Issue 65

J. She et alii, Frattura ed Integrità Strutturale, 65 (2023) 160-177; DOI: 10.3221/IGF-ESIS.65.11

1) The damage degree of the second to the third spandrel arch are most severe (point 1-point 3), which are about 30%. The damage degree of the crown of the main arch (point 4) is about 25%. 2) The WPERSS value is positively correlated with the damage degree. Furtherly, the damage identification effect of the bridge damage identification method based on WPERSS value is not quite sensitive to the above factors: the number of measuring points, the damage degree of the measuring points, and the degree of wavelet packet decomposition or noise. 3) Training effects of optimized BPNN are significantly better than BPNN whose MSE is 0.00041639, R2 is 0.93826, and training time is 4.42015s when n=12. Among the optimization approaches, the training effect of PSO-BPNN is better than GA-BPNN in terms of consuming time, while the training time of PSO-BPNN is nearly 5 times as long as that of GA BPNN. The MSE of PSO-BPNN is 0.00023449, which is 0.00004425 smaller than GA-BPNN. The R2 of PSO-BPNN is 0.96523, which is 0.00656 larger than GA-BPNN. A CKNOWLEDGEMENTS he authors are grateful for the support of Science and Technology Project of Department of Transport of Shaanxi Province (22-23K); Innovation and Entrepreneurship Training Program for College Students Research on Structural State and Degradation Mechanism of Stone Arch Bridges on Rural Roads (S20210712534). R EFERENCES [1] Figueiredo, E. and Brownjohn, J. (2022) Three decades of statistical pattern recognition paradigm for SHM of bridges. Structural Health Monitoring 21(6), pp. 3018-3054. DOI: 10.1177/14759217221075241. [2] Rizzo, P. and Enshaeian, A. (2021) Challenges in bridge health monitoring: A review. Sensors 21(13):4336. DOI: 10.3390/s21134336. [3] Goyal, D. and Pabla, B. S. (2016) The vibration monitoring methods and signal processing techniques for structural health monitoring: a review. Archives of Computational Methods in Engineering, 23(4), pp. 585-594. DOI: 10.1007/s11831-015-9145-0. [4] Facchini, G., Bernardini, L., Atek, S. and Gaudenzi, P. (2015) Use of the wavelet packet transform for pattern recognition in a structural health monitoring application. Journal of Intelligent Material Systems and Structures 26(12), pp. 1513-1529. DOI: 10.1177/1045389X14544146. [5] Kankanamge, Y., Hu, Y. and Shao, X. (2020) Application of wavelet transform in structural health monitoring. Earthquake Engineering and Engineering Vibration, 19(2), 515-532. DOI: 10.1007/s11803-020-0576-8. [6] Zhu Jinsong, Sun Yadan (2015) Wavelet Packet Energy Based Damage Detection Index for Bridge (In Chinese). Journal of Vibration, Measurement & Diagnosis 5(04), pp. 715-721+800. DOI: 10.16450/j.cnki.issn.1004-6801.2015.04.019. [7] Yue Pan, Limao Zhang, Xianguo Wu, Kainan Zhang, Miroslaw J. Skibniewski (2019) Structural health monitoring and assessment using wavelet packet energy spectrum. Safety Science 120, pp. 652-665. DOI: 10.1016/j.ssci.2019.08.015. [8] Sun L, Shang Z, Xia Y, et al. (2020) Review of bridge structural health monitoring aided by big data and artificial intelligence: From condition assessment to damage detection. Journal of Structural Engineering 2020 (5), 04020073. DOI: 10.1061/(ASCE)ST.1943-541X.0002535. [9] Malekloo A, Ozer E, AlHamaydeh M, et al. (2022) Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights. Structural Health Monitoring 21(4), pp. 1906-1955. DOI: 10.1177/14759217211036880. [10] Moisés Silva, Adam Santos, Eloi Figueiredo, et al. (2016) A novel unsupervised approach based on a genetic algorithm for structural damage detection in bridges. Engineering Applications of Artificial Intelligence 52, pp. 168-180. DOI: 10.1016/j.engappai.2016.03.002. [11] Osama Abdeljaber, Onur Avci, Mustafa Serkan Kiranyaz, et al. (2018) 1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data. Neurocomputing 275, pp. 1308-1317. DOI: 10.1016/j.neucom.2017.09.069. [12] Liu, J., Li, P., Tang, X. et al. (2021) Research on improved convolutional wavelet neural network. Sci Rep 11, 17941. DOI: 10.1038/s41598-021-97195-6. T

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