An ensemble machine learning approach for prediction and optimization of modulus of elasticity of recycled aggregate concrete

By Taihao Han, Ashfia Siddique, K. Khayat, Jie Huang, Aditya Kumar. Construction and Building Materials, 2020

216 citations recorded by the source corpus.

Abstract 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.

Subfields: Building and Construction, Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/dc49409dc46626ed189d29b0b181ae67e0b41f27