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Prediction of the degree of steel corrosion damage in reinforced concrete using field-based data by multi-gene genetic programming approach

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Abstract

Unanticipated failure of reinforced concrete structures due to corrosion of steel rebar embedded in concrete causes to increase the demand for finding methods to forecast the service life of concrete structures. In this field, the success of machine learning-based methods leads to the use of multi-gene genetic programming (MGGP) method for classifying the degree of corrosion destruction of steel in reinforced concrete in this paper. Despite the common application of MGGP that is the symbolic regression, in this research, MGGP was adapted to use in classification tasks. Accordingly, a large field database has been collected from different regions in the Persian Gulf for modeling of MGGP and neural networks. Comparing the results attained from the MGGP procedure with neural networks revealed that both methods have a good ability to predict the degree of steel corrosion damage for the data range of examined reinforced concrete. But, MGGP gives a particular mathematic equation to estimate the outcome by using the input variables. Moreover, this method can also implement sensitivity analysis simultaneously. The selected input variables by MGGP via the evolution process were the most relevant to the class corrosion whereas there was not any redundancy between them. It is in good agreement with results obtained from sensitivity analysis.

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Code availability

The codes generated during the current study are available from the corresponding author on reasonable request.

Data availability and material

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This project was supported by Hormozgan Regional Electric Company (HREC) and Niroo Research Institute (NRI), info@hrec.co.ir and info@nri.ac.ir.

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Contributions

ZR: Conceptualization, methodology, software; ME: conceptualization, methodology, software, supervision; MG: project administration; ME: conceptualization, methodology, software, supervision; HB: conceptualization, investigation, data curation.

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Correspondence to Mahdi Eftekhari.

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Rajabi, Z., Eftekhari, M., Ghorbani, M. et al. Prediction of the degree of steel corrosion damage in reinforced concrete using field-based data by multi-gene genetic programming approach. Soft Comput 26, 9481–9496 (2022). https://doi.org/10.1007/s00500-021-06704-2

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  • DOI: https://doi.org/10.1007/s00500-021-06704-2

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