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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.26 No.4
- 발행연도
- 2020.1
- 수록면
- 309 - 316 (8page)
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초록· 키워드
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The application of multi-variable adaptive regression spline (MARS) in predicting he long-term compressive strength of a concrete with various admixtures has been investigated in this study. The compressive strength of concrete specimens, which were made based on 24 different mix designs using various mineral and chemical admixtures in different curing ages have been obtained. First, The values of fly ash (FA), micro-silica (MS), water-reducing admixture (WRA), coarse and fine aggregates, cement, water, age of samples and compressive strength were defined as inputs to the model, and MARS analysis was used to model the compressive strength of concrete and to evaluate the most important parameters affecting the estimation of compressive strength of the concrete. Next, the proposed equation by the MARS method using particle swarm optimization (PSO) algorithm has been optimized to have more efficient equation from the economical point of view. The proposed model in this study predicted the compressive strength of the concrete with various admixtures with a correlation coefficient of R=0.958 rather than the measured compressive strengths within the laboratory. The final model reduced the production cost and provided compressive strength by reducing the WRA and increasing the FA and curing days, simultaneously. It was also found that due to the use of the liquid membrane-forming compounds (LMFC) for its lower cost than water spraying method (SWM) and also for the longer operating time of the LMFC having positive mechanical effects on the final concrete, the final product had lower cost and better mechanical properties.
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참고문헌 (46)
참고문헌 신청
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Agrawal, V. / 2010 / Prediction of slump in concrete using artificial neural networks / World Acad. Sci., Eng. Technol 45:25~32
2019 / ASTM C309-19, Standard Specification for Liquid Membrane-Forming Compounds for Curing Concrete / ASTM International
U. Atici / 2011 / Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network / Expert Systems with Applications 38(8):9609~9618
Cengiz Duran Atiş / 2005 / Strength properties of high-volume fly ash roller compacted and workable concrete, and influence of curing condition / Cement and Concrete Research 35(6):1112~1121
Agrawal, V. / 2010 / Prediction of slump in concrete using artificial neural networks / World Acad. Sci., Eng. Technol 45:25~32
2019 / ASTM C309-19, Standard Specification for Liquid Membrane-Forming Compounds for Curing Concrete / ASTM International
U. Atici / 2011 / Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network / Expert Systems with Applications 38(8):9609~9618
Cengiz Duran Atiş / 2005 / Strength properties of high-volume fly ash roller compacted and workable concrete, and influence of curing condition / Cement and Concrete Research 35(6):1112~1121
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