Absorption and Utilization of Pollutants in Water: A Novel Model for Predicting the Carrying Capacity and Sustainability of Buildings
The combination of water management and urban planning can promote the sustainable development of cities, which can be achieved through buildings’ absorption and utilization of pollutants in water. Sulfate ions are one of the important pollutants in water, and concrete is an important building mater...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/17/3152 |
| _version_ | 1797581799300268032 |
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| author | Enyang Mei Kunyang Yu |
| author_facet | Enyang Mei Kunyang Yu |
| author_sort | Enyang Mei |
| collection | DOAJ |
| description | The combination of water management and urban planning can promote the sustainable development of cities, which can be achieved through buildings’ absorption and utilization of pollutants in water. Sulfate ions are one of the important pollutants in water, and concrete is an important building material. The absorption of sulfate ions by concrete can change buildings’ bearing capacity and sustainability. Nevertheless, given the complex and heterogeneous nature of concrete and a series of chemical and physical reactions, there is currently no efficient and accurate method for predicting mechanical performance. This work presents a deep learning model for establishing the relationship between a water environment and concrete performance. The model is constructed using an experimental database consisting of 1328 records gathered from the literature. The utmost essential parameters influencing the compressive strength of concrete under a sulfate attack such as the water-to-binder ratio, the sulfate concentration and type, the admixture type and percentage, and the service age are contemplated as input factors in the modeling process. The results of using several loss functions all approach 0, and the error between the actual value and the predicted value is small. Moreover, the results also demonstrate that the method performed better for predicting the performance of concrete under water pollutant attacks compared to seven basic machine learning algorithms. The method can serve as a reference for the integration of urban building planning and water management. |
| first_indexed | 2024-03-10T23:10:44Z |
| format | Article |
| id | doaj.art-ad4fca4b6efc4a849bf35dd1e0c7d3d8 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T23:10:44Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-ad4fca4b6efc4a849bf35dd1e0c7d3d82023-11-19T09:02:56ZengMDPI AGWater2073-44412023-09-011517315210.3390/w15173152Absorption and Utilization of Pollutants in Water: A Novel Model for Predicting the Carrying Capacity and Sustainability of BuildingsEnyang Mei0Kunyang Yu1School of Bioengineering, Huainan Normal University, Huainan 232038, ChinaSchool of Civil Engineering, Harbin Institute of Technology, Harbin 150090, ChinaThe combination of water management and urban planning can promote the sustainable development of cities, which can be achieved through buildings’ absorption and utilization of pollutants in water. Sulfate ions are one of the important pollutants in water, and concrete is an important building material. The absorption of sulfate ions by concrete can change buildings’ bearing capacity and sustainability. Nevertheless, given the complex and heterogeneous nature of concrete and a series of chemical and physical reactions, there is currently no efficient and accurate method for predicting mechanical performance. This work presents a deep learning model for establishing the relationship between a water environment and concrete performance. The model is constructed using an experimental database consisting of 1328 records gathered from the literature. The utmost essential parameters influencing the compressive strength of concrete under a sulfate attack such as the water-to-binder ratio, the sulfate concentration and type, the admixture type and percentage, and the service age are contemplated as input factors in the modeling process. The results of using several loss functions all approach 0, and the error between the actual value and the predicted value is small. Moreover, the results also demonstrate that the method performed better for predicting the performance of concrete under water pollutant attacks compared to seven basic machine learning algorithms. The method can serve as a reference for the integration of urban building planning and water management.https://www.mdpi.com/2073-4441/15/17/3152water managementsustainable developmentdeep learningmechanical performanceconcrete |
| spellingShingle | Enyang Mei Kunyang Yu Absorption and Utilization of Pollutants in Water: A Novel Model for Predicting the Carrying Capacity and Sustainability of Buildings Water water management sustainable development deep learning mechanical performance concrete |
| title | Absorption and Utilization of Pollutants in Water: A Novel Model for Predicting the Carrying Capacity and Sustainability of Buildings |
| title_full | Absorption and Utilization of Pollutants in Water: A Novel Model for Predicting the Carrying Capacity and Sustainability of Buildings |
| title_fullStr | Absorption and Utilization of Pollutants in Water: A Novel Model for Predicting the Carrying Capacity and Sustainability of Buildings |
| title_full_unstemmed | Absorption and Utilization of Pollutants in Water: A Novel Model for Predicting the Carrying Capacity and Sustainability of Buildings |
| title_short | Absorption and Utilization of Pollutants in Water: A Novel Model for Predicting the Carrying Capacity and Sustainability of Buildings |
| title_sort | absorption and utilization of pollutants in water a novel model for predicting the carrying capacity and sustainability of buildings |
| topic | water management sustainable development deep learning mechanical performance concrete |
| url | https://www.mdpi.com/2073-4441/15/17/3152 |
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