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Prediction of Compressive Strength and Elastic Modulus for Recycled Aggregate Concrete Based on AutoGluon

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  • Chenxi Lin

    (Key Laboratory for Special Area Highway Engineering of Ministry of Education, Xi’an 710064, China
    Chang’an Dublin International College of Transportation, Chang’an University, Xi’an 710064, China
    These authors contributed equally to this work.)

  • Yidan Sun

    (Key Laboratory for Special Area Highway Engineering of Ministry of Education, Xi’an 710064, China
    Chang’an Dublin International College of Transportation, Chang’an University, Xi’an 710064, China
    These authors contributed equally to this work.)

  • Wenxiu Jiao

    (Key Laboratory for Special Area Highway Engineering of Ministry of Education, Xi’an 710064, China
    School of Highway, Chang’an University, Xi’an 710064, China)

  • Jiajie Zheng

    (Key Laboratory for Special Area Highway Engineering of Ministry of Education, Xi’an 710064, China
    Chang’an Dublin International College of Transportation, Chang’an University, Xi’an 710064, China)

  • Zhijuan Li

    (Key Laboratory for Special Area Highway Engineering of Ministry of Education, Xi’an 710064, China
    Chang’an Dublin International College of Transportation, Chang’an University, Xi’an 710064, China)

  • Shujun Zhang

    (Key Laboratory for Special Area Highway Engineering of Ministry of Education, Xi’an 710064, China
    Chang’an Dublin International College of Transportation, Chang’an University, Xi’an 710064, China)

Abstract

While the civil construction industry brings great convenience to life, the large amount of waste concrete also poses a significant problem of construction waste disposal. As one of the effective ways to utilize waste concrete, recycled aggregate concrete (RAC) can improve the environment while reducing the consumption of construction materials. This study aims to use AutoGluon (AG), an automated machine learning platform, to predict both the compressive strength and elastic modulus of RAC. Then the performance of AG is compared with traditional empirical formulas and multiple linear regression models. The determination coefficient ( R 2 ) is chosen as one of the evaluation standards for predicting values. The results demonstrate that the WeightedEnsemble model of AG performed best in predicting both the compressive strength and elastic modulus, which provides a new method for the rapid and accurate prediction of the properties of RAC in engineering construction.

Suggested Citation

  • Chenxi Lin & Yidan Sun & Wenxiu Jiao & Jiajie Zheng & Zhijuan Li & Shujun Zhang, 2023. "Prediction of Compressive Strength and Elastic Modulus for Recycled Aggregate Concrete Based on AutoGluon," Sustainability, MDPI, vol. 15(16), pages 1-40, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:16:p:12345-:d:1216809
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