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Journals / Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi / 2015 / Cilt: 21 Sayı: 3

Contribution of Machine Learning Methods to the Construction Industry: Prediction of Compressive Strength

Makine Öğrenmesi Yöntemlerinin İnşaat Sektörüne Katkısı: Basınç Dayanımı Tahminlemesi

Hamit Erdal

Pages
109–114
Publication Date
2015-07-01
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Abstract

Highly accurate prediction of high performance concrete (HPC) compressive strength is very important issue. In recent years, a variety of modeling approaches and methodologies have been applied to predict HPC’s compressive strength from a wide range of variables, with different ratios of success. In this study, an appropriate machine learning method, using different mixing ratios for the prediction of compressive strength of HPC, is investigated. In recent years, rather developing machine learning methods; Artificial Neural Networks (ANN) and Support Vector Machines (SVM)’s applicabilities for the prediction, handled in this study, are being investigated and extremely high results were obtained. In this paper, it’s obtained that prediction success of SVM has been found more satisfactory than ANN's. It is concluded that the SVM’s can be used effectively as an alternative method by research labs and the concrete firms for predicting the strength.

Özet

Yüksek performanslı beton (high performance concrete, HPC)’un eksenel basınç dayanımının yüksek doğrulukla tahmini son derece önemli bir konudur. Geçtiğimiz yıllarda, çeşitli gelişmiş modelleme yaklaşımları ve metodolojileri kullanılarak farklı başarı oranları ile HPC basınç dayanımı tahminlemeleri uygulanmıştır. Bu çalışmada farklı karışım oranları kullanılarak HPC’lerin eksenel basınç dayanımının tahmininde uygun bir makine öğrenmesi yöntemi araştırılmıştır. Son yıllarda makine öğrenmesinde oldukça gelişmekte olan Yapay Sinir Ağları (YSA) ve Destek Vektör Makineleri (DVM)’nin bu tahminde uygulanabilirliği incenmiş ve son derece yüksek tahmin sonuçları elde edilmiştir. Bu çalışmada DVM’lerin tahmin başarısının YSA’lara oranla daha tatmin edici sonuçlar verdiği görülmüştür. DVM yönteminin araştırma laboratuvarları ve beton firmaları tarafından dayanım tahmininde alternatif bir yöntem olarak etkin bir şekilde kullanılabileceği sonucuna varılmıştır.

Keywords
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Cite This Article

Erdal, H. (2015). Contribution of Machine Learning Methods to the Construction Industry: Prediction of Compressive Strength. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 21 Sayı: 3, 109–114. https://doi.org/10.5505/pajes.2014.26121

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