Prediction of the Strength of Rubberized Concrete by an Evolved Random Forest Model

By Yuantian Sun, Guichen Li, Junfei Zhang, Deyu Qian. 2019

104 citations recorded by the source corpus.

Rubberized concrete (RC) has attracted more attention these years as it is an economical and environmental-friendly construction material. Normally, the uniaxial compressive strength (UCS) of RC needs to be evaluated before application. In this study, an evolutionary random forest model (BRF) combining random forest (RF) and beetle antennae search (BAS) algorithms was proposed, which can be used for establishing the relationship between UCS of RC and its key variables. A total number of 138 cases were collected from the literature to develop and validate the BRF model. The results showed that the BAS can tune the RF effectively, and therefore, the hyperparameters of RF were obtained. The proposed BRF model can accurately predict the UCS of RC with a high correlation coefficient (0.96). Furthermore, the variable importance was determined, and the results showed that the age of RC is the most significant variable, followed by water-cement ratio, fine rubber aggregate, coarse rubber aggregate, and coarse aggregate. This study provides a new method to access the strength of RC and can efficiently guide the design of RC in practice.

Subfields: Building and Construction, Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/c2e2b1cc210be77beccf7c5a8998abd1ba4f4c7f

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In Building and Construction

In Civil and Structural Engineering

Citations in the corpus

Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest (2021, methodology): “RF is an ensemble machine learning technique which has been effectively used in the prediction of uniaxial compressive strength of rubberized concrete [30].”