings are deduced, the regularization technology is used to solve the problem of equation undetermined, meanwhile, the modal strain energy-based index is utilized to detect the damage locations, and the regularization objective function is constructed to quantify the damage severity. Then, for the subsequent procedure of damage detection, CMF-IWO is proposed based on moth-flame optimization and invasive weed optimization as well as chaos theory, reverse learning, and evolutional strategy. The optimization effectiveness of the hybrid algorithm is verified by five benchmark functions and a damage identification numerical example of a simply supported beam; the results demonstrate it is of great global search ability and higher convergence efficiency. After that, a numerical example of an 8-span continuous beam and an experimental reinforced concrete plate are both adopted to evaluate the proposed damage identification method. The results of the numerical example indicate that the proposed method can locate and quantify the damage of structural elements and bearings with high accuracy. Furthermore, the outcomes of the experimental example show that despite the existence of some errors and uncertain factors, the method still obtains an acceptable result. Generally speaking, the proposed method is proved that it is of good feasibility.

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“…To solve the optimization problems, intelligent algorithms are widely introduced [18], [19]. But these swarm intelligence techniques both have common failing on slow convergence, and some of them may fall into local optimization easily [20].…”

Section: Introductionmentioning

confidence: 99%

Damage Identification of Bridge System Based on a Hybrid Algorithm

Xin¹,

Hong²,

Zhou³

et al. 2019

International Journal of Robotics and Automation

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Structural damage identification is fundamentally important for ensuring bridge safety and improving the pertinence of maintenance work. This paper proposes a new hybrid method for structural damage identification, which integrates the stepwise regression model based on uniform design and the intelligent optimization algorithm based on chaotic search. A numerical example is studied to investigate the efficiency and correctness of the proposed method. The simulation results demonstrated that the hybrid model can accurately identify the damage location and degree of the structure, the identification accuracy of the proposed method is satisfactory with 7.332% of mean relative percentage error and 9.878% of maximum relative percentage error. This paper provides a new way for damage identification of bridge structures.

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Structural damage identification based on cuckoo search algorithm

Cited by 46 publications

References 32 publications

Structural Damage Identification Under Temperature Variations Based on PSO–CS Hybrid Algorithm

Structural Damage Identification Under Temperature Variations Based on PSO–CS Hybrid Algorithm

Bearing Damage Detection of a Reinforced Concrete Plate Based on Sensitivity Analysis and Chaotic Moth-Flame-Invasive Weed Optimization

Damage Identification of Bridge System Based on a Hybrid Algorithm

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