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Bond behaviour of rebar in concrete at elevated temperatures: A soft computing approach

Al Hamd, Rwayda Kh. S. / Alzabeebee, Saif / Cunningham, Lee S. / Gales, John

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 ( $MAE$ ), and root mean square error ( $RMSE$ ), a mean ( $μ$ ) 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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Bond behaviour of rebar in concrete at elevated temperatures: A soft computing approach

Al Hamd, Rwayda Kh. S. / Alzabeebee, Saif / Cunningham, Lee S. / Gales, John

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 ( $MAE$ ), and root mean square error ( $RMSE$ ), a mean ( $μ$ ) 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.

Bond behaviour of rebar in concrete at elevated temperatures: A soft computing approach

Al Hamd, Rwayda Kh. S. / Alzabeebee, Saif / Cunningham, Lee S. / Gales, John

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 ( $MAE$ ), and root mean square error ( $RMSE$ ), a mean ( $μ$ ) 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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Titel:

Bond behaviour of rebar in concrete at elevated temperatures: A soft computing approach

Beteiligte:

Al Hamd, Rwayda Kh. S. (Autor:in) / Alzabeebee, Saif (Autor:in) / Cunningham, Lee S. (Autor:in) / Gales, John (Autor:in)

Erschienen in:

Fire and Materials ; 47 ; 804-814

Erscheinungsdatum:

01.10.2023

Format / Umfang:

11 pages

ISSN:

0308-0501 , 1099-1018

DOI:

https://doi.org/10.1002/fam.3123

Medientyp:

Aufsatz (Zeitschrift)

Format:

Elektronische Ressource

Sprache:

Englisch

Schlagwörter:

bond strength , elevated temperature , evolutionary computing , soft computing

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