Model Validation and Uncertainty Quantification, Volume 3

33 Structural Damage Detection Using Convolutional Neural Networks 337 References 1. Flood, I., Kartam, N.: Neural networks in civil engineering. II: systems and application. J. Comput. Civ. Eng. 8(2), 149–162 (1994) 2. Shi, A., Yu, X.-H.: Structural damage detection using artificial neural networks and wavelet transform. In: 2012 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings, pp. 7–11. IEEE, New York (2012) 3. Hearn, G., Testa, R.B.: Modal analysis for damage detection in structures. J. Struct. Eng. 117(10), 3042–3063 (1991) 4. Hadzima-Nyarko, M., Nyarko, E.K., Moric´, D.: A neural network based modelling and sensitivity analysis of damage ratio coefficient. Expert Syst. Appl. 38(10), 13405–13413 (2011) 5. Shu, J., Zhang, Z., Gonzalez, I., Karoumi, R.: The application of a damage detection method using artificial neural network and train-induced vibrations on a simplified railway bridge model. Eng. Struct. 52, 408–421 (2013) 6. Fang, X., Luo, H., Tang, J.: Structural damage detection using neural network with learning rate improvement. Comput. Struct. 83(25), 2150–2161 (2005) 7. Szewczyk, Z.P., Hajela, P.: Damage detection in structures based on feature-sensitive neural networks. J. Comput. Civ. Eng. 8(2), 163–178 (1994) 8. Zhao, J., Ivan, J.N., DeWolf, J.T.: Structural damage detection using artificial neural networks. J. Infrastruct. Syst. 4(3), 93–101 (1998) 9. Bakhary, N., Hao, H., Deeks, A.J.: Damage detection using artificial neural network with consideration of uncertainties. Eng. Struct. 29(11), 2806–2815 (2007) 10. Nazarko, P., Ziemian´ski, L.: Application of artificial neural networks in the damage identification of structural elements. Comput. Assist. Mech. Eng. Sci. 18(3), 175–189 (2011) 11. Flood, I.: Towards the next generation of artificial neural networks for civil engineering. Adv. Eng. Inform. 22(1), 4–14 (2008) 12. Yao, R., Pakzad, S.N., Venkitasubramaniam, P.: Compressive sensing based structural damage detection and localization using theoretical and metaheuristic statistics. Struct. Control Health Monit. 24, e1881 (2017). doi:10.1002/stc.1881 13. Yao, R., Pakzad, S.N., Venkitasubramaniam, P., Hudson, J.M.: Iterative spatial compressive sensing strategy for structural damage diagnosis as a big data problem. In: Dynamics of Civil Structures, vol. 2, pp. 185–190. Springer, New York (2015) 14. Shahidi, S.G., Gulgec, N.S., Pakzad, S.N.: Compressive sensing strategies for multiple damage detection and localization. In: Dynamics of Civil Structures, vol. 2, pp. 17–22. Springer, New York (2016) 15. Bishop, C.M.: Pattern recognition. Mach. Learn. 128, 1–58 (2006) 16. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015) 17. Shalev-Shwartz, S., Ben-David, S. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, New York (2014) 18. Nilsson, N.J.: Introduction to machine learning. An Early Draft of a Proposed Textbook (1996) 19. Smola, A., Vishwanathan, S.V.N.: Introduction to Machine Learning, pp. 32–34. Cambridge University, New York (2008) 20. Alpaydin, E.: Introduction to Machine Learning. MIT, Cambridge (2014) 21. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998) 22. Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G.: Recent advances in convolutional neural networks. arXiv preprint, arXiv:1512.07108 (2015) 23. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009 (CVPR 2009), pp. 248–255. IEEE, New York (2009) 24. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems. Curran Associates Inc. (2012) 25. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint, arXiv:1409.1556 (2014) 26. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: European Conference on Computer Vision, pp. 818–833. Springer, Berlin (2014) 27. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015) 28. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv preprint, arXiv:1512.03385 (2015) 29. LeCun, Y., Bengio, Y.: Convolutional Networks for Images, Speech, and Time-Series. MIT Press, Cambridge (1995) 30. Elkordy, M.F., Chang, K.C., Lee, G.C.: Neural networks trained by analytically simulated damage states. J. Comput. Civ. Eng. 7(2), 130–145 (1993) 31. Theano Development Team. Theano: a Python framework for fast computation of mathematical expressions. arXiv e-prints, abs/1605.02688, May (2016) 32. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Aistats, vol. 9, pp. 249–256 (2010) 33. Robbins, H., Monro, S.: A stochastic approximation method. Ann. Math. Stat. 22(3), 400–407 (1951) 34. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Cogn. Model. 5(3), 1 (1988)

RkJQdWJsaXNoZXIy MTMzNzEzMQ==