吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (4): 1097-1102.doi: 10.13229/j.cnki.jdxbgxb201604013

• Orginal Article • Previous Articles     Next Articles

Prediction of concrete strength based on least square support vector machine optimized by chaotic particle swarm optimization

ZHANG Jing1, 2, LIU Xiang-dong1   

  1. 1.School of Materials Science and Engineering, Inner Mongolia University of Technology, Hohhot 010051, China;
    2.Department of Electromechanical and Heating Ventilation Engineering, Inner Mongola Technical College of Construction, Hohhot 010070,China
  • Received:2015-03-12 Online:2016-07-20 Published:2016-07-20

Abstract: In order to obtain precise prediction of concrete strength, a prediction model of concrete strength is proposed, which is based on Least Square Support Vector Machine (LSSVM) optimized by Chaotic Particle Swarm Optimization (CPSO). First, the data of concrete strength are collected and normalized. Second, LSSVM is used to model the relationship between the concrete strength and the impact factors, and CPSO algorithm is applied to search the optimal parameters of LSSVM. Finally, the prediction performance is analyzed on the concrete strength data. Results show that the proposed model can accurately describe the relationship between the concrete strength and the impact factors, and improve the prediction accuracy of the concrete strength. This model has certain practical value.

Key words: civil engineering, concrete strength, least squares support vector machine(LSSVM), chaotic particle swarm optimization(CPSO) algorithm, general test

CLC Number: 

  • TU528
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