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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
- 463 - 470 (8page)
이용수
초록· 키워드
오류제보하기
In the present study, soft computing i.e., machine learning techniques and regression models algorithms have earned much importance for the prediction of the various parameters in different fields of science and engineering. This paper depicts that how regression models can be implemented for the prediction of compressive strength of concrete. Three models are taken into consideration for this; they are Gaussian Process for Regression (GPR), Multi Adaptive Regression Spline (MARS) and Minimax Probability Machine Regression (MPMR). Contents of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate and age in days have been taken as inputs and compressive strength as output for GPR, MARS and MPMR models. A comparatively large set of data including 1030 normalized previously published results which were obtained from experiments were utilized. Here, a comparison is made between the results obtained from all the above mentioned models and the model which provides the best fit is established. The experimental results manifest that proposed models are robust for determination of compressive strength of concrete.
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참고문헌 (37)
참고문헌 신청
Belhouari, S.B. / 2004 / Gaussian process for nonstationary time series prediction / Comput. Stat. Data Anal. 47(4):705~714
Chen, T. / 2007 / Gaussian process regression for multivariate spectroscopic calibration / Chem. Intell. Lab. Syst. 87(1):59~71
Cheng, M.Y. / 2014 / Evolutionary multivariate adaptive regression splines for estimating shear strength in reinforced-concrete deep beams / Eng. Appl. Artif. Intell. 28:86~96
Chou, J. / 2015 / Shear strength prediction in reinforced concrete deep beams using nature-inspired metaheuristic support vector regression / J. Comput. Civil Eng. 30(1):04015002
Chou, J.S. / 2013 / Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength / Constr. Build. Mater. 49:554~563
Chen, T. / 2007 / Gaussian process regression for multivariate spectroscopic calibration / Chem. Intell. Lab. Syst. 87(1):59~71
Cheng, M.Y. / 2014 / Evolutionary multivariate adaptive regression splines for estimating shear strength in reinforced-concrete deep beams / Eng. Appl. Artif. Intell. 28:86~96
Chou, J. / 2015 / Shear strength prediction in reinforced concrete deep beams using nature-inspired metaheuristic support vector regression / J. Comput. Civil Eng. 30(1):04015002
Chou, J.S. / 2013 / Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength / Constr. Build. Mater. 49:554~563
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