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: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2019/5198583 |
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| _version_ | 1832549821230088192 |
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| author | Yuantian Sun Guichen Li Junfei Zhang Deyu Qian |
| author_facet | Yuantian Sun Guichen Li Junfei Zhang Deyu Qian |
| author_sort | Yuantian Sun |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-c5388ecb4977411c87c353fa1e38125e |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-c5388ecb4977411c87c353fa1e38125e2025-02-03T06:08:31ZengWileyAdvances 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 |
| 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 |
| title | 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_short | 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 |
| url | http://dx.doi.org/10.1155/2019/5198583 |
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