article · Structural Concrete
Fiber-reinforced polymer (FRP) bars offer a modern alternative to traditional reinforcement in construction, yet their performance in hollow concrete columns remains insufficiently investigated. To address this gap, this research evaluates advanced machine learning models to forecast structural performance using eight input parameters. The primary targets of the prediction are the first peak load and the ultimate failure load of FRP-reinforced hollow concrete columns. The assessment tested extreme gradient boosting, light gradient boosting, and categorical gradient boosting models. Each algorithm demonstrated strong predictive performance, maintaining deviations within ten percent of actual outcomes. Categorical gradient boosting emerged as the most reliable and robust model with superior generalisation. To facilitate adoption in structural engineering, a graphical user interface was also developed, allowing practitioners to input design parameters and immediately receive structural capacity estimates.
Using fiber-reinforced polymers in hollow concrete columns can modernise structural designs, but engineers need accurate, rapid calculation methods. Machine learning models that reliably predict structural limits within ten percent can replace complex calculations, simplifying the design process and reducing uncertainty when introducing alternative building materials into critical infrastructure.
The primary commercial application is structural engineering design software. Practising structural engineers and engineering consultancies can use the newly developed graphical user interface to assess FRP-reinforced hollow concrete columns rapidly. Because the interface is already functional and tested against empirical data, this tool appears applied and tested, sitting relatively close to direct operational use.
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Abstract Fiber reinforced polymer (FRP) has emerged as a significant advancement in construction, with design provisions outlined by codes such as GB/T 30022‐2013, CSA S806‐12 (R2017), and ACI 440:2015. While the use of FRP bars as alternatives to conventional reinforcement in columns has been extensively studied, their application in hollow concrete columns (HCCs) remains underexplored. This study investigates the behavior of FRP‐reinforced HCCs using advanced machine learning (ML) models, focusing on the prediction of two critical outputs: first peak load (Y1) and failure load (Y2), based on eight input parameters. Models evaluated include extreme gradient boosting (XGB), light gradient boosting (LGB), and categorical gradient boosting (CGB). A rigorous comparative analysis demonstrated that all models achieved high predictive accuracy, with deviations within ±10% of actual results, validating their reliability. Among the models, CGB exhibited superior generalization and robustness, emerging as the most reliable predictor for FRP‐reinforced HCC behavior. To enhance practicality, a user‐friendly graphical user interface was developed to allow engineers to input design parameters and instantly obtain predictions for Y1 and Y2. This study not only advances understanding of FRP‐reinforced HCCs but also bridges the gap between computational predictions and real‐world applications, contributing a robust predictive tool to structural engineering design.
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DOI: 10.1002/suco.202400886
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