SPT-CPT İlişkisinin Yapay Zeka Desteğiyle Çeşitli Zemin Tipleri İçin Araştırılması

Standart Penetrasyon Testi (SPT) ve Koni Penetrasyon Testi (CPT) zemin araştırmalarında en sık kullanılan yöntemler arasında yer almaktadır. Birçok zemin parametresi SPT ve/veya CPT ile ilişkilendirilmiştir. Bu testlerden herhangi birinin yokluğunda bir diğerinin kullanılabilmesi için SPT-CPT arasında güvenilir bir korelasyonun elde edilmesi önem arz etmektedir. Bu çalışmada literatürden yararlanılarak çeşitli zemin tipleri için SPT-N verilerine karşılık gelen CPT ile elde edilmiş uç direnci (qc) değerlerine ulaşılmıştır. SPT-N değerleri ile uç direnci (qc) verileri arasında anlamlı bir ilişkinin olup olmadığını belirlemek için varyans analizi gerçekleştirilmiştir. SPT-CPT korelasyonu için yapay sinir ağları ile simüle edilebilir ağlar oluşturularak her zemin tipi için yüksek dereceli korelasyon değerlerine sahip ayrı fonksiyonlar elde edilmiştir. Ulaşılan sonuçlar deneysel verilerle ve literatürdeki denklemlerle karşılaştırılmıştır. Böylece farklı zemin tipleri için yapay zeka desteğiyle oluşturulmuş iyi derecede korelasyon değerlerine sahip fonksiyonların yardımıyla SPT ile elde edilen sonuçların CPT ile anlamlı olarak ilişkilendirilmesine olanak sağlanmıştır. Farklı bölgelere ait çok sayıda verinin kullanılması durumunda yapay sinir ağları ile SPT-CPT korelasyonu oluşturmanın başarılı bir yöntem olacağı sonucuna varılmıştır.

Anahtar Kelimeler:

Koni Penetrasyon Testi, Standart Penetrasyon Testi, Yapay Zeka, SPT-CPT korelasyonu

Investigation of SPT-CPT Relationship for Various Soil Types with Artificial Intelligence Support

Standard Penetration Test (SPT) and Cone Penetration Test (CPT) are among the most commonly used methods in soil investigations. Many soil parameters are associated with SPT and/or CPT. It is essential to obtain a reliable correlation between SPT and CPT to use one of these tests individually. In this study, the tip resistance (qc) values obtained with CPT corresponding to SPT-N data for various soil types were reached utilizing the literature. Analysis of variance was performed to ascertain whether there was a significant relationship between SPT-N values and tip resistance (qc) data. Separate functions with high-order correlation values were obtained for each soil type by creating simulatable networks with artificial neural networks for SPT-CPT correlation. The obtained results were compared with experimental data and equations in the literature. As a result, functions in good correlation values created by the support of artificial intelligence for various soil types have provided the opportunity to correlate SPT and CPT outcomes significantly. It has been concluded that using a large number of data from various locations will enable in an effective way for generating SPT-CPT correlation with artificial neural networks.

Keywords:

Cone Penetration Test, Standard Penetration Test, Artificial intelligence, SPT-CPT correlation,

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