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Contribution of Machine Learning Methods to the Construction Industry: Prediction of Compressive Strength
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.
Keywords
References
- Machine [1] Yeh IC. “UCI Compressive Repository: Concrete
- http://archive.ics.uci.edu/ml/datasets/Concrete+Compr
- essive+Strength (20.03.2014). Strength Data Set” [2] Yeh IC. “Modeling of Strength of High-Performance
- Concrete Using Artificial Neural Networks”. Cement and
- Concrete Research, 28(12), 1797-1808, 1998. [3] Hong-Guang N, Ji-Zong W. “Prediction of Compressive
- Strength of Concrete by Neural Networks”. Cement and
- Concrete Research, 30(8), 1245-1250, 2000. [4] Seyhan AT, Tayfur G, Karakurt M, Tanoğlu M. “Artificial
- Neural Network (ANN) Prediction of Compressive
Details
Primary Language
English
Subjects
-
Journal Section
-
Authors
Publication Date
July 1, 2015
Submission Date
July 6, 2015
Acceptance Date
-
Published in Issue
Year 2015 Volume: 21 Number: 3
APA
Erdal, H. (2015). Contribution of Machine Learning Methods to the Construction Industry: Prediction of Compressive Strength. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 21(3), 109-114. https://doi.org/10.5505/pajes.2014.26121
AMA
1.Erdal H. Contribution of Machine Learning Methods to the Construction Industry: Prediction of Compressive Strength. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2015;21(3):109-114. doi:10.5505/pajes.2014.26121
Chicago
Erdal, Hamit. 2015. “Contribution of Machine Learning Methods to the Construction Industry: Prediction of Compressive Strength”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 21 (3): 109-14. https://doi.org/10.5505/pajes.2014.26121.
EndNote
Erdal H (July 1, 2015) Contribution of Machine Learning Methods to the Construction Industry: Prediction of Compressive Strength. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 21 3 109–114.
IEEE
[1]H. Erdal, “Contribution of Machine Learning Methods to the Construction Industry: Prediction of Compressive Strength”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 21, no. 3, pp. 109–114, July 2015, doi: 10.5505/pajes.2014.26121.
ISNAD
Erdal, Hamit. “Contribution of Machine Learning Methods to the Construction Industry: Prediction of Compressive Strength”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 21/3 (July 1, 2015): 109-114. https://doi.org/10.5505/pajes.2014.26121.
JAMA
1.Erdal H. Contribution of Machine Learning Methods to the Construction Industry: Prediction of Compressive Strength. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2015;21:109–114.
MLA
Erdal, Hamit. “Contribution of Machine Learning Methods to the Construction Industry: Prediction of Compressive Strength”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 21, no. 3, July 2015, pp. 109-14, doi:10.5505/pajes.2014.26121.
Vancouver
1.Hamit Erdal. Contribution of Machine Learning Methods to the Construction Industry: Prediction of Compressive Strength. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2015 Jul. 1;21(3):109-14. doi:10.5505/pajes.2014.26121