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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 국제구조공학회 Steel and Composite Structures, An International Journal Steel and Composite Structures, An International Journal Vol.45 No.6
- 발행연도
- 2022.12
- 수록면
- 877 - 894 (18page)
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초록· 키워드
오류제보하기
Geopolymer concrete ( ) has emerged as a feasible choice for construction materials as a result of the environmental issues associated with the production of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete to help reduce 2 emissions in the construction industry. The compressive strength ( ) of is predicted using artificial intelligence approaches in the present study when ground granulated blast-furnace slag ( ) is substituted with natural zeolite ( ), silica fume ( ), and varying concentrations. For this purpose, two machine learning methods multi-layer perceptron ( ) and radial basis function ( ) were considered and hybridized with arithmetic optimization algorithm ( ), and grey wolf optimization algorithm ( ). According to the results, all methods performed very well in predicting the of . The proposed − might be identified as the outperformed framework, although other methodologies ( − , − , and − ) were also reliable in the of forecasting process.
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