Prediction of the Strength of Rubberized Concrete by an Evolved Random Forest Model. (28th December 2019)

• Record Type: Journal Article
• Authors: Sun, Yuantian; Li, Guichen; Zhang, Junfei; Qian, Deyu
• Other Names: Pham Binh Thai Guest Editor.
• Is Part Of: Advances in civil engineering. Volume 2019(2019)
• Publication Date: 2019-12-28
• Subjects: Civil engineering -- Periodicals; Civil engineering; Periodicals; 624
• DOI: 10.1155/2019/5198583
• Languages: English
• ISSNs: 1687-8086

Abstract

Abstract : 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.