Data-Driven Estimation Models for Chloride Ion Diffusion Coefficient in Concrete Using Bayesian Approach
The corrosion of reinforcing steel bars induced by chloride has a significant impact on the performance of reinforced concrete (RC) structures. Although various models have been proposed to estimate chloride diffusivity, many empirical models remain unavailable due to database limitations or the ove...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2024/6019055 |
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| author | Ruifu Cui Huihui Yang Jiehong Li Honghui Tang Guowen Yao Yang Yu Xuanrui Yu |
| author_facet | Ruifu Cui Huihui Yang Jiehong Li Honghui Tang Guowen Yao Yang Yu Xuanrui Yu |
| author_sort | Ruifu Cui |
| collection | DOAJ |
| description | The corrosion of reinforcing steel bars induced by chloride has a significant impact on the performance of reinforced concrete (RC) structures. Although various models have been proposed to estimate chloride diffusivity, many empirical models remain unavailable due to database limitations or the oversight of certain factors. To enhance the accuracy of chloride diffusivity estimation, this paper has collected 109 sets of experimental data. These data were used to develop a Bayesian prediction model for a more precise estimation of chloride diffusivity. The input features examined in this study include water–cement (W/C) ratio, thickness of concrete specimens (M (cm)), volume fraction of coarse aggregate (R), ratio of environmental temperature to standard curing temperature (T/T0), ratio of environmental humidity to standard curing humidity (h/hc), and exposure time. The output parameter is the chloride diffusion coefficient. Sensitivity analysis of the input parameters reveals that the ratio of exposure time to curing time (t/t0) and T/T0 are the key factors influencing the chloride diffusion coefficient, with importance coefficients of 0.83 and 0.67, respectively. The h/hc has the least impact. In addition, the W/C ratio and R also have a certain influence on the chloride diffusion coefficient, and optimizing these parameters can further enhance the durability of concrete structures. |
| format | Article |
| id | doaj-art-ac02f67644fc4eb29f0d16489cee88a4 |
| institution | OA Journals |
| issn | 1687-8094 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-ac02f67644fc4eb29f0d16489cee88a42025-08-20T02:06:44ZengWileyAdvances in Civil Engineering1687-80942024-01-01202410.1155/2024/6019055Data-Driven Estimation Models for Chloride Ion Diffusion Coefficient in Concrete Using Bayesian ApproachRuifu Cui0Huihui Yang1Jiehong Li2Honghui Tang3Guowen Yao4Yang Yu5Xuanrui Yu6School of Civil EngineeringSchool of Civil EngineeringSchool of Civil and Environmental EngineeringSchool of Civil EngineeringSchool of Civil EngineeringSchool of Civil and Environmental EngineeringSchool of Civil EngineeringThe corrosion of reinforcing steel bars induced by chloride has a significant impact on the performance of reinforced concrete (RC) structures. Although various models have been proposed to estimate chloride diffusivity, many empirical models remain unavailable due to database limitations or the oversight of certain factors. To enhance the accuracy of chloride diffusivity estimation, this paper has collected 109 sets of experimental data. These data were used to develop a Bayesian prediction model for a more precise estimation of chloride diffusivity. The input features examined in this study include water–cement (W/C) ratio, thickness of concrete specimens (M (cm)), volume fraction of coarse aggregate (R), ratio of environmental temperature to standard curing temperature (T/T0), ratio of environmental humidity to standard curing humidity (h/hc), and exposure time. The output parameter is the chloride diffusion coefficient. Sensitivity analysis of the input parameters reveals that the ratio of exposure time to curing time (t/t0) and T/T0 are the key factors influencing the chloride diffusion coefficient, with importance coefficients of 0.83 and 0.67, respectively. The h/hc has the least impact. In addition, the W/C ratio and R also have a certain influence on the chloride diffusion coefficient, and optimizing these parameters can further enhance the durability of concrete structures.http://dx.doi.org/10.1155/2024/6019055 |
| spellingShingle | Ruifu Cui Huihui Yang Jiehong Li Honghui Tang Guowen Yao Yang Yu Xuanrui Yu Data-Driven Estimation Models for Chloride Ion Diffusion Coefficient in Concrete Using Bayesian Approach Advances in Civil Engineering |
| title | Data-Driven Estimation Models for Chloride Ion Diffusion Coefficient in Concrete Using Bayesian Approach |
| title_full | Data-Driven Estimation Models for Chloride Ion Diffusion Coefficient in Concrete Using Bayesian Approach |
| title_fullStr | Data-Driven Estimation Models for Chloride Ion Diffusion Coefficient in Concrete Using Bayesian Approach |
| title_full_unstemmed | Data-Driven Estimation Models for Chloride Ion Diffusion Coefficient in Concrete Using Bayesian Approach |
| title_short | Data-Driven Estimation Models for Chloride Ion Diffusion Coefficient in Concrete Using Bayesian Approach |
| title_sort | data driven estimation models for chloride ion diffusion coefficient in concrete using bayesian approach |
| url | http://dx.doi.org/10.1155/2024/6019055 |
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