Performance evaluation of the artificial hummingbird algorithm in the problem of structural damage identification
Email:
nthieu@utc.edu.vn
Từ khóa:
damage identification, Artificial Hummingbird Algorithm, metaheuristic algorithms, damaged bridge structure
Tóm tắt
Recently, Structural Health Monitoring (SHM) has become a critical component of the maintenance and safety of lifeline infrastructures such as dams, skyscrapers, and bridges, thanks to its ability to detect structural failures at the early stages. In this paper, we evaluate the performance of the SHM damage identification tool using a novel metaheuristic algorithm called the Artificial Hummingbird Algorithm (AHA). The proposed approach is evaluated by two case studies of different bridge structures in Vietnam with different simulated damage scenarios. The potency of the AHA is compared against the other well-known metaheuristic algorithms such as Cuckoo Search (CS), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Teaching-Learning Based Optimization (TLBO). The results show that the AHA performs much better than the other algorithms in terms of accuracy and computational cost. The application of AHA can help to reduce the cost and time required for structural maintenance significantly, as well as improve the lifecycle of the structure.Tài liệu tham khảo
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[2]. S. Das, P. Saha, Performance of swarm intelligence based chaotic meta-heuristic algorithms in civil structural health monitoring, Measurement, 169 (2021) 108533. https://doi.org/10.1016/j.measurement.2020.108533
[3]. H. Le-Minh, T. Sang-To, S. Khatir, M.A. Wahab, L.Thanh Cuong, Damage identification in high-rise concrete structures using a bio-inspired meta-heuristic optimization algorithm, Advances in Engineering Software, 176 (2023) 103399. https://doi.org/10.1016/j.advengsoft.2022.103399
[4]. L.V. Ho, D.H. Nguyen, M. Mousavi, G. De Roeck, T. Bui-Tien, A.H. Gandomi, M.A. Wahab, A hybrid computational intelligence approach for structural damage detection using marine predator algorithm and feedforward neural networks, Computers & Structures, 252 (2021) 106568. https://doi.org/10.1016/j.compstruc.2021.106568
[5]. H. Tran-Ngoc, S. Khatir, H. Ho-Khac, G. De Roeck, T. Bui-Tien, M.A. Wahab, Efficient Artificial neural networks based on a hybrid metaheuristic optimization algorithm for damage detection in laminated composite structures, Composite Structures, 262 (2021) 113339. https://doi.org/10.1016/j.compstruct.2020.113339
[6]. H. Hao, Y. Xia, Vibration-based Damage Detection of Structures by Genetic Algorithm, J. Comput. Civ. Eng, 16 (2002) 222–229. https://doi.org/10.1061/(ASCE)0887-3801(2002)16:3(222)
[7]. H. Tran-Ngoc, S. Khatir, G. De Roeck, T. Bui-Tien, M.A. Wahab, An efficient artificial neural network for damage detection in bridges and beam-like structures by improving training parameters using cuckoo search algorithm, Engineering Structures, 199 (2019) 109637. https://doi.org/10.1016/j.engstruct.2019.109637
[8]. Z. Wei, J. Liu, Z. Lu, Structural damage detection using improved particle swarm optimization, Inverse Problems in Science and Engineering, 26 (2018) 792–810. https://doi.org/10.1080/17415977.2017.1347168
[9]. G.F. Gomes, S.S. da Cunha, A.C. Ancelotti, A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates, Engineering with Computers, 35 (2019) 619–626. https://doi.org/10.1007/s00366-018-0620-8
[10]. B. Ahmadi-Nedushan, H. Fathnejat, A modified teaching–learning optimization algorithm for structural damage detection using a novel damage index based on modal flexibility and strain energy under environmental variations, Engineering with Computers, 38 (2022) 847–874. https://doi.org/10.1007/s00366-020-01197-3
[11]. S. Bureerat, N. Pholdee, Adaptive Sine Cosine Algorithm Integrated with Differential Evolution for Structural Damage Detection, Computational Science and Its Applications – ICCSA 2017, Springer International Publishing, Cham, (2017) 71–86. https://doi.org/10.1007/978-3-319-62392-4_6
[12]. A. Ramadan, S. Kamel, M.H. Hassan, S. Kamel, H.M. Hasanien, Accurate Photovoltaic Models Based on an Adaptive Opposition Artificial Hummingbird Algorithm. Electronics, 11 (2022) 318. https://doi.org/10.3390/electronics11030318
[13]. A. Ramadan, M. Ebeed, S. Kamel, E.M. Ahmed, M. Tostado-Véliz, Optimal allocation of renewable DGs using artificial hummingbird algorithm under uncertainty conditions, Ain Shams Engineering Journal, 14 (2023) 101872. https://doi.org/10.1016/j.asej.2022.101872
[14]. Md. Shadman Abid, H.J. Apon, K.A. Morshed, A. Ahmed, Optimal Planning of Multiple Renewable Energy-Integrated Distribution System With Uncertainties Using Artificial Hummingbird Algorithm, IEEE Access, 10 (2022) 40716–40730. https://doi.org/10.1109/ACCESS.2022.3167395
[15]. J. Wang, Y. Li, G. Hu, M. Yang, An enhanced artificial hummingbird algorithm and its application in truss topology engineering optimization, Advanced Engineering Informatics, 54 (2022) 101761. https://doi.org/10.1016/j.aei.2022.101761
[16]. W. Zhao, L. Wang, S. Mirjalili, Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications, Computer Methods in Applied Mechanics and Engineering, 388 (2022) 114194. https://doi.org/10.1016/j.cma.2021.114194
[17]. B.A. Fennelly, Observations from the Jewel Rooms, Ecotone, 8 (2012) 74–85. https://doi.org/10.1353/ect.2012.0064
[18]. S. François, M. Schevenels, D. Dooms, M. Jansen, J. Wambacq, G. Lombaert, G. Degrande, G. De Roeck, Stabil: An educational Matlab toolbox for static and dynamic structural analysis, Comput Appl Eng Educ, 29 (2021) 1372–1389. https://doi.org/10.1002/cae.22391
Tải xuống
Chưa có dữ liệu thống kê
Nhận bài
29/03/2023
Nhận bài sửa
21/04/2023
Chấp nhận đăng
15/05/2023
Xuất bản
15/05/2023
Chuyên mục
Công trình khoa học
Kiểu trích dẫn
Nguyen Ngoc, L., Nguyen Huu, Q., Nguyen Ngoc, L., & Nguyen Tran, H. (1684083600). Performance evaluation of the artificial hummingbird algorithm in the problem of structural damage identification. Tạp Chí Khoa Học Giao Thông Vận Tải, 74(4), 413-427. https://doi.org/10.47869/tcsj.74.4.3
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