What is it about?
Three new different models were generated to predict the bond strength in elevated temperatures. these new models have a better performance than the one presented in the literature.
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Why is it important?
To conclude, this work has presented a coherent methodology to implement a well-established soft computing approach using an AI algorithm to develop three correlations that can be easily used in practice. Further refinement of the correlations can be undertaken in the future. Such refinements should include the introduction of more experimental data; currently, the availability of experimental data for concrete in high temperatures is limited, particularly in the case of fibre-RC.
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This page is a summary of: Bond behaviour of rebar in concrete at elevated temperatures: A soft computing approach, Fire and Materials, December 2022, Wiley, DOI: 10.1002/fam.3123.
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Bond behaviour of rebar in concrete at elevated temperatures: A soft computing approach
This paper assesses the capability of using a new data-driven approach to predict the bond strength between steel rebar and concrete subjected to high temperatures. The analysis has been conducted using a novel evolutionary polynomial regression analysis (EPR-MOGA) that employs soft computing techniques, and new correlations have been proposed. The proposed correlations provide better predictions and enhanced accuracy than existing approaches, such as classical regression analysis. Based on this novel approach, the resulting correlations have achieved a lower mean absolute error (), and root mean square error (), a mean () close to the optimum value (1.0) and a higher coefficient of determination (R2) compared to available correlations, which use classical regression analysis. Based on their enhanced performance, the proposed correlations can be used to obtain better optimised and more robust design calculations.
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