Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms

Bawono, A. S., Ali, M. I., Kusumadewi, S., & Ramli, N. I. (2020). Methodological study to classification of damage state immediately subsequent to the Banjarnegara Indonesia Earthquake on 2018. IOP Conference Series: Materials Science and Engineering, 712(1), 012032.

Chandra, N., & Vaidya, H. (2022). Building detection methods from remotely sensed images. Current Science, 122(11), 1252.

Demir, S., & Sahin, E. K. (2022). Comparison of tree-based machine learning algorithms for predicting liquefaction potential using canonical correlation forest, rotation forest, and random forest based on CPT data. Soil Dynamics and Earthquake Engineering, 154, 107130.

Du, A. (2020). Ground motion intensity measure selection for probabilistic seismic risk assessment of multi-response structural systems rice University

Fang, C. (2022). SMAs for infrastructures in seismic zones: a critical review of latest trends and future needs. J Build Eng, 57, 104918.

Gaba, A., Jana, A., Subramaniam, R., Agrawal, Y., & Meleet, M. (2019). Analysis and prediction of earthquake impact-a machine learning approach. In 2019 4th International Conference on Computational Systems and Information Technology For Sustainable Solution (CSITSS). https://doi.org/10.1109/csitss47250.2019.9031026

Goswami, S., Anitescu, C., Chakraborty, S., & Rabczuk, T. (2020). Transfer learning enhanced physics informed neural network for phase-field modeling of fracture. Theoretical and Applied Fracture Mechanics, 106, 102447. https://doi.org/10.1016/j.tafmec.2019.102447

Han, J., Kim, J., Park, S., Son, S., & Ryu, M. (2020). Seismic vulnerability assessment and mapping of Gyeongju, South Korea using frequency ratio, decision tree, and random forest. Sustainability, 12(18), 7787.

Harirchian, E., Kumari, V., Jadhav, K., Rasulzade, S., Lahmer, T., & Raj Das, R. (2021a). A synthesized study based on machine learning approaches for rapid classifying earthquake damage grades to RC buildings. Applied Sciences, 11(16), 7540.

Ji, M., Liu, L., Du, R., & Buchroithner, M. F. (2019). A comparative study of texture and convolutional neural network features for detecting collapsed buildings after earthquakes using pre-and post-event satellite imagery. Remote Sensing, 11(10), 1202.

Kabir, M. A. B., Hasan, A. S., & Billah, A. M. (2021). Failure mode identification of column base plate connection using data-driven machine learning techniques. Engineering Structures, 240, 112389.

Kaveh, A. (2014). Advances in metaheuristic algorithms for optimal design of structures (pp. 9–40). Springer International Publishing.

Kaveh, A. (2017). Applications of metaheuristic optimization algorithms in civil engineering. Springer International Publishing.

Kaveh, A., & Dadras, A. (2018). Structural damage identification using an enhanced thermal exchange optimization algorithm. Engineering Optimization, 50(3), 430–451.

Kaveh, A., Gholipour, Y., & Rahami, H. (2008). Optimal design of transmission towers using genetic algorithm and neural networks. International Journal of Space Structures, 23(1), 1–19.

Kaveh, A., & Khalegi, A. (1998). Prediction of strength for concrete specimens using artificial neural networks. Advances in Engineering Computational Technology, 53, 165–171.

Kaveh, A., & Khavaninzadeh, N. (2023). Efficient training of two ANNs using four meta-heuristic algorithms for predicting the FRP strength. Structures, 52, 256–272.

Kaveh, A., & Sabani Rad, A. (2023). Metaheuristic-based optimal design of truss structures using algebraic force method. Structures, 50, 1951–1964.

Kaveh, A., & Servati, H. (2001). Design of double layer grids using backpropagation neural networks. Computers & Structures, 79(17), 1561–1568.

