Issue 59

D. Bui-Ngoc et alii, Frattura ed Integrità Strutturale, 59 (2022) 461-470; DOI: 10.3221/IGF-ESIS.59.30

[21] Hamid, O.A., Mohamed, A.R., Jiang, H. L., Deng, G., Penn; Yu, D. (2014). Convolutional Neural Networks for Speech Recognition. In: Ieee/acm transactions on audio, speech and language processing, Vol. 22(10), pp. 1533-1545. [22] Christian, S., Alexander, T., Dumitru, E. (2013). Deep Neural Networks for Object Detection. In: Advances in Neural Information Processing Systems 26, pp. 2553–2561. [23] Malek, S., Melgani, F., Bazi, Y. (2018). One - dimensional convolutional neural networks for spectroscopic signal regression. In: Journal of Chemometrics, 32, pp. 1-17. [24] Hieu, N.T, Thanh, B.T., Magd, A.W., Dung, B.N. (2021). Damage Detection in Structural Health Monitoring using Combination of Deep Neural Networks, Journal of Materials and Engineering Structures, pp. 619-626. [25] Amer, C. Kr., de Smet, C. A. M. and de Roeck, G. (1999). Proceedings of IMAC 17, the International Modal Analysis Conference, Kissimmee, FL, U.S.A., 1023–1029. [26] Tharwat, A. (2018). Damage detection tests. Classification assessment methods. Applied Computing and Informatics. 17(1) pp. 168-192. [27] Peeters, B., De Roeck, G. (2001). One-year monitoring of the Z24-Bridge: Environmental effects versus damage events. Earthq. Eng. Struct. Dyn. 30, pp. 149–171. [28] Maeck, J., Peeters, B., De Roeck, G. (2001). Damage identification on the Z24 bridge using vibration monitoring. Smart Mater. Struct. 10, pp. 512–517. [29] Figueiredo, E., Park, G., Figueiras, J., Farrar, C., Worden, K. (2009). Structural Health Monitoring Algorithm Comparisons using Standard Data Sets. Los Alamos National Laboratory: LA-14393. [30] Dung, B. N., Thanh, B. T., Hieu, N. T., Magd, A. W. and Guido, D. R. (2020). Structural health monitoring using handcrafted features and convolution neural network, Lecture Notes in Civil Engineering, 110, pp. 103-112.

470