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Title: Machine Learning for Shear Strength of Reinforced-Concrete Beams

Author(s): Rodrigo Castillo, Pinar Okumus, Negar Elhami Khorasani, and Varun Chandola

Publication: Structural Journal

Volume: 119

Issue: 5

Appears on pages(s): 83-94

Keywords:

Date: 9/1/2022

Abstract:
This study uses machine learning (ML) to characterize the shear strength of reinforced concrete (RC) beams and one-way slabs. A database of 1436 RC shear tests is used to train three ML algorithms (ordinary linear regression, support vector regression, and Gaussian process regression). The database is divided into two training subsets per ACI 318-19 minimum shear reinforcement requirements. Each training sample consists of up to 10 predictive features (geometry, materials, reinforcement detail, load location) and the corresponding target feature (shear strength). The most accurate algorithm, the Gaussian process regression, has 17% and 11% smaller mean percent error in shear strength predictions than ACI 318-19 guidelines for the subsets with shear reinforcement areas less than and larger than the minimum ACI 318-19 requirement, respectively. This algorithm also provides predictive confidence with a standard deviations less than 5.2 kN (1.18 kip) for 99.7% of the predictions.


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Electronic Structural Journal



  

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