EPS Daneciklerinin ve/veya Cam Tozunun Killi Zeminlerin Kıvam Limitlerine Etkisi ve Limitlerin YSA ve Regresyon ile Tahmin Edilmesi

Year 2023, , 385 - 398, 01.03.2023

Ebru Akıs , Öykü Yağmur Çiğdem

https://doi.org/10.21597/jist.1173024

Cited By: 1

Abstract

Zeminlerin kıvam özellikleri, zeminlerin sınıflandırmasında ve parametrelerinin tahmin edilmesinde önemli bir araçtır. Bu çalışmanın ilk bölümünde atık malzeme ile iyileştirilen killi zeminin kıvam limitlerinde meydana gelen değişiklikler deneysel olarak incelenmiştir. Çalışmada birleştirilmiş zemin sınıflamasına göre yüksek plastisiteli kil olan bentonit kullanılmıştır. Bentonit, yalnız atık cam tozu, yalnız atık genleştirilmiş polistiren (EPS) daneleri ve her iki katkı malzemesinin farklı oranlarda kullanılmasıyla iyileştirilmiş ve likit limit ve plastik limit deneyleri yapılmıştır. Çalışmanın ikinci bölümünde ise bu çalışmada elde edilen sonuçlar ile literatürdeki benzer çalışmaların deney sonuçları kullanılarak cam tozu ve/veya EPS daneleriyle iyileştirilen zeminlerin kıvam limitleri için 65 veri derlenmiştir. Bu verilerin %80’i eğitim veri seti, %20’si doğrulama veri seti olarak kullanılmak üzere düzenlenmiştir. Çoklu lineer regresyon yöntemiyle ampirik bağıntılar, eğitim veri seti kullanılarak elde edilmiştir. Yine, aynı veri seti yapay sinir ağları yönteminde kullanılmış ve algoritma eğitilmiştir. Her iki yöntem, doğrulama veri seti ile çalıştırılmış ve sonuçlar karşılaştırılmıştır. Her iki yöntemde de eğitim ve doğrulama veri setlerinden elde edilen determinasyon katsayıları oldukça yüksek olup iyileştirilmiş killerin kıvam limitlerinin gerçeğe yakın tahmin edileceği düşünülmektedir. Ayrıca, yapay sinir ağları yöntemi ile elde edilen sonuçların seçilen veri setlerinden bağımsız olduğunu kontrol etmek amacıyla, öğrenme yöntemlerinde genellikle uygulanan bir yaklaşım olan çapraz geçerlilik testi yapılarak çalışmada kullanılan algoritmanın geçerliliği test edilmiştir. Bu çalışma sonucunda, atık cam tozu ve/veya atık EPS daneleriyle iyileştirilen killi zeminlerin kıvam limitlerinin tahmin edilmesinde kullanılmak üzere ampirik bağıntılar ve yapay sinir ağları yöntemi önerilmektedir

Keywords

İyileştirme, kıvam limitleri, lineer regresyon, YSA

Thanks

Yazarlar Dr. Öğretim Üyesi Bahram Lotfsadigh’ye katkılarından dolayı teşekkür ederler.

The Effect of EPS Beads and/or Glass Powder on Consistency Limits of Clayey Soils and The Prediction of Limits by ANN and Regression Methods

Abstract

The consistency of fine soils is an essential tool in the classification and estimation of their parameters. Firstly, the changes in the consistency limits of the clayey soil improved with the waste material were investigated experimentally. Bentonite, which was classified as a high plasticity clay according to the unified soil classification system, was improved by using only waste glass powder, only waste EPS beads and both additives at different rates. Liquid limit and plastic limit tests were carried out. Secondly, a dataset (65 data) was gathered for the consistency limits of treated soils with similar waste materials in this study and literature. 80% of the data was selected to be used as training and 20% as a test dataset. Empirical correlations were obtained with the multiple linear regression method. The same dataset was used in the artificial neural network method (ANN) and the algorithm was trained. Both methods were run with the testing dataset and the results were compared. In both methods, the determination coefficients obtained from the training and testing data sets are satisfactorily high, and it is thought that the consistency limits of the treated clays will be estimated close to the actual values. In order to check that the results obtained by the ANN method are independent of the selected data sets, cross-validation tests were performed. As a result of this study, empirical correlations and ANN methods are proposed to be used in estimating the consistency limits of clayey soils improved with mentioned waste materials.

Keywords

Treatment, consistency limits, linear regression, ANN.

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