EARTHQUAKE FORCE ANALYSIS FOR REINFORCEMENT RESIDENTIAL BUILDINGS WITH DATA MINING
Yıl 2022,
Cilt: 30 Sayı: 2, 260 - 269, 18.08.2022
Tarkan Karaçay
,
Tolga Açıkgöz
Öz
This study is about the analysis of horizontal design earthquake loads acting on reinforced concrete residential buildings without shear walls using data mining methods and the prediction of loads using machine learning methods. For this purpose, a data set was created by using the equivalent earthquake load method according to the Building Earthquake Code of Turkey (2018). Data mining methods such as feature selection, detection and removing of outlier values, dimensionality reduction were used on the created data set, and how the results changed with which methods were discussed. The results revealed that short-period spectral acceleration coefficient taken from AFAD map (SS) and total building height (HN) are not required for successful prediction of earthquake force acting on reinforced concrete buildings without shear wall with machine learning methods.
Kaynakça
- Azimi, S., Azhdary Moghaddam, M., & Hashemi Monfared, S. A. (2018). Anomaly Detection and Reliability Analysis of Groundwater by Crude Monte Carlo and Importance Sampling Approaches. Water Resources Management, 32(14), 4447–4467. https://doi.org/10.1007/s11269-018-2029-y
- Cetinkaya, I. H. (2021). World saw 13,654 quakes of magnitude 4 or above in 2020.
- Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785
- Contreras, D., Wilkinson, S., & James, P. (2021). Earthquake Reconnaissance Data Sources, a Literature Review. Earth, 2(4), 1006–1037. https://doi.org/10.3390/earth2040060
- Falcone, R., Lima, C., & Martinelli, E. (2020). Soft computing techniques in structural and earthquake engineering: a literature review. Engineering Structures, 207(Kasım 2019), 110269. https://doi.org/10.1016/j.engstruct.2020.110269
- Gazete, R. (2021). Planlı Alanlar İmar Yönetmeliği.
- Geiß, C., & Taubenböck, H. (2013). Remote sensing contributing to assess earthquake risk: from a literature review towards a roadmap. Natural Hazards, 68(1), 7–48. https://doi.org/10.1007/s11069-012-0322-2
- Goswami, S., Chakraborty, S., Ghosh, S., Chakrabarti, A., & Chakraborty, B. (2018). A review on application of data mining techniques to combat natural disasters. Ain Shams Engineering Journal, 9(3), 365–378. https://doi.org/10.1016/j.asej.2016.01.012
- Kahandawa, K. A. R. V. D., Domingo, N. D., Park, K. S., & Uma, S. R. (2018). Earthquake damage estimation systems: Literature review. Procedia Engineering, 212, 622–628. https://doi.org/10.1016/j.proeng.2018.01.080
- Karaçay, T. (t.y.-a). TBDY 2018 - Deprem Kuvveti (Konut Binaları). (2022, 14 Şubat), Erişim adresi: https://www.kaggle.com/tarkankaraay/tbdy-2018-deprem-kuvveti-konut-binalari?select=Veri_Seti_1.xlsx
- Karaçay, T. (t.y.-b). TBDY 2018 - Deprem Kuvveti (Konut Binaları). (2022, 14 Şubat), Erişim adresi: https://www.kaggle.com/tarkankaraay/tbdy-2018-deprem-kuvveti-konut-binalari?select=AFAD_ss_s1.xlsx
- Ortega, J., Vasconcelos, G., Rodrigues, H., Correia, M., & Lourenço, P. B. (2017). Traditional earthquake resistant techniques for vernacular architecture and local seismic cultures: A literature review. Journal of Cultural Heritage, 27, 181–196. https://doi.org/10.1016/j.culher.2017.02.015
- Otari, G. V., & Kulkarni, D. R. V. (2012). A Review of Application of Data Mining in Earthquake Prediction. International Journal of Computer Science and Information Technologies, 3(2), 3570–3574. Erişim adresi: http://www.ijcsit.com/docs/Volume 3/Vol3Issue2/ijcsit2012030258.pdf
- Plevris, V., Bakas, N., Markeset, G., & Bellos, J. (2017). Literature Review of Masonry Structures Under Earthquake Excitation Utilizing Machine Learning Algorithms. Proceedings of the 6th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COMPDYN 2015), 1, 2685–2694. Athens: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece. https://doi.org/10.7712/120117.5598.18688
- Sayad, S. (t.y.). Support Vector Machine - Regression (SVR). (2022, 20 Ocak), Erişim adresi: https://www.saedsayad.com/support_vector_machine_reg.htm
- Türkoğlu, N. (2001). Türkiye’nin Yüzölçümü ve Nüfusunun Deprem Bölgelerine Dağılışı. Türkiye Coğrafyası Araştırma ve Uygulama Merkezi Dergisi.
