ArticleScientific reports2024
Prediction and reliability analysis of shear strength of RC deep beams.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Machine Learning-Driven Cellular Automata for Simulating Plant Growth Regulator-Optimized Callus Induction inPlants (Basel, Switzerland) · 2026Article
- Deep learning-based framework for time-dependent reliability analysis of a cable-stayed bridge with corroded PSC box girders under gravity loads.Scientific reports · 2025Article
- Predicting flexural strength of hybrid FRP-steel reinforced beams using symbolic regression and ML techniques.Scientific reports · 2025Article
- Data-Driven Approach to Derive Equation for Predicting Ultimate Shear Strength of Reinforced Concrete Beams Without Stirrups.Materials (Basel, Switzerland) · 2025Article
- Prediction of the axial compression capacity of ECC-CES columns using adaptive sampling and machine learning techniques.Scientific reports · 2025Article
- Symbolic regression for strength prediction of eccentrically loaded concrete-filled steel tubular columns.Scientific reports · 2025Article
- Strength prediction of ECC-CES columns under eccentric compression using adaptive sampling and ML techniques.Scientific reports · 2025Article
- STM-based symbolic regression for strength prediction of RC deep beams and corbels.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study explores machine learning (ML) capabilities for predicting the shear strength of reinforced concrete deep beams (RCDBs). For this purpose, eight typical machine-learning models, i.e., symbolic regression (SR), XGBoost (XGB), CatBoost (CATB), random forest (RF), LightGBM, support vector regression (SVR), artificial neural networks (ANN), and Gaussian process regression (GPR) models, are selected and compared based on a database of 840 samples with 14 input features. The hyperparameter tuning of the introduced ML models is performed using the Bayesian optimization (BO) technique. The comparison results show that the CatBoost model is the most reliable and accurate ML model (R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.