Abstract
Structures often suffer damages as a result of earthquakes, potentially threatening human lives, disrupting the economy and requiring large amounts of monetary reparations. Thus, it is essential for governments to be able to rank a given population of structures according to their expected degree of damage in an earthquake, in order for them to properly allocate the available resources for prevention. In this paper, the authors present a ranking approach, based on Machine Learning (ML) algorithms for pairwise comparisons, coupled with ad hoc ranking rules. The degree of damage of several structures from the Athens 1999 earthquake, along with collected attributes of the building, were used as input. The performance of the ML classification algorithms was evaluated using the respective metrics of Precision, Recall, F1-score, Accuracy and Area Under Curve (AUC). The overall performance was evaluated using Kendall’s tau distance and by viewing the problem as a classification into bins. The obtained results were promising, outperforming currently employed engineering practices. They have shown the capabilities and potential of these models in mitigating the effects of earthquakes on society.
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References
Greek code for seismic resistant structures - EAK (2000). https://iisee.kenken.go.jp/worldlist/23_Greece/23_Greece_Code.pdf
Alam, N., Alam, M.S., Tesfamariam, S.: Buildings’ seismic vulnerability assessment methods: a comparative study. Nat. Hazards 62, 405–424 (2012)
Barbat, A.H., Carreño, M.L., Pujades, L.G., Lantada, N., Cardona, O.D., Marulanda, M.C.: Seismic vulnerability and risk evaluation methods for urban areas a review with application to a pilot area. Struct Infrastructure Eng. 6(1–2), 17–38 (2010)
Buckland, M., Gey, F.: The relationship between recall and precision. J. Am. Society Inf. Sci. 45(1), 12–19 (1994)
Cicirello, V.A.: Kendall tau sequence distance: Extending Kendall tau from ranks to sequences. arXiv preprint arXiv:1905.02752 (2019)
Code, P.: Eurocode 8: Design of structures for earthquake resistance-part 1: general rules, seismic actions and rules for buildings. European Committee for Standardization, Brussels (2005)
Cunningham, P., Delany, S.J.: k-nearest neighbour classifiers-A tutorial. ACM Comput. Surv. (CSUR) 54(6), 1–25 (2021)
Fawagreh, K., Gaber, M.M., Elyan, E.: Random forests: from early developments to recent advancements. Syst. Sci. Control Eng. An Open Access J. 2(1), 602–609 (2014)
Flach, P., Kull, M.: Precision-recall-gain curves: PR analysis done right. In: Advances in Neural Information Processing Systems 28 (2015)
Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63, 3–42 (2006)
Ghasemi, S.H., Bahrami, H., Akbari, M.: Classification of seismic vulnerability based on machine learning techniques for RC frames. J. Soft Comput. Civil Eng. (2020)
Gutiérrez, P.A., Perez-Ortiz, M., Sanchez-Monedero, J., Fernandez-Navarro, F., Hervas-Martinez, C.: Ordinal regression methods: survey and experimental study. IEEE Trans. Knowl. Data Eng. 28(1), 127–146 (2015)
Herbrich, R.: Support vector learning for ordinal regression. In: Proceedings of 9th International Conference on Neural Networks 1999, pp. 97–102 (1999)
Karabinis, A.: Calibration of Rapid Visual Screening in Reinforced Concrete Structures based on data after a near field earthquake (7.9.1999 Athens - Greece) (2004). https://www.oasp.gr/assigned_program/2385
Köppen, M.: The curse of dimensionality. In: 5th Online World Conference on Soft Computing in Industrial Applications (WSC5), vol. 1, pp. 4–8 (2000)
Kotsiantis, S.B.: Decision trees: a recent overview. Artif. Intell. Rev. 39, 261–283 (2013)
Kotsiantis, S.B., Zaharakis, I., Pintelas, P., et al.: Supervised machine learning: A review of classification techniques. Emerging Artifi. Intell. Appli. Comput. Eng. 160(1), 3–24 (2007)
