ArticlePolymers2022
Ensemble Tree-Based Approach towards Flexural Strength Prediction of FRP Reinforced Concrete Beams.
Article in Polymers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- An Interpretable Ensemble Machine Learning Framework for Predicting the Ultimate Flexural Capacity of BFRP-Reinforced Concrete Beams.Polymers · 2026Article
- Review
- Article
- Systematic literature review on the application of machine learning for the prediction of properties of different types of concrete.PeerJ. Computer science · 2024Article
- Roll-Out Deployment Process Analysis of a Fiber Reinforced Polymer (FRP) Composite Tape-Spring Boom.Polymers · 2023Article
- Prediction of Stress-Strain Curves for HFRP Composite Confined Brick Aggregate Concrete under Axial Load.Polymers · 2023Article
- PCA-Based Hybrid Intelligence Models for Estimating the Ultimate Bearing Capacity of Axially Loaded Concrete-Filled Steel Tubes.Materials (Basel, Switzerland) · 2022Article
- Estimating Radiation Shielding of Fired Clay Bricks Using ANN and GEP Approaches.Materials (Basel, Switzerland) · 2022Article
- Prediction Models for Estimating Compressive Strength of Concrete Made of Manufactured Sand Using Gene Expression Programming Model.Materials (Basel, Switzerland) · 2022Article
- Article
- Investigating the Bond Strength of FRP Rebars in Concrete under High Temperature Using Gene-Expression Programming Model.Polymers · 2022Article
- Computational AI Models for Investigating the Radiation Shielding Potential of High-Density Concrete.Materials (Basel, Switzerland) · 2022Article
- Prediction Models for Evaluating Resilient Modulus of Stabilized Aggregate Bases in Wet and Dry Alternating Environments: ANN and GEP Approaches.Materials (Basel, Switzerland) · 2022Article
- Estimating Flexural Strength of FRP Reinforced Beam Using Artificial Neural Network and Random Forest Prediction Models.Polymers · 2022Article
- GEP Tree-Based Prediction Model for Interfacial Bond Strength of Externally Bonded FRP Laminates on Grooves with Concrete Prism.Polymers · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
Due to rise in infrastructure development and demand for seawater and sea sand concrete, fiber-reinforced polymer (FRP) rebars are widely used in the construction industry. Flexural strength is an important component of reinforced concrete structural design. Therefore, this research focuses on estimating the flexural capacity of FRP-reinforced concrete beams using novel artificial intelligence (AI) decision tree (DT) and gradient boosting tree (GBT) approaches. For this purpose, six input parameters, namely the area of bottom flexural reinforcement, depth of the beam, width of the beam, concrete compressive strength, the elastic modulus of FRP rebar, and the tensile strength of rebar at failure, are considered to predict the moment bearing capacity of the beam under bending loads. The models were trained using 60% of the database and were validated first-hand on the remaining 40% database employing the correlation coefficient (R), error indices namely, mean absolute error, root mean square error (MAE, RMSE) and slope of the regression line between observed and predicted results. The developed models were further validated using sensitivity and parametric analysis. Both models revealed comparable performance; however, based on the comparison of the slope of the validation data (0.83 for GBT model against 0.75 for the DT model) and higher R for the validation phase in case of the GBT model in comparison to the DT, the GBT model can be considered more accurate and robust. The sensitivity analysis yielded depth of the beam as the most influential parameter in contributing flexural strength of the beam, followed by the area of flexural reinforcement. The developed GBT model surpasses the existing gene expression programming (GEP) model in terms of accuracy; however, the current American Concrete Institute (ACI) model equations are more reliable than AI models in predicting the flexural strength of FRP-reinforced concrete beams.
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