Gauss Süreç Regresyonu ve Destek Vektör Makineleri Kullanılarak Değerlendirilen Kendiliğinden Yerleşen Beton Davranışının Deneysel Veri İle Doğrulanması

Yıl 2023, , 379 - 388, 28.03.2023

Merve Açıkgenç Ulaş

https://doi.org/10.35234/fumbd.1237839

Öz

İnşaat Mühendisliği alanında yapı malzemelerinin özellikle betonun karışım tasarımını anlamak ve bazı özelliklerini tahmin edebilmek için makine öğrenmesi metotları sıkça kullanılmaya başlanmıştır. Bu bağlamda oldukça faydalı olan makine öğrenmesi metotları sayısız denilebilecek çeşitliliktedir. Bu çalışmada makine öğrenmesi metotlarından Gauss Süreç Regresyonu (GSR) ve Destek Vektör Makineleri (DVM), Kendiliğinden Yerleşen Beton (KYB)’nin basınç dayanımını tahmin etmek için tercih edilmiştir. Çalışmanın amacı, farklı makine öğrenmesi metotlarının beton performansını tahmin etmekteki başarılarının ispat edilmesi ve böylece bu metotların özellikle beton karışım tasarımı alanında kullanımının arttırılmasıdır. Bu amaçla, KYB bileşimini ve özelliklerini içeren deneysel veri seti ile GSR ve DVM modelleri geliştirilmiştir. Geliştirilen modellerin performansları hem birbirleri ile hem de bu alanda başarısını literatürdeki birçok çalışma ile ispat etmiş olan başka bir makine öğrenmesi metodu, Yapay Sinir Ağı ile karşılaştırılmıştır. Sonuçta, deneysel veri ile eğitilen ve doğrulanan GSR ve DVM modellerinin KYB’nin basınç dayanım performansını tahmin etmekte başarılı oldukları ortaya çıkmıştır. Çalışma sonuçlarına göre GSR bu problemdeki en başarılı metot olmuştur. GSR için deneysel veri ile modelin çıkışı arasındaki korelasyon katsayıları eğitim aşamasında 0.9888 ve test aşamasında 0.8648 olarak hesaplanmıştır.

Anahtar Kelimeler

Destek Vektör Makineleri, Gauss Süreç Regresyonu, Yapay Sinir Ağları, Kendiliğinden Yerleşen Beton, Makine Öğrenmesi

Evaluation of Self-Compacting Concrete Behavior by Using Gaussian Process Regression and Support Vector Machines via Experimental Data Validation

Öz

In the field of Civil Engineering, machine learning methods have been used frequently in order to understand the mixture design and to predict some properties of building materials, especially concrete. Machine learning methods, which are very useful in this context, can be said to be innumerable. In this study, Gaussian Process Regression (GPR) and Support Vector Machines (SVM), which are two types of machine learning methods, were preferred to estimate the compressive strength of Self Compacting Concrete (SCC). The aim of the study is to prove the success of different machine learning methods in predicting concrete performance and thus to redound the usage of the methods, especially for concrete mix design. For this purpose, GPR and SVM models were developed with the experimental data set containing the SCC mix composition and properties. The performances of the developed models were compared both with each other and with another machine learning method, Artificial Neural Network, which has proven its success with numerous studies in the literature. As a result, it was revealed that the GPR and SVM models, trained and validated with an experimental dataset, were successful in predicting the compressive strength of SCC. In addition, GSR has been the most successful method in this problem. The correlation coefficients between the experimental data and the output of the GSR model were calculated as 0.9888 in the training state and 0.8648 in the testing state.

Anahtar Kelimeler

Support Vector Machines, Gaussian Process Regression, Artificial Neural Network, Self-Compacting Concrete, Machine Learning

Kaynakça

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