Tahmin Metotları Kullanılarak Farklı Kür Koşullarında Üretilen Ferrokrom Cürufu Esaslı Geopolimer Betonların Basınç Dayanım Tahmini

Year 2021, , 881 - 891, 15.09.2021

Yaşar Kalkan Mehmet Burhan Karakoç , Ahmet Özcan

https://doi.org/10.21205/deufmd.2021236916

Cited By: 1

Abstract

Bu çalışmada farklı kür koşullarındaki ferrokrom cürufu (FS) esaslı geopolimer betonların basınç dayanımı (CS) değerleri incelenmiştir. Öğütülmüş FS sodyum hidroksit ve sodyum silikat karışımı ile aktive edilmiştir. Geopolimer beton numunelerinin silis modülü (Ms) 1.25, 1.50 ve 1.75 olarak seçilmiştir. Aynı zamanda FS yerine %0, %10 ve %20 oranlarında silis dumanı (SF) ikame edilerek numuneler hazırlanmıştır. Böylece 9 grup geopolimer beton numunesi üretilmiştir. Farklı kür sürelerinde (24, 48, 72 ve 96 saat) ve kür sıcaklıklarında (23, 40, 60, 80 ve 100 °C), numunelerin CS değerleri belirlenmiştir. Aynı zamanda, numunelerin CS tahmini için çok katmanlı algılayıcı sinir ağı (MLPNN), aşırı öğrenme makinesi sinir ağı (ELMNN) ve M5 model ağacı modellenmiştir, tahmin ve deney sonuçları karşılaştırılmıştır. Deney sonuçlarına göre, kür süresi arttıkça genellikle CS değerleri artmıştır, fakat SF ilavesi arttıkça, CS değerleri genellikle azalmıştır. %100 FS içeren, silis modülü 1.25 olan ve 24-48-72 veya 96 saat 100°C’de kür edilen numunede en büyük CS elde edişmiştir. Test aşamasındaki MLPNN, ELMNN ve M5 model ağacının R2 değerleri sırasıyla 0.956, 0.935 ve 0.922’dir. En iyi tahmin sonucunu veren MLPNN’nin test aşamasındaki ortalama karakök hatası (RMSE) 0.723 ve normalleştirilmiş kök ortalama kare hatası (NMRSE) 26.485’tir.

Keywords

Geopolimer beton, Ferrokrom cürufu, Silis dumanı, Kür koşulları, ELMNN, MLPNN, M5 model ağacı

Supporting Institution

İnönü Üniversitesi

Project Number

2015-77

Compressive Strength Prediction of Ferrochrome Slag Based Geopolymer Concretes Produced Under Different Curing Conditions by Using Prediction Methods

Abstract

In this study, compressive strength (CS) values of ferrochrome slag (FS) based geopolymer concretes in different curing conditions were investigated. Ground FS was activated with the mixture of sodium hydroxide and sodium silicate. The silica modulus (Ms) of the geopolymer concrete samples were selected as 1.25, 1.50 and 1.75. Also, samples were prepared by substituting 0%, 10% and 20% silica fume (SF) replacement the FS. Thus, 9 groups geopolymer concrete samples were produced. The CS values of the samples were determined on different curing times (24, 48, 72 and 96 hours) and curing temperatures (23, 40, 60, 80 and 100 °C). At the same time, multilayer perceptron neural network (MLPNN), extreme learning machine neural network (ELMNN) and M5 model tree were modeled for the CS prediction of the samples, the predict and experimental results were compared. According to the experiment results, it was determined that the CS values generally increased as the curing time increased, but with the addition of SF, the CS values generally decreased. The highest CS was obtained in the sample containing 100% FS that had silica modulus of 1.25 and cured at 100 °C for 24-48-72 or 96 hours. The R2 values of MLPNN, ELMNN and M5 model tree in testing phase were 0.956, 0.935 and 0.922, respectively. MLPNN, the model that gave the best predict result, had root mean square error (RMSE) of 0.723 and normalized root mean square error (NMRSE) of 26.485 in testing.

Keywords

Geopolymer concrete, Ferrochrome slag, Silica fume, Curing condition, ELMNN, MLPNN, M5 model tree

Project Number

2015-77

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