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.


Civil, Architectural and Environmental Engineering

Second Department

Electrical and Computer Engineering

Third Department

Materials Science and Engineering

Research Center/Lab(s)

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


Experimental and computational tasks, described in this study, were conducted in the Advanced Construction Materials Laboratory (AMCL) and the Materials Research Center (MRC), respectively, of Missouri S&T. Funding for this study was provided by the Leonard Wood Institute (LWI), RE-CAST Tier-1 University Transportation Center at Missouri S&T, and the National Science Foundation (NSF; CMMI: 1661609 and CMMI: 1932690).

Keywords and Phrases

Ensemble machine learning; Modulus of elasticity (MOE); Random forests; Recycled concrete aggregate (RCA)

International Standard Serial Number (ISSN)

0950-0618; 1879-0526

Document Type

Article - Journal

Document Version


File Type





© 2020 Elsevier, All rights reserved.

Publication Date

30 May 2020