Khaleghi, M., Salimi, J., Farhangi, V., Moradi, M., & Karakouzian, M. (2021). Application of artificial neural network to predict load bearing capacity and stiffness of perforated masonry walls. Civileng, 2(1), 48–67. https://doi.org/10.3390/civileng2010004

Kiani, J., Camp, C., & Pezeshk, S. (2019). On the application of machine learning techniques to derive seismic fragility curves. Computers & Structures, 218, 108–122. https://doi.org/10.1016/j.compstruc.2019.03.004

Kostinakis, K., Morfidis, K., Demertzis, K., & Iliadis, L. (2022). Classification of buildings’ potential for seismic damage by means of artificial intelligence techniques. Preprint retrieved from https://arXiv.org/arXiv:2205.01076

Li, L. (2021). Social media crowdsourcing for rapid damage assessment following sudden-onset earthquakes University of Maryland, College Park

Lu, G. Y., Wang, K. H., & Zhang, P. P. (2019). Seismic design and evaluation methods for small-to-medium span highway girder bridges based on machine learning and earthquake damage experience. Journal of Highway and Transportation Research and Development (english Edition), 13(1), 24–37.

Mallouhy, R., Abou Jaoude, C., Guyeux, C., & Makhoul, A. (2019). Major earthquake event prediction using various machine learning algorithms. In 2019 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

Mangalathu, S., & Jeon, J. (2018a). Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques. Engineering Structures, 160, 85–94. https://doi.org/10.1016/j.engstruct.2018.01.008

Phoon, K.-K., & Zhang, W. (2022). Future of machine learning in geotechnics. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 1–16.

Roeslin, S., Ma, Q., Chigullapally, P., Wicker, J., & Wotherspoon, L. (2022). Development of a seismic loss prediction model for residential buildings using machine learning–Christchurch, New Zealand. Natural Hazards and Earth System Sciences, 23, 1–31.

Roeslin, S., Ma, Q., Juárez-Garcia, H., Gómez-Bernal, A., Wicker, J., & Wotherspoon, L. (2020). A machine learning damage prediction model for the 2017 Puebla-Morelos, Mexico, earthquake. Earthquake Spectra, 36(2_suppl), 314–339.

Segal, E., Feitelson, E., Goulden, S., Razin, E., Rein-Sapir, Y., Kagan, E., & Negev, M. (2022). Residential seismic retrofitting: Contextualizing policy packages to local circumstances. International Journal of Disaster Risk Reduction, 81, 103264. https://doi.org/10.1016/j.ijdrr.2022.103264

Stoffel, M., Bamer, F., & Markert, B. (2018). Artificial neural networks and intelligent finite elements in non-linear structural mechanics. Thin-Walled Structures, 131, 102–106. https://doi.org/10.1016/j.tws.2018.06.035

Sun, H., Burton, H. V., & Huang, H. (2021). Machine learning applications for building structural design and performance assessment: state-of-the-art review. Journal of Building Engineering, 33, 101816. https://doi.org/10.1016/j.jobe.2020.101816

Taubenböck, H. (2019). Remote sensing for the analysis of global urbanization Julius-Maximilians-Universität Würzburg.

Thaler, D., Stoffel, M., Markert, B., & Bamer, F. (2021). Machine-learning-enhanced tail end prediction of structural response statistics in earthquake engineering. Earthquake Engineering & Structural Dynamics, 50(8), 2098–2114. https://doi.org/10.1002/eqe.3432

Todorov, B. (2021). Seismic performance evaluation of reinforced concrete bridge piers considering postearthquake capacity degradation.

Zhang, Y., & Burton, H. (2019). Pattern recognition approach to assess the residual structural capacity of damaged tall buildings. Structural Safety, 78, 12–22. https://doi.org/10.1016/j.strusafe.2018.12.004

Zhang, Y., Burton, H., Sun, H., & Shokrabadi, M. (2018). A machine learning framework for assessing post-earthquake structural safety. Structural Safety, 72, 1–16. https://doi.org/10.1016/j.strusafe.2017.12.001