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

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) combin...

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Main Authors:	Yuantian Sun, Guichen Li, Junfei Zhang, Deyu Qian
Format:	Article
Language:	English
Published:	Hindawi Limited 2019-01-01
Series:	Advances in Civil Engineering
Online Access:	http://dx.doi.org/10.1155/2019/5198583

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record_format	Article
spelling	doaj-c5388ecb4977411c87c353fa1e38125e2020-11-25T02:26:24ZengHindawi LimitedAdvances in Civil Engineering1687-80861687-80942019-01-01201910.1155/2019/51985835198583Prediction of the Strength of Rubberized Concrete by an Evolved Random Forest ModelYuantian Sun0Guichen Li1Junfei Zhang2Deyu Qian3School of Mines, Key Laboratory of Deep Coal Resource Mining, Ministry of Education of China, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mines, Key Laboratory of Deep Coal Resource Mining, Ministry of Education of China, China University of Mining and Technology, Xuzhou 221116, ChinaDepartment of Civil, Environmental and Mining Engineering, The University of Western Australia, Perth 6009, AustraliaSchool of Mines, Key Laboratory of Deep Coal Resource Mining, Ministry of Education of China, China University of Mining and Technology, Xuzhou 221116, ChinaRubberized 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.http://dx.doi.org/10.1155/2019/5198583
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Yuantian Sun Guichen Li Junfei Zhang Deyu Qian
spellingShingle	Yuantian Sun Guichen Li Junfei Zhang Deyu Qian Prediction of the Strength of Rubberized Concrete by an Evolved Random Forest Model Advances in Civil Engineering
author_facet	Yuantian Sun Guichen Li Junfei Zhang Deyu Qian
author_sort	Yuantian Sun
title	Prediction of the Strength of Rubberized Concrete by an Evolved Random Forest Model
title_short	Prediction of the Strength of Rubberized Concrete by an Evolved Random Forest Model
title_full	Prediction of the Strength of Rubberized Concrete by an Evolved Random Forest Model
title_fullStr	Prediction of the Strength of Rubberized Concrete by an Evolved Random Forest Model
title_full_unstemmed	Prediction of the Strength of Rubberized Concrete by an Evolved Random Forest Model
title_sort	prediction of the strength of rubberized concrete by an evolved random forest model
publisher	Hindawi Limited
series	Advances in Civil Engineering
issn	1687-8086 1687-8094
publishDate	2019-01-01
description	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.
url	http://dx.doi.org/10.1155/2019/5198583
work_keys_str_mv	AT yuantiansun predictionofthestrengthofrubberizedconcretebyanevolvedrandomforestmodel AT guichenli predictionofthestrengthofrubberizedconcretebyanevolvedrandomforestmodel AT junfeizhang predictionofthestrengthofrubberizedconcretebyanevolvedrandomforestmodel AT deyuqian predictionofthestrengthofrubberizedconcretebyanevolvedrandomforestmodel
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Prediction of the Strength of Rubberized Concrete by an Evolved Random Forest Model

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