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Advances in Nano Research Volume 13, Number 5, November 2022 , pages 499-512 DOI: https://doi.org/10.12989/anr.2022.13.5.499 |
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Estimation of lightweight aggregate concrete characteristics using a novel stacking ensemble approach |
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Mosbeh R. Kaloop, Abidhan Bardhan, Jong Wan Hu and Mohamed Abd-Elrahman
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| Abstract | ||
| 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. | ||
| Key Words | ||
| compressive strength; dry density; extra tree regressor; lightweight concrete; prediction; stacking ensemble | ||
| Address | ||
| Mosbeh R. Kaloop: Department of Civil and Environmental Engineering, Incheon National University, Incheon, Korea/ Incheon Disaster Prevention Research Center, Incheon National University, Incheon, Korea/ Public Works Engineering Department, Mansoura University, Mansoura, Egypt Abidhan Bardhan: Department of Civil Engineering, National Institute of Technology Patna, India Jong Wan Hu: Department of Civil and Environmental Engineering, Incheon National University, Incheon, Korea/ Incheon Disaster Prevention Research Center, Incheon National University, Incheon, Korea Mohamed Abd-Elrahman: Structural Engineering Department, Mansoura University, Mansoura, Egypt | ||