Kumari, R., Srivastava, S.K.: Machine learning: A review on binary classification. Int. J. Comput. Appli. 160(7) (2017)
Lang, K., Bachmann, H.: On the seismic vulnerability of existing unreinforced masonry buildings. J. Earthquake Eng. 7(03), 407–426 (2003)
Li, L., Lin, H.T.: Ordinal regression by extended binary classification. In: Advances in Neural Information Processing Systems 19 (2006)
Liu, Y., Li, X., Kong, A.W.K., Goh, C.K.: Learning from small data: A pairwise approach for ordinal regression. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–6. IEEE (2016)
Lizundia, B., et al.: Update of fema p-154: Rapid visual screening for potential seismic hazards. In: Improving the Seismic Performance of Existing Buildings and Other Structures 2015, pp. 775–786 (2015)
Luo, H., Paal, S.G.: A locally weighted machine learning model for generalized prediction of drift capacity in seismic vulnerability assessments. Comput. Aided Civil Infrastructure Eng. 34(11), 935–950 (2019)
Marom, N.D., Rokach, L., Shmilovici, A.: Using the confusion matrix for improving ensemble classifiers. In: 2010 IEEE 26-th Convention of Electrical and Electronics Engineers in Israel, pp. 000555–000559. IEEE (2010)
Nanda, R., Majhi, D.: Review on rapid seismic vulnerability assessment for bulk of buildings. J. Institution of Eng. (India): Series A 94, 187–197 (2013)
Natekin, A., Knoll, A.: Gradient boosting machines, a tutorial. Front. Neurorobot. 7, 21 (2013)
Ningthoujam, M., Nanda, R.P.: Rapid visual screening procedure of existing building based on statistical analysis. In. J. Disaster Risk Reduct. 28, 720–730 (2018)
Pedregosa, F., et al.: Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Rahman, A., Tasnim, S.: Ensemble classifiers and their applications: a review. arXiv preprint arXiv:1404.4088 (2014)
Rosti, A., Rota, M., Penna, A.: An empirical seismic vulnerability model. Bull. Earthquake Eng., 1–27 (2022). https://doi.org/10.1007/s10518-022-01374-3
Ruggieri, S., Cardellicchio, A., Leggieri, V., Uva, G.: Machine-learning based vulnerability analysis of existing buildings. Autom. Constr. 132, 103936 (2021)
Singh, A., Prakash, B.S., Chandrasekaran, K.: A comparison of linear discriminant analysis and ridge classifier on Twitter data. In: 2016 International Conference on Computing, Communication and Automation (ICCCA), pp. 133–138. IEEE (2016)
So, Y.: A tutorial on logistic regression. SAS White Papers (1995)
Soofi, A.A., Awan, A.: Classification techniques in machine learning: applications and issues. J. Basic Appl. Sci 13, 459–465 (2017)
Tesfamariam, S., Saatcioglu, M.: Risk-based seismic evaluation of reinforced concrete buildings. Earthq. Spectra 24(3), 795–821 (2008)
Vanschoren, J.: Meta-learning: A survey. arXiv preprint arXiv:1810.03548 (2018)
Vicente, R., Parodi, S., Lagomarsino, S., Varum, H., Silva, J.M.: Seismic vulnerability and risk assessment: case study of the historic city centre of Coimbra, Portugal. Bull. Earthq. Eng. 9, 1067–1096 (2011)
Visa, S., Ramsay, B., Ralescu, A.L., Van Der Knaap, E.: Confusion matrix-based feature selection. Maics 710(1), 120–127 (2011)
Wauthier, F., Jordan, M., Jojic, N.: Efficient ranking from pairwise comparisons. In: International Conference on Machine Learning, pp. 109–117. PMLR (2013)
Yuan, Y., Wu, L., Zhang, X.: Gini-impurity index analysis. IEEE Trans. Inf. Forensics Secur. 16, 3154–3169 (2021)
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Karampinis, I., Iliadis, L. (2023). A Machine Learning Approach for Seismic Vulnerability Ranking. In: Iliadis, L., Maglogiannis, I., Alonso, S., Jayne, C., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2023. Communications in Computer and Information Science, vol 1826. Springer, Cham. https://doi.org/10.1007/978-3-031-34204-2_1
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