Structural damage identification based on substructure method and improved whale optimization algorithm

Huang, Minshui; Cheng, Xihao; Lei, Yongzhi

doi:10.1007/s13349-020-00456-7

Cited by 42 publications

(19 citation statements)

References 35 publications

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“…By solving Equations ( 23), (28), and ( 31)-(33), the vector γ (0) -γ (4) can be obtained. Similarly, the higher order of expansion coefficient vectors can be achieved.…”

Section: The Stochastic Icmcm Methodsmentioning

confidence: 99%

“…At present, the updating of FE model is mainly to adjust the stiffness matrix and mass matrix in the FE model, or to update the physical parameters in these matrices, so that the updated model can be used to reasonably predict the structural real mechanical behaviors. By now, many deterministic model updating methods have been developed, which mainly include optimization based model updating methods [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ], or FE based model updating methods [ 8 , 9 , 10 , 11 , 12 ], Bayesian model updating methods and recently developed model updating methods based on artificial intelligence [ 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…in Equation (32), Equation (33) and Equation (36) are replaced by C (0) E (0) and C (1) E (1) , respectively, the vectors γ (0) -γ (4) and γ may be recursively obtained by solving Equations ( 21), ( 26), ( 29), (32), (33) and (36). In this way, the proposed method will degenerate into the HPG-CMCM method.…”

mentioning

confidence: 99%

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A New Stochastic Model Updating Method Based on Improved Cross-Model Cross-Mode Technique

Chen

Huang

Tee

et al. 2021

Sensors

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This paper proposes a new stochastic model updating method to update structural models based on the improved cross-model cross-mode (ICMCM) technique. This new method combines the stochastic hybrid perturbation-Galerkin method with the ICMCM method to solve the model updating problems with limited measurement data and uncertain measurement errors. First, using the ICMCM technique, a new stochastic model updating equation with an updated coefficient vector is established by considering the uncertain measured modal data. Then, the stochastic model updating equation is solved by the stochastic hybrid perturbation-Galerkin method so as to obtain the random updated coefficient vector. Following that, the statistical characteristics of the updated coefficients can be determined. Numerical results of a continuous beam show that the proposed method can effectively cope with relatively large uncertainty in measured data, and the computational efficiency of this new method is several orders of magnitude higher than that of the Monte Carlo simulation method. When considering the rank deficiency, the proposed stochastic ICMCM method can achieve more accurate updating results compared with the cross-model cross-mode (CMCM) method. An experimental example shows that the new method can effectively update the structural stiffness and mass, and the statistics of the frequencies of the updated model are consistent with the measured results, which ensures that the updated coefficients are of practical significance.

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“…By solving Equations ( 23), (28), and ( 31)-(33), the vector γ (0) -γ (4) can be obtained. Similarly, the higher order of expansion coefficient vectors can be achieved.…”

Section: The Stochastic Icmcm Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Stochastic Model Updating Method Based on Improved Cross-Model Cross-Mode Technique

Chen

Huang

Tee

et al. 2021

Sensors

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“…Roumaissa et al [23] compared the accuracy of the GA algorithm and Bat algorithm through the objective function composed of a measured frequency response function and calculated frequency response function. The substructure, strain energy and SIOA were used to identify the damage of a large spatial structure [24,25], in which the substructure was used to divide the large spatial structure, the strain energy was used to locate the structural damage, and SIOA was used to quantify the structural damage. It is worth noting that in addition to the above modal parameters, the objective function can also be constructed by using the structural response data.…”

Section: Introductionmentioning

confidence: 99%

Structural Damage Identification Using a Modified Directional Bat Algorithm

Liu

2021

Applied Sciences

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Bat algorithm (BA) has been widely used to solve optimization problems in different fields. However, there are still some shortcomings of standard BA, such as premature convergence and lack of diversity. To solve this problem, a modified directional bat algorithm (MDBA) is proposed in this paper. Based on the directional bat algorithm (DBA), the individual optimal updating mechanism is employed to update a bat’s position by using its own optimal solution. Then, an elimination strategy is introduced to increase the diversity of the population, in which individuals with poor fitness values are eliminated, and new individuals are randomly generated. The proposed algorithm is applied to the structural damage identification and to an objective function composed of the actual modal information and the calculated modal information. Finally, the proposed MDBA is used to solve the damage detection of a beam-type bridge and a truss-type bridge, and the results are compared with those of other swarm intelligence algorithms and other variants of BA. The results show that in the case of the same small population number and few iterations, MDBA has more accurate identification and better convergence than other algorithms. Moreover, the study on anti-noise performance of the MDBA shows that the maximum relative error is only 5.64% at 5% noise level in the beam-type bridge, and 6.53% at 3% noise in the truss-type bridge, which shows good robustness.

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“…Derived from the bubble-net hunting behavior of humpback whales, the whale optimization algorithm (WOA) [15] was proposed as a meta-heuristic algorithm to handle optimization problems in 2016. As an emerging and prevailing meta-heuristic algorithm, the superiority and effectiveness of WOA are verified by being tested with 29 mathematical optimization problems and six structural design problems [16], which leads to the wide application of WOA in engineering optimization problems, such as electrical engineering, civil engineering, classification, clustering, image processing, mechanical engineering, control engineering, robot path, networks, industrial engineering, task scheduling, and other engineering applications [17][18][19][20][21][22][23][24][25][26][27][28]. However, similarly to other meta-heuristic algorithms, the WOA is confronted with the problem of slow convergence speed, which motivates chaos theory to be introduced into the WOA optimization process, and the results prove that the chaotic mappings are capable of enhancing the performance of WOA, especially Tent chaotic mapping [29].…”

Section: Introductionmentioning

confidence: 99%

Improving Bridge Expansion and Contraction Installation Replacement Decision System Using Hybrid Chaotic Whale Optimization Algorithm

Huang

2021

Applied Sciences

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Bridge expansion and contraction installation (BECI) has proved to be an essential component of the bridge structure due to its stability, comfort, and durability benefits. At present, traditional replacement technologies for modular type, comb plate type, and seamless type BECIs are widely applied worldwide. Nevertheless, it is unfortunate that the research conducted on decision-making (DM) approaches for the technical condition assessment and the optimal replacement plan selection of existing BECIs remain scarce, which results in the waste of resources and the increase in cost. Therefore, a BECI technical condition assessment approach, which contains specific on-site inspection regulations with both qualitative and quantitative descriptions, is proposed in this research, and a corresponding calculation program has been developed based on the MATLAB platform, which provides the basis for the necessity of replacement. Simultaneously, the hybrid chaotic whale optimization algorithm is designed and performed to improve and automate the process of optimal replacement plan selection under the assistance of the analytic hierarchy process (AHP), where both the achievement in consistency modification and the reservation of initial information are perused, and its superiority and effectiveness are verified via the comparative experimental analysis. The improved BECI replacement decision system is established, and the corresponding case study demonstrates that the proposed system in this research proves reasonable and feasible. The improved system can effectively assist bridge managers in making more informed operation and maintenance (O and M) decisions in actual engineering projects.

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Structural damage identification based on substructure method and improved whale optimization algorithm

Cited by 42 publications

References 35 publications

A New Stochastic Model Updating Method Based on Improved Cross-Model Cross-Mode Technique

A New Stochastic Model Updating Method Based on Improved Cross-Model Cross-Mode Technique

Structural Damage Identification Using a Modified Directional Bat Algorithm

Improving Bridge Expansion and Contraction Installation Replacement Decision System Using Hybrid Chaotic Whale Optimization Algorithm

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