Reinforced concrete bond strength deterioration is one of the most serious problems in the construction industry. It is one of the most common factors impacting structural deterioration and the major cause of premature decadence of reinforced concrete structures. Therefore, developing an accurate model with the lowest variance and high reliability for the bond strength of corroded reinforced concrete is very important. The current work evaluates the efficiency of convolution-based ensemble learning algorithms. To address these issues, convolution-based ensemble learning models are developed using a database collected from the previous experimental studies of relative bond strength for corroded reinforced concrete. Seven parameters are considered as inputs, including bar diameter, the ratio of concrete cover to bar diameter, the ratio of the bar diameter to embedment length, transverse reinforcement ratio, yielding strength, concrete compressive strength and the corrosion level. Results indicate that the convolution-based integrated stacking model produces excellent predictions with the coefficient of determination, a-20 index, and mean square error values of 0.84, 0.75, and 0.022. Since machine learning based models are commonly black-box models, the Shapley Additive Explanations (SHAP) method is used to explicitly relate the predicted output to the inputs. Based on SHAP, the corrosion level, concrete compressive strength, transverse reinforcement ratio, and the concrete cover to bar diameter ratio are ranked among the most influential parameters on the bond strength of the corroded reinforced concrete.

Cavaleri L., Barkhordari M.S., Repapis C.C., Armaghani D.J., Ulrikh D.V., Asteris P.G. (2022). Convolution-based ensemble learning algorithms to estimate the bond strength of the corroded reinforced concrete. CONSTRUCTION AND BUILDING MATERIALS, 359, 129504 [10.1016/j.conbuildmat.2022.129504].

Convolution-based ensemble learning algorithms to estimate the bond strength of the corroded reinforced concrete

Cavaleri L.;
2022-10-28

Abstract

Reinforced concrete bond strength deterioration is one of the most serious problems in the construction industry. It is one of the most common factors impacting structural deterioration and the major cause of premature decadence of reinforced concrete structures. Therefore, developing an accurate model with the lowest variance and high reliability for the bond strength of corroded reinforced concrete is very important. The current work evaluates the efficiency of convolution-based ensemble learning algorithms. To address these issues, convolution-based ensemble learning models are developed using a database collected from the previous experimental studies of relative bond strength for corroded reinforced concrete. Seven parameters are considered as inputs, including bar diameter, the ratio of concrete cover to bar diameter, the ratio of the bar diameter to embedment length, transverse reinforcement ratio, yielding strength, concrete compressive strength and the corrosion level. Results indicate that the convolution-based integrated stacking model produces excellent predictions with the coefficient of determination, a-20 index, and mean square error values of 0.84, 0.75, and 0.022. Since machine learning based models are commonly black-box models, the Shapley Additive Explanations (SHAP) method is used to explicitly relate the predicted output to the inputs. Based on SHAP, the corrosion level, concrete compressive strength, transverse reinforcement ratio, and the concrete cover to bar diameter ratio are ranked among the most influential parameters on the bond strength of the corroded reinforced concrete.
28-ott-2022
Settore ICAR/09 - Tecnica Delle Costruzioni
Cavaleri L., Barkhordari M.S., Repapis C.C., Armaghani D.J., Ulrikh D.V., Asteris P.G. (2022). Convolution-based ensemble learning algorithms to estimate the bond strength of the corroded reinforced concrete. CONSTRUCTION AND BUILDING MATERIALS, 359, 129504 [10.1016/j.conbuildmat.2022.129504].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/595102
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