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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.5
- 발행연도
- 2018.1
- 수록면
- 505 - 511 (7page)
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초록· 키워드
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This paper investigates the potential of a hybrid model which combines the least squares support vector machine (LSSVM) and an improved particle swarm optimization (IMPSO) techniques for prediction of concrete compressive strength. A modified PSO algorithm is employed in determining the optimal values of LSSVM parameters to improve the forecasting accuracy. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed IMPSO-LSSVM model. Further, predictions from five models (the IMPSO-LSSVM, PSOLSSVM, genetic algorithm (GA) based LSSVM, back propagation (BP) neural network, and a statistical model) were compared with the experimental data. The results show that the proposed IMPSO-LSSVM model is a feasible and efficient tool for predicting the concrete compressive strength with high accuracy.
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참고문헌 (23)
참고문헌 신청
Ali Demir / 2015 / Prediction of Hybrid fibre-added concrete strength using artificial neural networks / Computers and Concrete, An International Journal 15(4):503~514
Bilgehan, M. / 2011 / A comparative study for the concrete compressive strength estimation using neural network and neuro-fuzzy modeling approaches / Nondestr. Test. Eval. 26(1):35~55
Cai, Z.L. / 2016 / Prediction of landslide displacement based GA-LSSVM with multiple factors / B. Eng.Geol. Environ. 75(2):637~646
Chopra, P. / 2016 / Prediction of compressive strength of concrete using artificial neural network and genetic programming / Adv. Mater. Sci. Eng. :1~10
Dibike, Y. / 2001 / Model induction with support vector machines: introduction and applications / J. Comput. Civil Eng. 15(3):208~216
Bilgehan, M. / 2011 / A comparative study for the concrete compressive strength estimation using neural network and neuro-fuzzy modeling approaches / Nondestr. Test. Eval. 26(1):35~55
Cai, Z.L. / 2016 / Prediction of landslide displacement based GA-LSSVM with multiple factors / B. Eng.Geol. Environ. 75(2):637~646
Chopra, P. / 2016 / Prediction of compressive strength of concrete using artificial neural network and genetic programming / Adv. Mater. Sci. Eng. :1~10
Dibike, Y. / 2001 / Model induction with support vector machines: introduction and applications / J. Comput. Civil Eng. 15(3):208~216
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