Advances in nano research

  • 제13권5호
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  • Pages.499-512
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  • 2022
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  • 2287-237X(pISSN)
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  • 2287-2388(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Estimation of lightweight aggregate concrete characteristics using a novel stacking ensemble approach

  • Kaloop, Mosbeh R. (Department of Civil and Environmental Engineering, Incheon National University) ;
  • Bardhan, Abidhan (Department of Civil Engineering, National Institute of Technology Patna) ;
  • Hu, Jong Wan (Department of Civil and Environmental Engineering, Incheon National University) ;
  • Abd-Elrahman, Mohamed (Structural Engineering Department, Mansoura University)
  • 투고 : 2022.02.09
  • 심사 : 2022.09.26
  • 발행 : 2022.11.25
⟨ 이전 논문 다음 논문 ⟩

초록

This study investigates the efficiency of ensemble machine learning for predicting the lightweight-aggregate concrete (LWC) characteristics. A stacking ensemble (STEN) approach was proposed to estimate the dry density (DD) and 28 days compressive strength (Fc-28) of LWC using two meta-models called random forest regressor (RFR) and extra tree regressor (ETR), and two novel ensemble models called STEN-RFR and STEN-ETR, were constructed. Four standalone machine learning models including artificial neural network, gradient boosting regression, K neighbor regression, and support vector regression were used to compare the performance of the proposed models. For this purpose, a sum of 140 LWC mixtures with 21 influencing parameters for producing LWC with a density less than 1000 kg/m3, were used. Based on the experimental results with multiple performance criteria, it can be concluded that the proposed STEN-ETR model can be used to estimate the DD and Fc-28 of LWC. Moreover, the STEN-ETR approach was found to be a significant technique in prediction DD and Fc-28 of LWC with minimal prediction error. In the validation phase, the accuracy of the proposed STEN-ETR model in predicting DD and Fc-28 was found to be 96.79% and 81.50%, respectively. In addition, the significance of cement, water-cement ratio, silica fume, and aggregate with expanded glass variables is efficient in modeling DD and Fc-28 of LWC.

키워드

과제정보

This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 21CFRP-C163381-01).

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