Details

Title

Development of Data-mining Technique for Seismic Vulnerability Assessment

Journal title

International Journal of Electronics and Telecommunications

Yearbook

2021

Volume

vol. 67

Issue

No 2

Affiliation

Wójcik, Waldemar : Lublin Technical University, Poland ; Karmenova, Markhaba : D. Serikbayev East Kazakhstan State Technical University, Kazakhstan ; Smailova, Saule : D. Serikbayev East Kazakhstan State Technical University, Kazakhstan ; Tlebaldinova, Aizhan : S. Amanzholov East Kazakhstan State University, Kazakhstan ; Belbeubaev, Alisher : Cukurova University, Turkey

Authors

Keywords

data analysis ; seismic assessment ; clustering ; hkmeans ; random forest

Divisions of PAS

Nauki Techniczne

Coverage

261-266

Publisher

Polish Academy of Sciences Committee of Electronics and Telecommunications

Bibliography

[1] I. Riedel, Ph. Guéguen, M. D. Mura, E. Pathier, T. Leduc, J. Chanussotet, “Seismic vulnerability assessment of urban environments in moderate-to-low seismic hazard regions using association rule learning and support vector machine methods”, Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer, vol. 76, no. 2, March 2015, pp. 1111-1141, DOI: 10.1007/s11069-014-1538-0.
[2] Z. Zhang, T.-Y. Hsu, H.-H. Wei, J.-H. Chen, “Development of a Data-Mining Technique for Regional-Scale Evaluation of Building Seismic Vulnerability,” Applied Sciences, vol. 9, no. 7, April 2019, p. 1502, DOI: 10.3390/app9071502.
[3] C. S. Chen, M. Y. Cheng, Y. W. Wu, “Seismic assessment of school buildings in Taiwan using the evolutionary support vector machine inference system,” Expert Systems with Applications, vol. 39, no. 4, March 2012, pp. 4102-4110, DOI: 10.1016/j.eswa.2011.09.078.
[4] H. M. Chen, W. K. Kao, H. C. Tsai, “Genetic programming for predicting aseismic abilities of school buildings,” Engineering Applications of Artificial Intelligence, vol. 25, no. 6, Sep. 2012, pp. 1103-1113, DOI: 10.1016/j.engappai.2012.04.002
[5] W. K. Kao, H. M. Chen, J. S. Chou, “Aseismic ability estimation of school building using predictive data mining models,” Expert Systems with Applications, vol. 38, Aug. 2011, pp. 10252-10263, DOI: 10.1016/j.eswa.2011.02.059.
[6] Y. Liu, Z. Li, B. Wei, Xiaoli li, “Seismic vulnerability assessment at urban scale using data mining and GIScience technology: application to Urumqi (China),” Geomatics, Natural Hazards and Risk, vol. 10, no. 1, Jan. 2019, pp. 958-985, DOI: 10.1080/19475705.2018.1524400.
[7] X. Shang, Xibing Li, A. Morales-Esteban, G. A. Cortés, “Data field-based K-means clustering for spatio-temporal seismicity analysis and hazard assessment”, Remote Sensing, vol. 10, no. 3, March 2018, p. 461, DOI:10.3390/rs10030461.
[8] J. Ortega, G. Vasconcelos, H. Rodrigues, M. Correia, “Development of a Numerical Tool for the Seismic Vulnerability Assessment of Vernacular Architecture”, Journal of Earthquake Engineering, pp. 1-29, Sep. 2019, DOI: 10.1080/13632469.2019.1657987.
[9] G. Brando, G. De Matteis, E. Spacone, “Predictive model for the seismic vulnerability assessment of small historic centres: application to the inner Abruzzi Region in Italy”, Engineering Structures, vol. 153, Dec. 2017, pp. 81-96, DOI: 10.1016/j.engstruct.2017.10.013.
[10] C. Drago, R. Ferlito, M. Zucconiс, “Clustering of damage variables for masonry buildings measured after L’Aquila earthquake,” Sep. 2015.
[11] E. Irwansyah, Е. Winarko, “Spatial data clustering and zonation of earthquake building damage hazard area,” The European Physical Journal Conferences, 68. Feb. 2014. DOI: 10.1051/epjconf/20146800005.
