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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국계산역학회 Computers and Concrete, An International Journal Computers and Concrete, An International Journal Vol.21 No.4
- 발행연도
- 2018.1
- 수록면
- 407 - 417 (11page)
이용수
초록· 키워드
오류제보하기
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.
목차
등록된 정보가 없습니다.
참고문헌 (70)
참고문헌 신청
Altun, F. / 2008 / Predicting the compressive strength of steel fiber added lightweight concrete using neural network / Comput. Mater. Sci. 42(2):259~265
Antonaci, P. / 2010 / Monitoring evolution of compressive damage in concrete with linear and nonlinear ultrasonic methods / Cement Concrete Res. 40:1106~1113
Arif, M. / 2001 / Incorporation of experience in iterative learning controllers using locally weighted learning / Autom 37:881~888
2001 / ASTM C39, Standard Test Method for Compressive Strength of Cylindrical Concrete Specimens, Annual Book of ASTM Standards
1999 / ASTM C42, Standard Test Method for Obtaining and Testing Drilled Cores and Sawed Beams of Concrete, Annual Book of ASTM Standards
Antonaci, P. / 2010 / Monitoring evolution of compressive damage in concrete with linear and nonlinear ultrasonic methods / Cement Concrete Res. 40:1106~1113
Arif, M. / 2001 / Incorporation of experience in iterative learning controllers using locally weighted learning / Autom 37:881~888
2001 / ASTM C39, Standard Test Method for Compressive Strength of Cylindrical Concrete Specimens, Annual Book of ASTM Standards
1999 / ASTM C42, Standard Test Method for Obtaining and Testing Drilled Cores and Sawed Beams of Concrete, Annual Book of ASTM Standards
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