REFERENCES

1. He Z, Li W, Salehi H, Zhang H, Zhou H, Jiao P. Integrated structural health monitoring in bridge engineering. Automat Constr 2022;136:104168.

2. Khodabandehlou H, Pekcan G, Fadali MS. Vibration-based structural condition assessment using convolution neural networks. Struct Control Health Monit 2019;26:e2308.

3. Gonen S, Erduran E. A hybrid method for vibration-based bridge damage detection. Remote Sensing 2022;14:6054.

4. Daneshvar MH, Saffarian M, Jahangir H, Sarmadi H. Damage identification of structural systems by modal strain energy and an optimization-based iterative regularization method. Eng Comput 2023;39:2067-87.

5. Pooya SMH, Massumi A. A novel damage detection method in beam-like structures based on the relation between modal kinetic energy and modal strain energy and using only damaged structure data. J Sound Vib 2022;530:116943.

6. An X, Zhang Q, Li C, Hou J, Shi Y. Damage identification of semi-rigid joints in frame structures based on additional virtual mass method. Sensors 2022;22:6495.

7. Zhang Y, Xiong Z, Liang Z, She J, Ma C. Structural damage identification system suitable for old arch bridge in rural regions: random forest approach. Comp Model Eng Sci 2023;136:447-69.

8. Gomes GF, de Almeida FA, Junqueira DM, da Cunha SS, Ancelotti AC. Optimized damage identification in CFRP plates by reduced mode shapes and GA-ANN methods. Eng Struct 2019;181:111-23.

9. Cuong-Le T, Nghia-Nguyen T, Khatir S, Trong-Nguyen P, Mirjalili S, Nguyen KD. An efficient approach for damage identification based on improved machine learning using PSO-SVM. Eng Comput 2022;38:3069-84.

10. Ren J, Zhang B, Zhu X, Li S. Damaged cable identification in cable-stayed bridge from bridge deck strain measurements using support vector machine. Adv Struct Eng 2022;25:754-71.

11. Farias SV, Saotome O, Campos Velho HF, Shiguemori EH. A damage detection method using neural network optimized by multiple particle collision algorithm. J Sensors 2021;2021:1-14.

12. Tang Q, Zhou J, Xin J, Zhao S, Zhou Y. Autoregressive model-based structural damage identification and localization using convolutional neural networks. KSCE J Civ Eng 2020;24:2173-85.

13. Mai HT, Lee S, Kang J, Lee J. A damage-informed neural network framework for structural damage identification. Comput Struct 2024;292:107232.

14. Mousavi M, Gandomi AH. Structural health monitoring under environmental and operational variations using MCD prediction error. J Sound Vib 2021;512:116370.

15. Sony S, Gamage S, Sadhu A, Samarabandu J. Vibration-based multiclass damage detection and localization using long short-term memory networks. Structures 2022;35:436-51.

16. Fu L, Tang Q, Gao P, Xin J, Zhou J. Damage identification of long-span bridges using the hybrid of convolutional neural network and long short-term memory network. Algorithms 2021;14:180.

17. Fernandez-Navamuel A, Pardo D, Magalhães F, Zamora-Sánchez D, Omella ÁJ, Garcia-Sanchez D. Bridge damage identification under varying environmental and operational conditions combining Deep Learning and numerical simulations. Mech Syst Signal Proc 2023;200:110471.

18. Sony S, Gamage S, Sadhu A, Samarabandu J. Multiclass damage identification in a full-scale bridge using optimally tuned one-dimensional convolutional neural network. J Comput Civ Eng 2022;36:04021035.

19. Liu Z, Lin Y, Cao Y, et al. Swin transformer: hierarchical vision transformer using shifted windows. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV); 2021 Oct 10-17; Montreal, Canada. IEEE; 2021. pp. 9992-10002.

20. Üzen H, Türkoğlu M, Yanikoglu B, Hanbay D. Swin-MFINet: Swin transformer based multi-feature integration network for detection of pixel-level surface defects. Expert Syst Appl 2022;209:118269.

21. Xu Y, Wang X, Zhang H, Lin H. SE-Swin: an improved Swin-Transfomer network of self-ensemble feature extraction framework for image retrieval. IET Image Process 2024;18:13-21.

22. Miao R, Shan Z, Zhou Q, et al. Real-time defect identification of narrow overlap welds and application based on convolutional neural networks. J Manuf Syst 2022;62:800-10.

23. Zhou J, Li Z, Chen J. Application of two dimensional Morlet wavelet transform in damage detection for composite laminates. Compos Struct 2023;318:117091.

24. Hou Y, Qian S, Li X, Wei S, Zheng X, Zhou S. Application of vibration data mining and deep neural networks in bridge damage identification. Electronics 2023;12:3613.

25. Diao Y, Men X, Sun Z, Guo K, Wang Y. Structural damage identification based on the transmissibility function and support vector machine. Shock Vib 2018;2018:1-13.

Intelligence & Robotics
ISSN 2770-3541 (Online)
Follow Us

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/