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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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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.20 No.6
- 발행연도
- 2017.1
- 수록면
- 627 - 634 (8page)
이용수
초록· 키워드
오류제보하기
Predicting the compressive strength of concrete is important to assess the load-carrying capacity of a structure. However, the use of blended cements to accrue the technical, economic and environmental benefits has increased the complexity of prediction models. Artificial Neural Networks (ANNs) have been used for predicting the compressive strength of ordinary Portland cement concrete, i.e., concrete produced without the addition of supplementary cementing materials. In this study, models to predict the compressive strength of blended cement concrete prepared with a natural pozzolan were developed using regression models and single- and 2-phase learning ANNs. Back-propagation (BP), Levenberg-Marquardt (LM) and Conjugate Gradient Descent (CGD) methods were used for training the ANNs. A 2-phase learning algorithm is proposed for the first time in this study for predictive modeling of the compressive strength of blended cement concrete. The output of these predictive models indicates that the use of a 2-phase learning algorithm will provide better results than the linear regression model or the traditional single-phase ANN models.
목차
등록된 정보가 없습니다.
참고문헌 (31)
참고문헌 신청
Aticin, U. / 2011 / Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network / Exp. Syst. Appl. 38(8):9609~9618
Awwad, M. T. / 2004 / Developing a forecasting model of concrete compressive strength using relevance vector machines / J. Eng. Technol. 3(2):224~229
Bingöl, A. F. / 2013 / Neural networks analysis of compressive strength of lightweight concrete after high temperatures / Mater. Des. 52(1):258~264
Cheng, M. Y. / 2012 / High-performance concrete compressive strength prediction using time-weighted evolutionary fuzzy support vector machines inference model / Automat. Constr. 28(1):106~115
Chou, J. / 2013 / Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength / Constr. Build. Mater. 49(1):554~563
Awwad, M. T. / 2004 / Developing a forecasting model of concrete compressive strength using relevance vector machines / J. Eng. Technol. 3(2):224~229
Bingöl, A. F. / 2013 / Neural networks analysis of compressive strength of lightweight concrete after high temperatures / Mater. Des. 52(1):258~264
Cheng, M. Y. / 2012 / High-performance concrete compressive strength prediction using time-weighted evolutionary fuzzy support vector machines inference model / Automat. Constr. 28(1):106~115
Chou, J. / 2013 / Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength / Constr. Build. Mater. 49(1):554~563
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