A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Statistical Approach to Modeling Reduced Shear Capacity of Corrosion-Damaged Reinforced Concrete Beams
In the 2017, ASCE graded the national infrastructure of the United States as D+ overall. The ASCE gave the national School Buildings category a D+ grade and the Bridges category a C+ grade. Steel corrosion is the main reason for the trend toward deterioration in US infrastructure, including buildings, bridges, pipelines, and wharves. Reinforced concrete (RC) is among the most widely used primary construction materials worldwide. Objectives of the current research were to determine the design parameters that have the greatest impact on the reduced shear capacity of RC beams in the presence of corrosion, and to create a model to estimate the reduced shear capacity by refining the ACI shear design model for RC beams in buildings. Using a database of experimental tests, an artificial neural network model was created to estimate reduced shear strength and to perform a sensitivity analysis of the parameters affecting residual shear capacity. The sensitivity analysis showed that the compressive strength of concrete is the most influential parameter affecting reduced shear strength. A multiple linear-regression analysis was also performed to aid in proposing a new model.
Statistical Approach to Modeling Reduced Shear Capacity of Corrosion-Damaged Reinforced Concrete Beams
In the 2017, ASCE graded the national infrastructure of the United States as D+ overall. The ASCE gave the national School Buildings category a D+ grade and the Bridges category a C+ grade. Steel corrosion is the main reason for the trend toward deterioration in US infrastructure, including buildings, bridges, pipelines, and wharves. Reinforced concrete (RC) is among the most widely used primary construction materials worldwide. Objectives of the current research were to determine the design parameters that have the greatest impact on the reduced shear capacity of RC beams in the presence of corrosion, and to create a model to estimate the reduced shear capacity by refining the ACI shear design model for RC beams in buildings. Using a database of experimental tests, an artificial neural network model was created to estimate reduced shear strength and to perform a sensitivity analysis of the parameters affecting residual shear capacity. The sensitivity analysis showed that the compressive strength of concrete is the most influential parameter affecting reduced shear strength. A multiple linear-regression analysis was also performed to aid in proposing a new model.
Statistical Approach to Modeling Reduced Shear Capacity of Corrosion-Damaged Reinforced Concrete Beams
Soltani, Mahmoodreza (author) / Abu-Abaileh, Adham (author) / Scott Rowe, Bryan (author)
2020-12-24
Article (Journal)
Electronic Resource
Unknown
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