Journal
CONSTRUCTION AND BUILDING MATERIALS
Volume 244, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.conbuildmat.2020.118271
Keywords
Recycled concrete aggregate (RCA); Modulus of elasticity (MOE); Ensemble machine learning; Random forests; And voting
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Funding
- Leonard Wood Institute (LWI)
- RE-CAST Tier-1 University Transportation Center at Missouri ST
- National Science Foundation (NSF) [CMMI: 1661609, CMMI: 1932690]
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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. (C) 2020 Elsevier Ltd. All rights reserved.
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