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Abstract Glass fiber-reinforced polymers (GFRP) are widely applied to enhance the strength of concrete columns due to their lightweight and high-strength characteristics. This study presents the development of a metaheuristics-guided machine learning (ML) model for predicting the compressive strength (CS) of GFRP-confined concrete columns (GFRP-CC). Traditional predictive models, primarily based on Linear or nonlinear regression, are often limited by narrow data scopes and methodological constraints. To address this gap, we propose an innovative ML model, leveraging an extensive database of 319 experimental results compiled from 41 peer-reviewed articles spanning 1991–2024. Using an artificial neural network (ANN) combined with five metaheuristic algorithms, the study aims to reduce the dependency on costly and time-intensive laboratory testing. The model development considered eight key parameters: diameter of the compression member (D), height of the compression member (H), compressive strength of unconfined concrete (f′_co), GFRP reinforcement ratio (ρ_f), tensile modulus of elasticity of GFRP (E_f), ultimate tensile strength of GFRP (f_f), nominal thickness of GFRP reinforcement (t_f), and number of GFRP layers. Among the tested models, the Stochastic Paint Optimizer (SPO)-ANN model demonstrated the highest accuracy, achieving a coefficient of determination of 0.9630 with minimal error values. To ensure transparency and interpretability, SHapley Additive exPlanations (SHAP), Olden methodologies, and Partial dependence were employed to elucidate the relative importance of contributing features. Critical factors influencing the CS of GFRP-CC included the thickness of GFRP reinforcement, tensile strength, and layer count. A user-friendly graphical interface was developed to facilitate practical adoption, enabling researchers and practitioners to model CFRP-CC compressive strength efficiently. This work represents a paradigm shift in concrete research, emphasizing sophisticated, data-driven methodologies that bridge the gap between experimental data and practical applications. 

Abstract The increasing demand for concrete in construction presents challenges such as pollution, high energy consumption, and complex structural requirements. Three‐dimensional printing (3DP) offers a promising solution by eliminating formwork, reducing waste, and enabling intricate geometries. Predicting the strength of 3D‐printed fiber‐reinforced concrete (3DP‐FRC) remains challenging due to the nonlinear nature of neural networks and uncertainty in optimizing key parameters. In this study, we developed machine learning models using five metaheuristic algorithms—arithmetic optimization algorithm, African Vulture Optimization Algorithm, flow direction algorithm, generalized normal distribution optimization, and Mountain Gazelle Optimizer—to optimize the weights and biases in a feed‐forward backpropagation network. Among all the algorithms, MGO demonstrated the best performance. To address data limitations, a data augmentation method combining Kernel density estimation and Wasserstein generative adversarial networks is employed. Sensitivity analysis using SHapley Additive exPlanations (SHAP) identifies the most influential input parameters. The proposed MGO‐ANN model enhances predictive accuracy, reducing the need for extensive laboratory testing. Additionally, a user‐friendly graphical user interface is developed to facilitate practical applications in estimating 3DP‐FRC flexural strength. 

The alkali–silica reaction (ASR) is a critical concern for concrete durability, yet its assessment remains challenging and directly impacts mixture design decisions. This review shows that the inconsistencies are more prevalent in mitigation evaluations compared to aggregate reactivity assessments, mainly due to the chemical variations in supplementary cementitious materials (SCMs). A validated framework is suggested to determine the optimal SCM replacement levels for ASR mitigation based on extensive field data, offering direct guidance for mix design decisions involving potentially reactive aggregates. The combination of the accelerated mortar bar test (AMBT) and the miniature concrete prism test (MCPT) is shown to be a reliable alternative for the concrete prism test (CPT) in aggregate reactivity. Also, their extended versions, AMBT (28-day) and MCPT (84-day), can be applied for SCMs mitigation evaluation. Given the slower reactivity of SCMs compared to ordinary Portland cement (OPC), the importance of incorporating indirect test methods, such as the modified R3 test and bulk resistivity is underscored. In addition, emerging sustainability shifts further complicate ASR assessment, including the adoption of Portland limestone cement (PLC), the use of seawater in concrete, and the declining availability of fly ash (FA) and slag. These changes call for updated ASR testing specifications and increased research into natural pozzolans (NPs) as promising SCMs for future ASR mitigation. 

Abstract Fiber-reinforced polymer (FRP) bars offer a promising alternative to conventional steel reinforcement in reinforced concrete (RC) structures, primarily due to their corrosion resistance. However, their intrinsic linear elastic behavior may limit their applications to non-seismic zones or regions with limited seismic activity. To extend their applications in seismic zones such as Seismic Design Category D, a novel approach involving a hybrid-RC (HRC) cross-section is proposed. This approach entails placing FRP bars on the cross-section exterior for corrosion resistance, while steel bars on the inner side of the cross-section to ensure ductility and energy dissipation. This paper presents a methodology for designing HRC cross-sections and evaluates their ductility and energy dissipation capabilities. The discussion encompasses various design aspects of an HRC section including strength reduction factor, minimum reinforcement ratio, reinforcement strain, concrete shear strength, and the impact of confinement on ductility and energy dissipation. Additionally, an illustrative example of a HRC section demonstrates the practicality of the proposed design methodology in practical applications.