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Super learner machine‐learning algorithms for compressive strength prediction of high performance concrete

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journal contribution
posted on 2022-07-14, 00:24 authored by Seunghye Lee, Ngoc‐Hien Nguyen, Armagan Karamanli, Jaehong Lee, Thuc VoThuc Vo

Abstract: Because the proportion between the compressive strength of high-performanceconcrete (HPC) and its composition is highly nonlinear, more advanced regres-sion methods are demanded to obtain better results. Super learner models,which are based on several ensemble methods including random forest regres-sion (RFR), an adaptive boosting (AdaBoost), gradient boosting machine(GBM), extreme gradient boosting (XGBoost), light gradient boosting machine(LightGBM), categorical gradient Boosting (CatBoost), are used to solve thiscomplicated problem. A grid search method is employed to determine the bestset of hyper-parameters of each ensemble algorithm. Two super learnermodels, which combine all six models or select the top three effective ones asthe base learners, are then proposed to develop an accurate approach to esti-mate the compressive strength of HPC. The results on four popular datasetsshow significant improvement of the proposed super learner models in termsof prediction accuracy. It also reveals that their trained models always performbetter than other methods since their errors (MAE, MSE, RMSE) are alwaysmuch lower and values ofR2are higher than those of the previous studies. Theproposed super learner models can be used to provide a reliable tool for mix-ture design optimization of the HPC. 

Funding

National Research Foundation of Korea, Grant/Award Number: NRF-2020R1A4A2002855; Ministry of Education and Science Technology

History

Publication Date

2022-07-07

Journal

Structural Concrete

Pagination

21p.

Publisher

John Wiley & Sons Ltd on behalf of International Federation for Structural Concrete

ISSN

1464-4177

Rights Statement

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in anymedium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.© 2022 The Authors.

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