VERİ MADENCİLİĞİ İLE BETONARME KONUT BİNALARINDA DEPREM KUVVETİ ANALİZİ

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.

Anahtar Kelimeler:

Veri madenciliği, Makine öğrenmesi, Deprem, Deprem yönetmeliği, Betonarme

EARTHQUAKE FORCE ANALYSIS FOR REINFORCEMENT RESIDENTIAL BUILDINGS WITH DATA MINING

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.

Keywords:

Data mining, Machine learning, Earthquake, Earthquake code, Reinforced concrete,

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