An Overview of Deep Learning Methods Used in Vibration-Based Damage Detection in Civil Engineering 97 19. Ghahramani, Z.: Probabilistic machine learning and artificial intelligence. Nature. (2015). https://doi.org/10.1038/nature14541 20. Kwon, D., Kim, H., Kim, J., Suh, S.C., Kim, I., Kim, K.J.: A survey of deep learning-based network anomaly detection. Cluster Comput. (2017). https://doi.org/10.1007/s10586-017-1117-8 21. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science. 313 (2006). https://doi.org/10.1126/ science.1127647 22. Patterson, J., Gibson, A.: Deep Learning: A Practitioner’s Approach. O’Reilly Media, Newton, MA (2017). https://doi.org/10.1038/ nature14539 23. Fallahian, M., Khoshnoudian, F., Meruane, V.: Ensemble classification method for structural damage assessment under varying temperature. Struct. Health Monit. (2017). https://doi.org/10.1177/1475921717717311 24. Fallahian, M., Khoshnoudian, F., Talaei, S., Meruane, V., Shadan, F.: Experimental validation of a deep neural network—sparse representation classification ensemble method. Struct. Des. Tall Spec. Build. (2018). https://doi.org/10.1002/tal.1504 25. Shadan, F., Khoshnoudian, F., Esfandiari, A.: A frequency response-based structural damage identification using model updating method. Struct. Control Health Monit. (2016). https://doi.org/10.1002/stc.1768 26. Pathirage, C.S.N., Li, J., Li, L., Hao, H., Liu, W., Ni, P.: Structural damage identification based on autoencoder neural networks and deep learning. Eng. Struct. (2018). https://doi.org/10.1016/j.engstruct.2018.05.109 27. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 2012, 1097–1105 (2012). https://doi.org/10.1145/3065386 28. Kiranyaz, S., Waris, M.A., Ahmad, I., Hamila, R., Gabbouj, M.: Face segmentation in thumbnail images by data-adaptive convolutional segmentation networks. In: 2016 IEEE Int. Conf. Image Process., pp. 2306–2310 (2016). https://doi.org/10.1109/ICIP.2016.7532770 29. Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., Inman, D.J.: 1D convolutional neural networks and applications: a survey. Mech. Syst. Signal Process. 151 (2021). https://doi.org/10.1016/j.ymssp.2020.107398 30. Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M., Gabbouj, M., Inman, D.J.: A review of vibration-based damage detection in civil structures: from traditional methods to machine learning and deep learning applications. Mech. Syst. Signal Process. (2021). https://doi.org/10.1016/ j.ymssp.2020.107077 31. O. Avci, O. Abdeljaber, S. Kiranyaz, S. Sassi, A. Ibrahim, M. Gabbouj, One Dimensional Convolutional Neural Networks for Real-Time Damage Detection of Rotating Machinery, Conf. Proc. Soc. Exp. Mech. Ser., 2021 32. Avci, O., Abdeljaber, O., Kiranyaz, S., Inman, D.: Convolutional Neural Networks for Real-Time and Wireless Damage Detection, Conf. Proc. Soc. Exp. Mech. Ser. (2020). https://doi.org/10.1007/978-3-030-12115-0_17 33. O. Avci, O. Abdeljaber, S. Kiranyaz, Structural Damage Detection in Civil Engineering with Machine-Learning: Current State of the Art, Conf. Proc. Soc. Exp. Mech. Ser., 2021 34. Ciresan, D.C., Meier, U., Gambardella, L.M., Schmidhuber, J.: Deep, big, simple neural nets for handwritten digit recognition. Neural Comput. 