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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.23 No.4
- 발행연도
- 2019.1
- 수록면
- 255 - 265 (11page)
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초록· 키워드
오류제보하기
The potential of using genetic programming to predict engineering data has caught the attention of researchers in recent years. The present paper utilized weighted genetic programming (WGP), a derivative model of genetic programming (GP), to model the compressive strength of concrete. The calculation results of Abrams’ laws, which are used as the design codes for calculating the compressive strength of concrete, were treated as the inputs for the genetic programming model. Therefore, knowledge of the Abrams’ laws, which is not a factor of influence on common data-based learning approaches, was considered to be a potential factor affecting genetic programming models. Significant outcomes of this work include: 1) the employed design codes positively affected the prediction accuracy of modeling the compressive strength of concrete; 2) a new equation was suggested to replace the design code for predicting concrete strength; and 3) common data-based learning approaches were evolved into knowledge-based learning approaches using historical data and design codes.
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참고문헌 (26)
참고문헌 신청
Babanajad, S. K. / 2013 / Numerical modeling of concrete strength under multiaxial confinement pressures using linear genetic programming / Autom. Constr 36:136~144
Babu, K. G. / 1996 / Efficiency of fly ash in concrete with age / Cement Concrete Res 26(3):465~474
Baykasoglu, A. / 2008 / Prediction of compressive and tensile strength of limestone via genetic programming / Exp. Syst. Appl 35(1-2):111~123
Baykasoglu, A. / 2009 / Prediction and multi-objective optimization of high-strength concrete parameters via soft computing approaches / Exp. Syst. Appl 36(3):6145~6155
Behzad, M. / 2009 / Generalization performance of support vector machines and neural networks in runoff modeling / Exp. Syst. Appl 36(4):7624~7629
Babu, K. G. / 1996 / Efficiency of fly ash in concrete with age / Cement Concrete Res 26(3):465~474
Baykasoglu, A. / 2008 / Prediction of compressive and tensile strength of limestone via genetic programming / Exp. Syst. Appl 35(1-2):111~123
Baykasoglu, A. / 2009 / Prediction and multi-objective optimization of high-strength concrete parameters via soft computing approaches / Exp. Syst. Appl 36(3):6145~6155
Behzad, M. / 2009 / Generalization performance of support vector machines and neural networks in runoff modeling / Exp. Syst. Appl 36(4):7624~7629
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