ArticleMaterials (Basel, Switzerland)2021
Application of Principal Component Analysis Approach to Predict Shear Strength of Reinforced Concrete Beams with Stirrups.
Article in Materials (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 19 citations in OpenAlex.
- Predictive modeling of wide-shallow RC beams shear strength considering stirrups effect using (FEM-ML) approach.Scientific reports · 2024Article
- Accelerated First-Principles Calculations Based on Machine Learning for Interfacial Modification Element Screening of SiCp/Al Composites.Materials (Basel, Switzerland) · 2024Article
- Study on Meso-Structure Evolution in Granular Matters Based on the Contact Loop Recognition and Determination Technique.Materials (Basel, Switzerland) · 2021Article
- Machine Learning Assisted Classification of Aluminum Nitride Thin Film Stress via In-Situ Optical Emission Spectroscopy Data.Materials (Basel, Switzerland) · 2021Article
- Shear Capacity of Reinforced Concrete Beams under Monotonic and Cyclic Loads: Experiments and Computational Models.Materials (Basel, Switzerland) · 2021Article
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
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Abstract
The reinforced concrete (RC) member's shear strength estimation has been experimentally studied in most cases due to its nonlinear behavior. Many empirical equations have been derived from the experimental data; however, even those adopted in the construction codes do not thoroughly and accurately describe their shear behavior. Theoretically explained equations, on the other hand, are aligned with the experiment; however, they are complicated to use in practice. As shear behavior research is data-driven, the machine learning technique is applicable. Herein, an artificial neural network (ANN) algorithm is trained with 776 experiment results collected from available publications. The raw data is preprocessed by principal component analysis (PCA) before the application of the ANN technique. The predictions of the trained algorithm using ANN with PCA are compared to those of formulae adopted in a few existing building codes. Finally, a parametric study is conducted, and the significance of each variable to the strength of RC members is analyzed.
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