22, 3207–3220 (2010). https://doi.org/10.1162/NECO_a_00052 35. Scherer, D., Müller, A., Behnke, S.: Evaluation of pooling operations in convolutional architectures for object recognition. In: Proc. 20th Int. Conf. Artif. Neural Networks Part III, pp. 92–101. Springer, Berlin (2010). https://doi.org/10.1007/978-3-642-15825-4_10 36. Kiranyaz, S., Ince, T., Gabbouj, M.: Personalized monitoring and advance warning system for cardiac arrhythmias. Sci. Rep. 7(2017). https:// doi.org/10.1038/s41598-017-09544-z 37. Kiranyaz, S., Ince, T., Hamila, R., Gabbouj, M.: Convolutional neural networks for patient-specific ECG classification. In: Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS (2015). https://doi.org/10.1109/EMBC.2015.7318926 38. Kiranyaz, S., Ince, T., Gabbouj, M.: Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans. Biomed. Eng. 63, 664–675 (2016). https://doi.org/10.1109/TBME.2015.2468589 39. Kiranyaz, S., Ince, T., Abdeljaber, O., Avci, O., Gabbouj, M.: 1-D convolutional neural networks for signal processing applications. In: ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc. (2019). https://doi.org/10.1109/ICASSP.2019.8682194 40. Abdeljaber, O., Avci, O., Kiranyaz, S., Gabbouj, M., Inman, D.J.: Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. J. Sound Vib. 388, 154–170 (2017). https://doi.org/10.1016/j.jsv.2016.10.043 41. Yu, Y., Wang, C., Gu, X., Li, J.: A novel deep learning-based method for damage identification of smart building structures. Struct. Health Monit. 18, 143–163 (2019). https://doi.org/10.1177/1475921718804132 42. Wu, Y.M., Samali, B.: Shake table testing of a base isolated model. Eng. Struct. (2002). https://doi.org/10.1016/S0141-0296(02)00054-8 43. Khodabandehlou, H., Pekcan, G., Fadali, M.S.: Vibration-based structural condition assessment using convolution neural networks. Struct. Control Health Monit. (2018). https://doi.org/10.1002/stc.2308 44. Cofre-Martel, S., Kobrich, P., Lopez Droguett, E., Meruane, V.: Deep convolutional neural network-based structural damage localization and quantification using transmissibility data. Shock Vib. (2019). https://doi.org/10.1155/2019/9859281 45. Cofré, S., Kobrich, P., López Droguett, E., Meruane, V.: Transmissibility based structural assessment using deep convolutional neural network. In: Proc. ISMA 2018 - Int. Conf. Noise Vib. Eng. USD 2018 - Int. Conf. Uncertain. Struct. Dyn. (2018) 46. Kiranyaz, S., Gastli, A., Ben-Brahim, L., Alemadi, N., Gabbouj, M.: Real-time fault detection and identification for MMC using 1D convolutional neural networks. IEEE Trans. Ind. Electron. (2018). https://doi.org/10.1109/TIE.2018.2833045 47. Ince, T., Kiranyaz, S., Eren, L., Askar, M., Gabbouj, M.: Real-time motor fault detection by 1-D convolutional neural networks. IEEE Trans. Ind. Electron. (2016). https://doi.org/10.1109/TIE.2016.2582729 48. Avci, O., Abdeljaber, O., Kiranyaz, S., Inman, D.: Structural damage detection in real time: implementation of 1D convolutional neural networks for SHM applications. In: Niezrecki, C. (ed.) Struct. Heal. Monit. Damage Detect Proc. 35th IMAC, A Conf. Expo. Struct. Dyn. 2017, vol. 7, pp. 49–54. Springer International Publishing, Cham (2017). https://doi.org/10.1007/978-3-319-54109-9_6 49. Abdeljaber, O., Avci, O., Kiranyaz, M.S., Boashash, B., Sodano, H., Inman, D.J.: 1-D CNNs for structural damage detection: verification on a structural health monitoring benchmark data. Neurocomputing. (2017). https://doi.org/10.1016/j.neucom.2017.09.069 50. Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M., Inman, D.J.: Wireless and real-time structural damage detection: a novel decentralized method for wireless sensor networks. J. Sound Vib. (2018) 51. Avci, O., Abdeljaber, O., Kiranyaz, S., Boashash, B., Sodano, H., Inman, D.J.: Efficiency validation of one dimensional convolutional neural networks for structural damage detection using a SHM benchmark data. In: 25th Int. Congr. Sound Vib. (2018)
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