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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.22 No.4
- 발행연도
- 2018.1
- 수록면
- 419 - 437 (19page)
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초록· 키워드
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The compressive strength of self-compacting concrete (SCC) containing fly ash (FA) is highly related to its constituents. The principal purpose of this paper is to investigate the efficiency of hybrid fuzzy radial basis function neural network with biogeography-based optimization (FRBFNN-BBO) for predicting the compressive strength of SCC containing FA based on its mix design i.e., cement, fly ash, water, fine aggregate, coarse aggregate, superplasticizer, and age. In this regard, biogeography-based optimization (BBO) is applied for the optimal design of fuzzy radial basis function neural network (FRBFNN) and the proposed model, implemented in a MATLAB environment, is constructed, trained and tested using 338 available sets of data obtained from 24 different published literature sources. Moreover, the artificial neural network and three types of radial basis function neural network models are applied to compare the efficiency of the proposed model. The statistical analysis results strongly showed that the proposed FRBFNN-BBO model has good performance in desirable accuracy for predicting the compressive strength of SCC with fly ash.
목차
등록된 정보가 없습니다.
참고문헌 (62)
참고문헌 신청
Bhairevi Ganesh Aiyer / 2014 / Prediction of Compressive Strength of Self-Compacting Concrete using Least Square Support Vector Machine and Relevance Vector Machine / KSCE Journal of Civil Engineering 18(6):1753~1758
Ahmed M. Ashteyat / 2018 / Predicting residual compressive strength of self-compacted concrete under various temperatures and relative humidity conditions by artificial neural networks / Computers and Concrete, An International Journal 21(1):47~54
Atici, 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
Baykasoǧlu, A. / 2009 / Prediction and multi-objective optimization of high-strength concrete parameters via soft computing approaches / Exp. Syst. Appl. 36(3):6145~6155
Bilim, C. / 2009 / Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network / Adv. Eng. Softw. 40(5):334~340
Ahmed M. Ashteyat / 2018 / Predicting residual compressive strength of self-compacted concrete under various temperatures and relative humidity conditions by artificial neural networks / Computers and Concrete, An International Journal 21(1):47~54
Atici, 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
Baykasoǧlu, A. / 2009 / Prediction and multi-objective optimization of high-strength concrete parameters via soft computing approaches / Exp. Syst. Appl. 36(3):6145~6155
Bilim, C. / 2009 / Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network / Adv. Eng. Softw. 40(5):334~340
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