Review of Prediction Models for Chloride Ion Concentration in Concrete Structures
Chloride ion concentration significantly impacts the durability of reinforced concrete, particularly regarding corrosion. Accurately assessing how this concentration varies with the age of structures is crucial for ensuring their safety and longevity. Recently, several predictive models have emerged...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/1/149 |
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| author | Jiwei Ma Qiuwei Yang Xinhao Wang Xi Peng Fengjiang Qin |
| author_facet | Jiwei Ma Qiuwei Yang Xinhao Wang Xi Peng Fengjiang Qin |
| author_sort | Jiwei Ma |
| collection | DOAJ |
| description | Chloride ion concentration significantly impacts the durability of reinforced concrete, particularly regarding corrosion. Accurately assessing how this concentration varies with the age of structures is crucial for ensuring their safety and longevity. Recently, several predictive models have emerged to analyze chloride ion concentration over time, classified into empirical models and machine learning models based on their data processing techniques. Empirical models directly relate chloride ion concentration to the age of concrete through specific functions. Their primary advantage lies in their low data requirements, making them convenient for engineering use. However, these models often fail to account for multiple influencing factors, which can limit their accuracy. Conversely, machine learning models can handle various factors simultaneously, providing a more detailed understanding of how chloride concentration evolves. When adequately trained with sufficient experimental data, these models generally offer superior prediction accuracy compared to mathematical models. The downside is that they necessitate a larger dataset for training, which can complicate their practical application. Future research could focus on combining machine learning and empirical models, leveraging their respective strengths to achieve a more precise evaluation of chloride ion concentration in relation to structural age. |
| format | Article |
| id | doaj-art-759671c50957495d9ece23eb59e14806 |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-759671c50957495d9ece23eb59e148062025-01-10T13:16:12ZengMDPI AGBuildings2075-53092025-01-0115114910.3390/buildings15010149Review of Prediction Models for Chloride Ion Concentration in Concrete StructuresJiwei Ma0Qiuwei Yang1Xinhao Wang2Xi Peng3Fengjiang Qin4School of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, ChinaSchool of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, ChinaSchool of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, ChinaSchool of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, ChinaKey Laboratory of New Technology for Construction of Cities in Mountain Area, School of Civil Engineering, Chongqing University, Chongqing 400045, ChinaChloride ion concentration significantly impacts the durability of reinforced concrete, particularly regarding corrosion. Accurately assessing how this concentration varies with the age of structures is crucial for ensuring their safety and longevity. Recently, several predictive models have emerged to analyze chloride ion concentration over time, classified into empirical models and machine learning models based on their data processing techniques. Empirical models directly relate chloride ion concentration to the age of concrete through specific functions. Their primary advantage lies in their low data requirements, making them convenient for engineering use. However, these models often fail to account for multiple influencing factors, which can limit their accuracy. Conversely, machine learning models can handle various factors simultaneously, providing a more detailed understanding of how chloride concentration evolves. When adequately trained with sufficient experimental data, these models generally offer superior prediction accuracy compared to mathematical models. The downside is that they necessitate a larger dataset for training, which can complicate their practical application. Future research could focus on combining machine learning and empirical models, leveraging their respective strengths to achieve a more precise evaluation of chloride ion concentration in relation to structural age.https://www.mdpi.com/2075-5309/15/1/149chloride ion concentrationstructural ageempirical modelmachine learning modelprediction model |
| spellingShingle | Jiwei Ma Qiuwei Yang Xinhao Wang Xi Peng Fengjiang Qin Review of Prediction Models for Chloride Ion Concentration in Concrete Structures Buildings chloride ion concentration structural age empirical model machine learning model prediction model |
| title | Review of Prediction Models for Chloride Ion Concentration in Concrete Structures |
| title_full | Review of Prediction Models for Chloride Ion Concentration in Concrete Structures |
| title_fullStr | Review of Prediction Models for Chloride Ion Concentration in Concrete Structures |
| title_full_unstemmed | Review of Prediction Models for Chloride Ion Concentration in Concrete Structures |
| title_short | Review of Prediction Models for Chloride Ion Concentration in Concrete Structures |
| title_sort | review of prediction models for chloride ion concentration in concrete structures |
| topic | chloride ion concentration structural age empirical model machine learning model prediction model |
| url | https://www.mdpi.com/2075-5309/15/1/149 |
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