This work is licensed under the Creative Commons Attribution 4.0 International License.
Zhou, Y. L., & Wahab, M. A. (2017). Cosine based and extended transmissibility damage indicators for structural damage detection. Engineering Structures, 141(JUN.15), 175-183.Search in Google Scholar
Cui, W., & Caracoglia, L. (2016). Exploring hurricane wind speed along US Atlantic coast in warming climate and effects on predictions of structural damage and intervention costs. Engineering Structures, 122, 209-225.Search in Google Scholar
Hirotoshi, U., Uebayashi, M., et al. (2016). Evaluation of the structural damage of high-rise reinforced concrete buildings using ambient vibrations recorded before and after damage. Earthquake Engineering & Structural Dynamics, 45(2), 213-228.Search in Google Scholar
Cao, J., & Liu, X. (2016). Wireless Sensor Networks for Structural Health Monitoring. Springer International Publishing.Search in Google Scholar
Md, Z. A., Alam, et al. (2017). Dependable Structural Health Monitoring Using Wireless Sensor Networks. IEEE Transactions on Dependable & Secure Computing.Search in Google Scholar
Zhang, J., Gui, T., Adi, M., et al. (2017). A Review of Passive RFID Tag Antenna-Based Sensors and Systems for Structural Health Monitoring Applications. Sensors, 17(2), 265.Search in Google Scholar
Feng, D., & Feng, M. Q. (2016). Vision-based multipoint displacement measurement for structural health monitoring. Structural Control and Health Monitoring, 23(5), 876-890.Search in Google Scholar
Feng, D., & Feng, M. Q. (2017). Experimental validation of cost-effective vision-based structural health monitoring. Mechanical Systems & Signal Processing, 88(MAY), 199-211.Search in Google Scholar
Kulkarni, S. S., & Achenbach, J. D. (2016). Structural Health Monitoring and Damage Prognosis in Fatigue. Structural Health Monitoring, 7(1), 37-49.Search in Google Scholar
Gorgin, R. (2020). Damage identification technique based on mode shape analysis of beam structures. Structures, 27, 2300-2308.Search in Google Scholar
Lam, H. F., & Yang, J. (2015). Bayesian structural damage detection of steel towers using measured modal parameters. Earthquakes & Structures, 8(4), 935-956.Search in Google Scholar
Hamey, C. S., Lestari, W., Qiao, P., et al. (2016). Experimental Damage Identification of Carbon/Epoxy Composite Beams Using Curvature Mode Shapes. Structural Health Monitoring: An International Journal.Search in Google Scholar
Yang, Z., Zhang, Y., Wang, Y., et al. (2020). Conventional Bridge Damage Identification Based on BP Neural Network. IOP Conference Series Materials Science and Engineering, 730, 012037.Search in Google Scholar
Yza, B., Sl, A., Tx, C., et al. (2021). Application of convolutional neural network in random structural damage identification. Structures, 29, 570-576.Search in Google Scholar
Chen, F. C., & Jahanshahi, R. (2018). NB-CNN: Deep Learning-based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion. IEEE Transactions on Industrial Electronics, 65(99), 4392-4400.Search in Google Scholar
Guirao, J. L. G., Sabir, Z., & Saeed, T. (2020). Design and numerical solutions of a novel third-order nonlinear Emden–Fowler delay differential model. Mathematical Problems in Engineering.Search in Google Scholar
Wang, X., & Wang, Y. (2020). The Research on Bridge Engineering Risk Management and Assessment Model Based on BP Neural Network. IOP Conference Series: Earth and Environmental Science, 455(1), 012127 (7pp).Search in Google Scholar
Regalado, F. F. J., Esenarro, D., Reátegui, M. D., et al. (2021). Model based on balanced scorecard applied to the strategic plan of a Peruvian public entity. 3c Empresa: investigación y pensamiento crítico, 10(4), 127-147.Search in Google Scholar