[1] J. Peng, S. Zhang, D. Peng, and K. Liang, “Application of machine learning method in bridge health monitoring,” 2017 2nd Int. Conf. Reliab. Syst. Eng. ICRSE 2017, no. Icrse, 2017, doi: 10.1109/ICRSE.2017.8030793.
[2] K. H. Padil, N. Bakhary, and H. Hao, “The use of a non-probabilistic artificial neural network to consider uncertainties in vibration-based-damage detection,” Mech. Syst. Signal Process., vol. 83, pp. 194–209, Jan. 2017, doi: 10.1016/j.ymssp.2016.06.007.
[3] P. da S. L. Alexandrino, G. F. Gomes, and S. S. Cunha, “A robust optimization for damage detection using multiobjective genetic algorithm, neural network and fuzzy decision making,” Inverse Probl. Sci. Eng., vol. 28, no. 1, pp. 21–46, Jan. 2020, doi: 10.1080/17415977.2019.1583225.
[4] H. S. Kim, C. Jin, M. H. Kim, and K. Kim, “Damage detection of bottom-set gillnet using Artificial Neural Network,” Ocean Eng., vol. 208, p. 107423, Jul. 2020, doi: 10.1016/J.OCEANENG.2020.107423.
[5] D. E. Sidarta, J. O’Sullivan, and H. J. Lim, “Damage detection of offshore platform mooring line using artificial neural network,” in Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE, Sep. 2018, vol. 1, doi: 10.1115/OMAE2018-77084.
[6] A. Santos, E. Figueiredo, M. F. M. Silva, C. S. Sales, and J. C. W. A. Costa, “Machine learning algorithms for damage detection: Kernel-based approaches,” Elsevier, 2016.
[7] C. Modarres, N. Astorga, E. L. Droguett, and V. Meruane, “Convolutional neural networks for automated damage recognition and damage type identification,” Struct. Control Heal. Monit., vol. 25, no. 10, pp. 1–17, 2018, doi: 10.1002/stc.2230.
[8] M. Azimi, A. D. Eslamlou, and G. Pekcan, “Data-driven structural health monitoring and damage detection through deep learning: State-ofthe- art review,” Sensors (Switzerland), vol. 20, no. 10, 2020, doi: 10.3390/s20102778.
[9] Y. Z. Lin, Z. H. Nie, and H. W. Ma, “Structural Damage Detection with Automatic Feature-Extraction through Deep Learning,” Comput. Civ. Infrastruct. Eng., vol. 32, no. 12, pp. 1025–1046, 2017, doi: 10.1111/mice.12313.
[10] O. Abdeljaber, O. Avci, M. S. Kiranyaz, B. Boashash, H. Sodano, and D. J. Inman, “1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data,” Neurocomputing, vol. 275, pp. 1308–1317, 2018, doi: 10.1016/j.neucom.2017.09.069.
[11] Y. J. Cha, W. Choi, and O. Büyüköztürk, “Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks,” Comput. Civ. Infrastruct. Eng., vol. 32, no. 5, pp. 361–378, 2017, doi: 10.1111/mice.12263.
[12] Y. Yu, C. Wang, X. Gu, and J. Li, “A novel deep learning-based method for damage identification of smart building structures,” Struct. Heal. Monit., vol. 18, no. 1, pp. 143–163, 2019, doi: 10.1177/1475921718804132.
[13] Y. Bao, Z. Tang, H. Li, and Y. Zhang, “Computer vision and deep learning–based data anomaly detection method for structural health monitoring,” Struct. Heal. Monit., vol. 18, no. 2, pp. 401–421, 2019, doi: 10.1177/1475921718757405.
[14] H. Liu and Y. Zhang, “Image-driven structural steel damage condition assessment method using deep learning algorithm,” Meas. J. Int. Meas. Confed., vol. 133, pp. 168–181, 2019, doi: 10.1016/j.measurement.2018.09.081.
[15] T. Guo, L. Wu, C. Wang, and Z. Xu, “Damage detection in a novel deep-learning framework: a robust method for feature extraction,” Struct. Heal. Monit., no. 28, 2019, doi: 10.1177/1475921719846051.
[16] H. Liu and Y. Zhang, “Deep learning-based brace damage detection for concentrically braced frame structures under seismic loadings,” Adv. Struct. Eng., 2019, doi: 10.1177/1369433219859389.
[17] C. Modarres, N. Astorga, E. L. Droguett, and V. Meruane, “Convolutional neural networks for automated damage recognition and damage type identification,” Struct. Control Heal. Monit., vol. 25, no. 10, 2018, doi: 10.1002/stc.2230.
[18] H. Li, J. Zhang, W. Li, and H. Jiang, “Study on the technology of replacement of closure segment for strengthening existing prestressed concrete cable stayed bridges,” Tumu Gongcheng Xuebao/China Civ. Eng. J., vol. 44, no. 7, pp. 83–89, 2011.
[19] M. R. Kaloop and J. W. Hu, “Stayed-Cable Bridge Damage Detection and Localization Based on Accelerometer Health Monitoring Measurements,” Shock Vib., 2015, doi: 10.1155/2015/102680.
[20] S. Arangio and F. Bontempi, “Structural health monitoring of a cable-stayed bridge with Bayesian neural networks,” Struct. Infrastruct. Eng., vol. 11, no. 4, pp. 575–587, 2015, doi: 10.1080/15732479.2014.951867.
[21] J. DeLaughter, B. Meltz, S. Smith, J. Yun, and M. Murat, “Use of z-score to rescale amplitudes,” Lead. Edge, vol. 24, no. 7, pp. 698–701, 2005, doi: 10.1190/1.1993260.
[22] Y. LeCun and others, “Generalization and network design strategies,” in Connectionism in perspective, 1989, pp. 143–155.
[23] Y. LeCun et al., “Backpropagation Applied to Handwritten Zip Code Recognition,” Neural Comput., vol. 1, no. 4, pp. 541–551, 1989, doi: 10.1162/neco.1989.1.4.541.
[24] E. R. Ziegel, “The Elements of Statistical Learning,” Technometrics, vol. 45, no. 3, pp. 267–268, 2003, doi: 10.1198/tech.2003.s770.
[25] Y. T. Zhou and R. Chellappa, “Computation of optical flow using a neural network,” 1988, pp. 71–78, doi: 10.1109/icnn.1988.23914.
[26] M. Heusel et al., “Fast and Accurate CNN Learning on ImageNet,” Iccv, p. 2015, 2015.
[27] L. Bull, K. Worden, G. Manson, and N. Dervilis, “Active learning for semi-supervised structural health monitoring,” J. Sound Vib., vol. 437, pp. 373–388, 2018, doi: 10.1016/j.jsv.2018.08.040.
[28] Y. Liu and Q. Liu, “Convolutional neural networks with large-margin softmax loss function for cognitive load recognition,” in Chinese Control Conference, CCC, 2017, pp. 4045–4049, doi: 10.23919/ChiCC.2017.8027991.
[29] Z. Zhang and M. R. Sabuncu, “Generalized cross entropy loss for training deep neural networks with noisy labels,” in Advances in Neural Information Processing Systems, 2018, vol. 2018-Decem, pp. 8778–8788.
[30] D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” pp. 1–15, 2014, [Online]. Available: http://arxiv.org/abs/1412.6980.