An Ensemble Machine Learning Approach for Prediction and Optimization of Modulus of Elasticity of Recycled Aggregate Concrete
This paper presents an ensemble machine learning (ML) model for prediction of modulus of elasticity (MOE) of concrete formulated using recycled concrete aggregate (RCA), in relation to features of its mixture design (e.g., physiochemical characteristics of RCA). The ensemble ML model's prediction performance was compared with five commonly-used ML models. It is shown that the ensemble ML model unfailingly produces more accurate predictions compared to standalone models. To demonstrate the ability of the ensemble ML model to go beyond MOE predictions, the model was used to develop optimal mixture designs for RCA concretes that satisfy imposed target MOE.
T. Han et al., "An Ensemble Machine Learning Approach for Prediction and Optimization of Modulus of Elasticity of Recycled Aggregate Concrete," Construction and Building Materials, vol. 244, article no. 118271, Elsevier, May 2020.
The definitive version is available at https://doi.org/10.1016/j.conbuildmat.2020.118271
Civil, Architectural and Environmental Engineering
Electrical and Computer Engineering
Materials Science and Engineering
Re-Cast Tier1 University Transportation Center
Second Research Center/Lab
Center for Research in Energy and Environment (CREE)
Third Research Center/Lab
Intelligent Systems Center
Keywords and Phrases
Ensemble machine learning; Modulus of elasticity (MOE); Random forests; Recycled concrete aggregate (RCA)
International Standard Serial Number (ISSN)
Article - Journal
© 2020 Elsevier, All rights reserved.
30 May 2020