VERİ MADENCİLİĞİ İLE BETONARME KONUT BİNALARINDA DEPREM KUVVETİ ANALİZİ
Yıl 2022,
Cilt: 30 Sayı: 2, 260 - 269, 18.08.2022
Tarkan Karaçay
,
Tolga Açıkgöz
Öz
Bu çalışma perdesiz betonarme konut binalarına etkiyen yatay tasarım deprem yükünün veri madenciliği yöntemleri ile analiz edilmesi ve makine öğrenmesi yöntemleri ile tahminlenmesi konusundadır. Bu amaçla Türkiye Bina Deprem Yönetmeliği’ne (2018) göre eşdeğer deprem yükü yöntemi kullanılarak veri seti oluşturulmuştur. Oluşturulan veri seti üzerinde öznitelik seçimi, uç değerlerin tespit edilmesi ve silinmesi, boyut azaltma gibi veri madenciliği yöntemleri kullanılmış hangi yöntemlerle sonuçların nasıl değiştiği tartışılmıştır. Sonuçlar perdesiz betonarme binalara etkiyen deprem kuvvetinin makine öğrenmesi yöntemleri ile başarılı tahmini için kısa periyot harita spektral ivme katsayısının (SS) ve bina toplam yüksekliğinin (HN) gerekmediğini ortaya koymuştur.
Kaynakça
- Azimi, S., Azhdary Moghaddam, M., & Hashemi Monfared, S. A. (2018). Anomaly Detection and Reliability Analysis of Groundwater by Crude Monte Carlo and Importance Sampling Approaches. Water Resources Management, 32(14), 4447–4467. https://doi.org/10.1007/s11269-018-2029-y
- Cetinkaya, I. H. (2021). World saw 13,654 quakes of magnitude 4 or above in 2020.
- Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785
- Contreras, D., Wilkinson, S., & James, P. (2021). Earthquake Reconnaissance Data Sources, a Literature Review. Earth, 2(4), 1006–1037. https://doi.org/10.3390/earth2040060
- Falcone, R., Lima, C., & Martinelli, E. (2020). Soft computing techniques in structural and earthquake engineering: a literature review. Engineering Structures, 207(Kasım 2019), 110269. https://doi.org/10.1016/j.engstruct.2020.110269
- Gazete, R. (2021). Planlı Alanlar İmar Yönetmeliği.
- Geiß, C., & Taubenböck, H. (2013). Remote sensing contributing to assess earthquake risk: from a literature review towards a roadmap. Natural Hazards, 68(1), 7–48. https://doi.org/10.1007/s11069-012-0322-2
- Goswami, S., Chakraborty, S., Ghosh, S., Chakrabarti, A., & Chakraborty, B. (2018). A review on application of data mining techniques to combat natural disasters. Ain Shams Engineering Journal, 9(3), 365–378. https://doi.org/10.1016/j.asej.2016.01.012
- Kahandawa, K. A. R. V. D., Domingo, N. D., Park, K. S., & Uma, S. R. (2018). Earthquake damage estimation systems: Literature review. Procedia Engineering, 212, 622–628. https://doi.org/10.1016/j.proeng.2018.01.080
- Karaçay, T. (t.y.-a). TBDY 2018 - Deprem Kuvveti (Konut Binaları). (2022, 14 Şubat), Erişim adresi: https://www.kaggle.com/tarkankaraay/tbdy-2018-deprem-kuvveti-konut-binalari?select=Veri_Seti_1.xlsx
- Karaçay, T. (t.y.-b). TBDY 2018 - Deprem Kuvveti (Konut Binaları). (2022, 14 Şubat), Erişim adresi: https://www.kaggle.com/tarkankaraay/tbdy-2018-deprem-kuvveti-konut-binalari?select=AFAD_ss_s1.xlsx
- Ortega, J., Vasconcelos, G., Rodrigues, H., Correia, M., & Lourenço, P. B. (2017). Traditional earthquake resistant techniques for vernacular architecture and local seismic cultures: A literature review. Journal of Cultural Heritage, 27, 181–196. https://doi.org/10.1016/j.culher.2017.02.015
- Otari, G. V., & Kulkarni, D. R. V. (2012). A Review of Application of Data Mining in Earthquake Prediction. International Journal of Computer Science and Information Technologies, 3(2), 3570–3574. Erişim adresi: http://www.ijcsit.com/docs/Volume 3/Vol3Issue2/ijcsit2012030258.pdf
- Plevris, V., Bakas, N., Markeset, G., & Bellos, J. (2017). Literature Review of Masonry Structures Under Earthquake Excitation Utilizing Machine Learning Algorithms. Proceedings of the 6th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COMPDYN 2015), 1, 2685–2694. Athens: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece. https://doi.org/10.7712/120117.5598.18688
- Sayad, S. (t.y.). Support Vector Machine - Regression (SVR). (2022, 20 Ocak), Erişim adresi: https://www.saedsayad.com/support_vector_machine_reg.htm
- Türkoğlu, N. (2001). Türkiye’nin Yüzölçümü ve Nüfusunun Deprem Bölgelerine Dağılışı. Türkiye Coğrafyası Araştırma ve Uygulama Merkezi Dergisi.