Predicting corrosion behaviour of steel reinforcement in eco-friendly coral aggregate concrete based on hybrid machine learning methods

ABSTRACT

This study proposes a machine learning-assisted method for assessing steel reinforcement corrosion, utilising data on chloride ion concentration (CIC) and chloride ion concentration threshold (CCT) from eco-friendly coral aggregate concrete (EFCAC). A total of 2185 data points were collected to establish the EFCAC-CIC database. A multi-objective slime mould optimised support vector regression (MOSMA-SVR) model for EFCAC-CIC was developed. The significance of each feature to EFCAC-CIC was analysed. Subsequently, a graphical user interface (GUI) was developed based on the MOSMA-SVR model. Finally, the GUI and EFCAC-CCT were used to assess the corrosion behaviour of reinforcement. Results indicate that the MOSMA-SVR model provides predictions that are closer to the actual values, with smaller mean errors and standard deviations. The performance indicators, including coefficient of determination, mean absolute error, mean absolute percentage error, mean square error, root mean square error, and a20-index, of the MOSMA-SVR model are 0.987, 0.0124, 0.0332, 2.91e − 4, 0.0171 and 0.9991, respectively, and they are superior to those of the other tested models. The water type and the water–binder ratio are identified as the two most critical factors. Furthermore, by integrating the GUI and feature importance analysis results, chloride salt-resistant EFCAC can be designed. When this is combined with the GUI and EFCAC-CCT, the corrosion of rebar in EFCAC can be assessed in real time.

KEYWORDS:

• Coral aggregate concrete
• hybrid machine learning
• initiation corrosion
• chloride resistance
• graphical user interface
• feature importance analysis

Abbreviations

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Acknowledgments

The authors gratefully acknowledge the financial support provided by the National Natural Science Foundation of China (Nos. 51808438, 52208290, and 51578450).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data used to support the findings of this study are available from the corresponding author upon request.

Credit authorship contribution statement

Zhen Sun: Investigation, Methodology, Writing-original draft, Writing-review & editing, Data curation, Conceptualisation. Yalin Li: Writing – review & editing, Writing – original draft, Resources. Li Su: Writing – original draft. Shui Liu: Writing – original draft. Zhiyuan Chen: Writing – original draft.

Additional information

Funding

The work was supported by the Youth Science and Technology Foundation of Gansu Province (No. 23JRRA824).