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@article{188043,
        author = {SATHYA PRABHA K and A. Sheelavathi, B.Ashwin},
        title = {Machine Learning Models for Predicting Compressive Strength in FRP-Wrapped Concrete Columns: A Critical Review (2022–2025)},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {370-378},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188043},
        abstract = {Fibre-reinforced polymer (FRP) confinement markedly enhances the compressive capacity and ductility of concrete columns. Yet, classical formulations struggle to generalise across materials, geometries, and loading paths, particularly for noncircular sections and eccentric loads . Recent machine learning (ML) advances—including gene expression programming (GEP), group method of data handling (GMDH), gradient boosting, artificial neural networks (ANN), and Gaussian process regression (GPR) have delivered higher predictive accuracy, interpretable feature attributions, and deployable tools, especially when paired with physics informed features and external validation . This review consolidates developments from 2022 to 2025, benchmarks key ML methods against classical baselines, and identifies recommended feature formulas, practical deployment strategies, and future research priorities. Persistent gaps remain in dataset standardization, domain shifts, and handling partial confinement or eccentric loading. The integration of hybrid physics–ML and uncertainty-aware pipelines is recommended for robust design.},
        keywords = {Machine learning, FRP wrapping, concrete columns, compressive strength prediction, neuro-fuzzy systems, XGBoost, structural rehabilitation},
        month = {November},
        }