Computers and Concrete

  • 제21권4호
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  • Pages.407-417
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  • 2018
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Prediction of concrete compressive strength using non-destructive test results

  • Erdal, Hamit (Institute of Social Sciences, Ataturk University) ;
  • Erdal, Mursel (Technology Faculty, Department of Civil Engineering, Gazi University) ;
  • Simsek, Osman (Technology Faculty, Department of Civil Engineering, Gazi University) ;
  • Erdal, Halil Ibrahim (Turkish Cooperation and Coordination Agency (TIKA))
  • 투고 : 2017.07.03
  • 심사 : 2017.12.13
  • 발행 : 2018.04.25
⟨ 이전 논문 다음 논문 ⟩

초록

Concrete which is a composite material is one of the most important construction materials. Compressive strength is a commonly used parameter for the assessment of concrete quality. Accurate prediction of concrete compressive strength is an important issue. In this study, we utilized an experimental procedure for the assessment of concrete quality. Firstly, the concrete mix was prepared according to C 20 type concrete, and slump of fresh concrete was about 20 cm. After the placement of fresh concrete to formworks, compaction was achieved using a vibrating screed. After 28 day period, a total of 100 core samples having 75 mm diameter were extracted. On the core samples pulse velocity determination tests and compressive strength tests were performed. Besides, Windsor probe penetration tests and Schmidt hammer tests were also performed. After setting up the data set, twelve artificial intelligence (AI) models compared for predicting the concrete compressive strength. These models can be divided into three categories (i) Functions (i.e., Linear Regression, Simple Linear Regression, Multilayer Perceptron, Support Vector Regression), (ii) Lazy-Learning Algorithms (i.e., IBk Linear NN Search, KStar, Locally Weighted Learning) (iii) Tree-Based Learning Algorithms (i.e., Decision Stump, Model Trees Regression, Random Forest, Random Tree, Reduced Error Pruning Tree). Four evaluation processes, four validation implements (i.e., 10-fold cross validation, 5-fold cross validation, 10% split sample validation & 20% split sample validation) are used to examine the performance of predictive models. This study shows that machine learning regression techniques are promising tools for predicting compressive strength of concrete.

키워드

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