[12] A. Guettiche, Ph. Gueguen, “Seismic vulnerability assessment using association rule learning: application to the city of Constantine, Algeria,” Natural Hazards, vol. 86 no. 3, Jan. 2017. doi: 10.1007/s11069-016-2739-5.
[13] I. Riedel, P. Gueguen, F. Dunand, S.Cottaz, “Macroscale vulnerability assessment of cities using association rule learning,” Seismol Res Lett, vol. 85, no. 2, pp. 295–305, 2014.
[14] D. P. Sari, D. Rosadi, A. R. Effendie, D. Danardono, “Application of Bayesian network model in determining the risk of building damage caused by earthquakes,” in 2018 International Conference on Information and Communications Technology, January 2018, pp. 131-135.
[15] D. P. Sari, D. Rosadi, A. R. Effendie, D. Danardono, “K-means and bayesian networks to determine building damage levels,” Computer Science, vol. 17, no. 2, pp. 719–727, April 2019. DOI: 10.12928/telkomnika.v17i2.11756.
[16] R. Zhang, Zh. Chen, S. Chen, J. Zheng, O. Büyüköztürk, H. Sun, “Deep long short-term memory networks for nonlinear structural seismic response prediction,” Computers & Structures, pp. 55-68, Aug. 2019.
[17] V. N. Kasyanov, V. A. Evstigneev, “Graphs in programming: processing, visualization and application,” SPb.: BHV-Petersburg, 2003.
[18] P. J. Tan, D. L. Dowe, “MML inference of decision graph with milti-way and dynamic attributes,” http://www.csse.monash.edu.au/~dld/ Publications/2003/Tan+Dowe2003_MMLDecisionGraphs.pdf.
[19] L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5-32, 2001.
[20] T. Hastie, R. Tibshirani, J. Friedman, “Chapter 15. Random Forests,” in The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer-Verlag, 2009.
[21] M. Pal, “Random forest classifier for remote sensing classification,” International Journal of Remote Sensing, vol. 26, no. 1, pp. 217–222, 2015.
[22] M. Karmenova, A. Nugumanova, A. Tlebaldinova. “Klasternyy analiz dannykh v reshenii zadach po otsenke seysmicheskoy uyazvimosti ob’yektov gorodskoy sredy,” Scientific and technical journal «Vestnik AUES», vol. 1, no. 48, 2020.
[23] M. Karmenova, A. Nugumanova, A. Tlebaldinova, A. Beldeubaev, G. Popova, A. Sedchenko, “Seismic assessment of urban buildings using data mining methods,” ICCTA’20, April 2020, pp 154–159. DOI: 10.1145/3397125.3397152.
[24] L. Breiman, R. Friedman, R. Olshen, C. Stone. “Classification and Regression Trees,” Belmont, California: Wadsworth International, 1984.
[25] J. R. Quinlan, “Simplifying decision trees,” International Journal of ManMachine Studies, vol. 27, pp. 221–234, 1987.
[26] C. P. Chistyakov, “Random forests: an overview,” Proceedings of the Karelian scientific center of the Russian Academy of Sciences, no. 1, pp. 117-136, 2013.
[27] V.F. Rodriguez-Galiano, B. Ghimire, J. Rogan, M. Chica-Olmo, J. P. Rigol-Sanchez, “An assessment of the effectiveness of a random forest classifier for land-cover classification,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 67, pp. 93-104, Jan 2012.
[28] R. Dzierżak, “Comparison of the influence of standardization and normalization of data on the effectiveness of spongy tissue texture classification,” Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, vol. 9, no. 3, pp. 66-69, Mar. 2019. https://doi.org/10.35784/iapgos.62
[29] Otchet po vyborochnomu obsledovaniyu zdaniy v ramkakh «Issledovaniya po upravleniyu riskami, svyazannymi s seysmicheskimi bedstviyami v gorode Almaty, Respublika Kazakhstan», Almaty, Feb. 2008. https://openjicareport.jica.go.jp/pdf/11961802_02.pdf.

Date

2021.05.25

Type

Article

Identifier

DOI: 10.24425/ijet.2021.135974

Source

International Journal of Electronics and Telecommunications; 2021; vol. 67; No 2; 261-266
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