Computers and Concrete

  • 제21권5호
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  • Pages.505-511
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  • 2018
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Evaluation of concrete compressive strength based on an improved PSO-LSSVM model

  • Xue, Xinhua (State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University)
  • 투고 : 2017.01.17
  • 심사 : 2018.02.20
  • 발행 : 2018.05.25
⟨ 이전 논문 다음 논문 ⟩

초록

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, PSO-LSSVM, 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.

키워드

참고문헌

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