Concrete chloride diffusion modelling using marine creatures-based metaheuristic artificial intelligence
Chloride-induced steel reinforcement corrosion endangers the durability of concrete structures and may cause substantial economic loss and environmental impact. An accurate estimation of chloride diffusion of steel-reinforced concrete assists in the reliable prediction of its service life. Artificia...
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| Published in: | Journal of cleaner production Vol. 374; p. 134021 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
10.11.2022
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| ISSN: | 0959-6526, 1879-1786 |
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| Abstract | Chloride-induced steel reinforcement corrosion endangers the durability of concrete structures and may cause substantial economic loss and environmental impact. An accurate estimation of chloride diffusion of steel-reinforced concrete assists in the reliable prediction of its service life. Artificial neural network (ANN) trained by classical optimization algorithms suffer from the overfitting issue. However, metaheuristic algorithms keep the balance between exploration and exploitation to overcome this issue. Four marine creatures-based metaheuristic optimization algorithms, i.e., Jellyfish search optimizer (JSO), marine predators algorithm (MPA), salp swarm algorithm (SSA), and whale optimization algorithm (WOA) are synthesized by ANN in this study. The proposed algorithms are served to model the apparent chloride diffusion (Dap) of concrete exposed to atmosphere, tidal, splash, and submerged environments. A total of 216 data from relevant field experiments was extracted from the literature. The results indicate that the synthesized algorithms with simpler architectures perform better than the traditional algorithm. The Wilcoxon rank-sum test shows that the ANN-JSO algorithm performs better than other ANN algorithms, and there is no preference among the average performances of the ANN-MPA, ANN-SSA, and ANN-WOA in the testing phase. The global sensitivity analysis denotes that the exposure time, curing conditions, and exposure are the most critical parameters in predicting the Dap of concrete. |
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| AbstractList | Chloride-induced steel reinforcement corrosion endangers the durability of concrete structures and may cause substantial economic loss and environmental impact. An accurate estimation of chloride diffusion of steel-reinforced concrete assists in the reliable prediction of its service life. Artificial neural network (ANN) trained by classical optimization algorithms suffer from the overfitting issue. However, metaheuristic algorithms keep the balance between exploration and exploitation to overcome this issue. Four marine creatures-based metaheuristic optimization algorithms, i.e., Jellyfish search optimizer (JSO), marine predators algorithm (MPA), salp swarm algorithm (SSA), and whale optimization algorithm (WOA) are synthesized by ANN in this study. The proposed algorithms are served to model the apparent chloride diffusion (Dₐₚ) of concrete exposed to atmosphere, tidal, splash, and submerged environments. A total of 216 data from relevant field experiments was extracted from the literature. The results indicate that the synthesized algorithms with simpler architectures perform better than the traditional algorithm. The Wilcoxon rank-sum test shows that the ANN-JSO algorithm performs better than other ANN algorithms, and there is no preference among the average performances of the ANN-MPA, ANN-SSA, and ANN-WOA in the testing phase. The global sensitivity analysis denotes that the exposure time, curing conditions, and exposure are the most critical parameters in predicting the Dₐₚ of concrete. Chloride-induced steel reinforcement corrosion endangers the durability of concrete structures and may cause substantial economic loss and environmental impact. An accurate estimation of chloride diffusion of steel-reinforced concrete assists in the reliable prediction of its service life. Artificial neural network (ANN) trained by classical optimization algorithms suffer from the overfitting issue. However, metaheuristic algorithms keep the balance between exploration and exploitation to overcome this issue. Four marine creatures-based metaheuristic optimization algorithms, i.e., Jellyfish search optimizer (JSO), marine predators algorithm (MPA), salp swarm algorithm (SSA), and whale optimization algorithm (WOA) are synthesized by ANN in this study. The proposed algorithms are served to model the apparent chloride diffusion (Dap) of concrete exposed to atmosphere, tidal, splash, and submerged environments. A total of 216 data from relevant field experiments was extracted from the literature. The results indicate that the synthesized algorithms with simpler architectures perform better than the traditional algorithm. The Wilcoxon rank-sum test shows that the ANN-JSO algorithm performs better than other ANN algorithms, and there is no preference among the average performances of the ANN-MPA, ANN-SSA, and ANN-WOA in the testing phase. The global sensitivity analysis denotes that the exposure time, curing conditions, and exposure are the most critical parameters in predicting the Dap of concrete. |
| ArticleNumber | 134021 |
| Author | Mohammadi Golafshani, Emadaldin Kim, Taehwan Arashpour, Mehrdad Kashani, Alireza |
| Author_xml | – sequence: 1 givenname: Emadaldin orcidid: 0000-0001-8499-3975 surname: Mohammadi Golafshani fullname: Mohammadi Golafshani, Emadaldin email: e.mohammadi_golafshani@unsw.edu.au organization: School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW, 2052, Australia – sequence: 2 givenname: Alireza surname: Kashani fullname: Kashani, Alireza email: ali.kashani@unsw.edu.au organization: School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW, 2052, Australia – sequence: 3 givenname: Taehwan orcidid: 0000-0003-4371-7178 surname: Kim fullname: Kim, Taehwan email: taehwan.kim@unsw.edu.au organization: School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW, 2052, Australia – sequence: 4 givenname: Mehrdad orcidid: 0000-0003-4148-3160 surname: Arashpour fullname: Arashpour, Mehrdad email: mehrdad.arashpour@monash.edu organization: Department of Civil Engineering, Monash University, Melbourne, Australia |
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| Keywords | Concrete Marine creatures-based metaheuristic optimization algorithms Artificial neural network Artificial intelligence Chloride diffusion |
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| SubjectTerms | algorithms Artificial intelligence Artificial neural network Chloride diffusion chlorides Concrete corrosion durability environmental impact exposure duration financial economics Marine creatures-based metaheuristic optimization algorithms neural networks prediction Scyphozoa steel swarms |
| Title | Concrete chloride diffusion modelling using marine creatures-based metaheuristic artificial intelligence |
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