This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Hurwitz, J., Kirsch, D. (2018). Machine Learning for dummies. Hoboken: Wiley.Search in Google Scholar
Kameshwar, S., Padgett, J. E. (2017). Effect of vehicle bridge interaction on seismic response and fragility of bridges. Earthquake Engineering Structural Dynamics, 47(3), 697–713. DOI:10.1002/eqe.2986.Search in Google Scholar
Koutsourelakis, P. S. (2010). Assessing structural vulnerability against earthquakes using multi-dimensional fragility surfaces: A Bayesian framework. Probabilistic Engineering Mechanics, 25, 49–60. DOI: 10.1016/j.probengmech.2009.05.005.Search in Google Scholar
Yazdi, A. J., Haukaas, T., Yang, T., Gardoni, P. (2016). Multivariate fragility models for earthquake engineering. Earthquake Spectra, 32, 441–461. DOI : 10.1193/061314eqs085m.Search in Google Scholar
German, S., Brilakis, I., Desroches, R. (2012). Rapid entropy-based detection and properties measurement of concrete spalling with machine vision for post-earthquake safety assessments. Advanced Engineering Informatics, 26, 846–858. DOI : 10.1016/j.aei.2012.06.005.Search in Google Scholar
Luo, H., Paal, S., G. (2019). A Locally Weighted Machine Learning Model for Generalized Prediction of Drift Capacity in Seismic Vulnerability Assessments. Computer-Aided Civil and Infrastructure Engineering, 34 (11), 935–50. DOI : 10.1111/mice.12456.Search in Google Scholar
Aladsani, M., Burton, H., Abdullah, S., Wallace, J. (2022). Explainable Machine Learning Model for Predicting Drift Capacity of Reinforced Concrete Walls. ACI Structural Journal, 119, 191-204. DOI: 10.14359/51734484.Search in Google Scholar
Abdullah, S. A., Wallace, J. W. (2019). Drift Capacity of Reinforced Concrete Structural Walls with Special Boundary Elements. ACI Structural Journal, V. 116, No. 1, 183-194. DOI: 10.14359/51710864Search in Google Scholar
Berry, ., Parrish, ., & Eberhard, . (2004). PEER structural performance database user’s manual. Retrieved July 25, 2024, from http://nisee.berkeley.eduSearch in Google Scholar
American Concrete Institute. (2019). Building Code Requirements for Structural Concrete. ACI 318-19. Michigan.Search in Google Scholar
Pokhrel, M., Bandelt, M. J. (2019). Plastic hinge behavior and rotation capacity in reinforced ductile concrete flexural members. Engineering Structures, 200, 109699–109699. DOI: 10.1016/j.engstruct.2019.109699.Search in Google Scholar
Mpampatsikos, V., Nascimbene, R., Petrini, L. (2008). A Critical Review of the R.C. Frame Existing Building Assessment Procedure According to Eurocode 8 and Italian Seismic Code. Journal of Earthquake Engineering. 12, 52-82. DOI:10.1080/13632460801925020.Search in Google Scholar
European Committee for Standardization. (2005). Eurocode 8, Design of Structures for Earthquake Resistance, Part 3: Assessment and Retrofitting of Buildings. EN-1998-3. Brussels.Search in Google Scholar
Moehle, J., Elwood, K. (2003). Collapse performance prediction for Reinforced Concrete frame structures. Proceedings of the Pacific Conference on Earthquake Engineering.Search in Google Scholar
Paulay, T., Priestley, M.,J.,N. (1992). Seismic Design of Reinforced Concrete and Masonry Buildings. New York: Wiley.Search in Google Scholar
Scikit-Learn. (2024). LinearRegression. Retrieved July 15, 2024, from https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.htmlSearch in Google Scholar
Jakkula, V. (2006). Tutorial on Support Vector Machine (SVM). School of EECS, Washington State University. 37 (2.5), 3.Search in Google Scholar
Boswell, D., (2002). Introduction to Support Vector Machines. Department of Computer Science and Engineering University of California, San Diego. 11.Search in Google Scholar
Drumond, R. (2019). Jupyter Notebook Viewer. Retrieved July 12, 2024, from https://nbviewer.org/github/RomuloDrumond/LSSVM/blob/master/LSSVC.ipynbSearch in Google Scholar
Jijo, B., Abdulazeez, A. M. (2021). Classification based on decision tree algorithm for machine learning. Journal of Applied Science and Technology Trends, 2, 20–28. DOI: 10.38094/jastt20165.Search in Google Scholar
Uddin, S., Khan, A., Hossain, M., Moni, M., A. (2019). Comparing different supervised machine learning algorithms for disease prediction. BMC Medical Informatics and Decision Making, 19(1), 281. DOI: 10.1186/s12911-019-1004-8.Search in Google Scholar
Readthedocs.io. (2022). XGBoost Documentation — xgboost 2.1.1 documentation. Retrieved July 16, 2024, from https://xgboost.readthedocs.io/en/stable/Search in Google Scholar
Perez, F., Granger, B. (2015). Jupyter Notebook [computer software]. Berkeley : Project Jupyter.Search in Google Scholar
Scikit-Learn.org. (2024). Scikit-learn: machine learning in Python — scikit-learn 1.5.1 documentation Retrieved July 16, 2024, from https://scikit-learn.org/stable/Search in Google Scholar
Nalcin, S. (2022). StandardScaler vs. MinMaxScaler vs. RobustScaler: Which one to use for your next ML project? Retrieved July 16, 2024, from https://medium.com/@onersarpnalcin/standardscaler-vs-minmaxscalervs-robustscaler-which-one-to-use-for-your-next-ml-project-ae5b44f571b9Search in Google Scholar