Machine Learning Models for Ecofriendly Optimum Design of Reinforced Concrete Columns
CO<sub>2</sub> emission is one of the biggest environmental problems and contributes to global warming. The climatic changes due to the damage to nature is triggering a climate crisis globally. To prevent a possible climate crisis, this research proposes an engineering design solution to...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/7/4117 |
| _version_ | 1797608403490570240 |
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| author | Yaren Aydın Gebrail Bekdaş Sinan Melih Nigdeli Ümit Isıkdağ Sanghun Kim Zong Woo Geem |
| author_facet | Yaren Aydın Gebrail Bekdaş Sinan Melih Nigdeli Ümit Isıkdağ Sanghun Kim Zong Woo Geem |
| author_sort | Yaren Aydın |
| collection | DOAJ |
| description | CO<sub>2</sub> emission is one of the biggest environmental problems and contributes to global warming. The climatic changes due to the damage to nature is triggering a climate crisis globally. To prevent a possible climate crisis, this research proposes an engineering design solution to reduce CO<sub>2</sub> emissions. This research proposes an optimization-machine learning pipeline and a set of models trained for the prediction of the design variables of an ecofriendly concrete column. In this research, the harmony search algorithm was used as the optimization algorithm, and different regression models were used as predictive models. Multioutput regression is applied to predict the design variables such as section width, height, and reinforcement area. The results indicated that the random forest algorithm performed better than all other machine learning algorithms that have also achieved high accuracy. |
| first_indexed | 2024-03-11T05:43:54Z |
| format | Article |
| id | doaj.art-87ba5c6bc0334e70a5611a606b381fe6 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:43:54Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-87ba5c6bc0334e70a5611a606b381fe62023-11-17T16:15:45ZengMDPI AGApplied Sciences2076-34172023-03-01137411710.3390/app13074117Machine Learning Models for Ecofriendly Optimum Design of Reinforced Concrete ColumnsYaren Aydın0Gebrail Bekdaş1Sinan Melih Nigdeli2Ümit Isıkdağ3Sanghun Kim4Zong Woo Geem5Department of Civil Engineering, Istanbul University-Cerrahpaşa, 34320 Istanbul, TurkeyDepartment of Civil Engineering, Istanbul University-Cerrahpaşa, 34320 Istanbul, TurkeyDepartment of Civil Engineering, Istanbul University-Cerrahpaşa, 34320 Istanbul, TurkeyDepartment of Informatics, Mimar Sinan Fine Arts University, 34427 Istanbul, TurkeyDepartment of Civil and Environmental Engineering, Temple University, Philadelphia, PA 19122, USADepartment of Smart City & Energy, Gachon University, Seongnam 13120, Republic of KoreaCO<sub>2</sub> emission is one of the biggest environmental problems and contributes to global warming. The climatic changes due to the damage to nature is triggering a climate crisis globally. To prevent a possible climate crisis, this research proposes an engineering design solution to reduce CO<sub>2</sub> emissions. This research proposes an optimization-machine learning pipeline and a set of models trained for the prediction of the design variables of an ecofriendly concrete column. In this research, the harmony search algorithm was used as the optimization algorithm, and different regression models were used as predictive models. Multioutput regression is applied to predict the design variables such as section width, height, and reinforcement area. The results indicated that the random forest algorithm performed better than all other machine learning algorithms that have also achieved high accuracy.https://www.mdpi.com/2076-3417/13/7/4117reinforced concreteoptimizationpredictive modelingcarbon emissionharmony search |
| spellingShingle | Yaren Aydın Gebrail Bekdaş Sinan Melih Nigdeli Ümit Isıkdağ Sanghun Kim Zong Woo Geem Machine Learning Models for Ecofriendly Optimum Design of Reinforced Concrete Columns Applied Sciences reinforced concrete optimization predictive modeling carbon emission harmony search |
| title | Machine Learning Models for Ecofriendly Optimum Design of Reinforced Concrete Columns |
| title_full | Machine Learning Models for Ecofriendly Optimum Design of Reinforced Concrete Columns |
| title_fullStr | Machine Learning Models for Ecofriendly Optimum Design of Reinforced Concrete Columns |
| title_full_unstemmed | Machine Learning Models for Ecofriendly Optimum Design of Reinforced Concrete Columns |
| title_short | Machine Learning Models for Ecofriendly Optimum Design of Reinforced Concrete Columns |
| title_sort | machine learning models for ecofriendly optimum design of reinforced concrete columns |
| topic | reinforced concrete optimization predictive modeling carbon emission harmony search |
| url | https://www.mdpi.com/2076-3417/13/7/4117 |
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