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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. 

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. 

Abstract The current provisions for development length in the ACI 440.11 code disregard the confinement effect provided by stirrups on the bond strength of longitudinal bars and require splice lengths that pose implementation challenges. Given the significant improvement in GFRP material properties, this study investigated the bond strength of sand-coated GFRP bars and proposed a new factor to include the effect of stirrup confinement on the bond-strength provisions. The experimental program involved 16 GFRP-reinforced concrete (RC) beams having a width of 300 mm, and depth 440 mm, consisting of two repetitions for every configuration, subjected to four-point loading. The test parameters comprised lap-splice length and stirrup spacing in the lap-spliced zone. Out of 16 GFRP-RC beams, two beams were reinforced with two M16 (No. 5) continuous bars and six with varying lap-splice lengths [i.e., 40, 60, and 80 bar diameters (d_b)] without confining stirrups. To evaluate the effect of confining stirrups, eight beams were reinforced with two M16 (No. 5) lap-spliced longitudinal bars (i.e., 40 and 60 d_b) and M13 (No. 4) stirrups spaced at 100 mm (4 in.) and 200 mm (8 in.) center-to-center. Based on experimental results, stirrup confinement clearly increased the bond strength, reduced longitudinal bar slippage, and increased splitting stress. The beams with a splice length of 60 d_band stirrups on 100 mm (4 in.) centers achieved 57% higher capacity than those with the same lap-splice length but without stirrups. Further, the ACI 440.11 equation overestimated the bond strength of sand-coated GFRP bars but yielded conservative results with closely spaced stirrups. CSA S6:25 predicted bond-strength values that were close to the experimental results compared to CSA S6:19, and CSA S806:12. 

Abstract The current code provisions in ACI 440.11 are based on the flexural theory that applies to slender members and may not represent the actual structural behavior when the shear span-to-reinforcement depth ratio is less than 2.5 (i.e., deep members). The Strut-and-tie method (STM) can be a better approach to design deep members; however, this chapter is not included in the code. Research has shown that STM models used for steel-reinforced concrete (RC) give satisfactory results when applied to glass fiber-reinforced polymer-reinforced (GFRP)-RC members with a/d less than 2.5. Therefore, this study is carried out to provide insights into the use of STM for GFRP-RC deep members based on the available literature and to highlight the necessity for the inclusion of a new chapter addressing the use of STM in the ACI 440.11 Code. It includes a design example to show the implications of ACI 440.11 code provisions when applied to GFRP-RC deep members (i.e., isolated footings) and compares it when designed as per STM provided in ACI 318-19. It was observed that current code provisions in ACI 440.11 required more concrete thickness (i.e.,h = 1.12 m) leading to implementation challenges. However, the required dimensions decreased (i.e.,h = 0.91 m) when the design was carried out as per STM. Due to the novelty of GFRP reinforcement, current code provisions may limit its extensive use in RC buildings, particularly in footings given the water table issues and excavation costs. Therefore, it is necessary to adopt innovative methods such as STM to design GFRP-RC deep members if allowed by the code. 

Abstract Glass fiber reinforced polymer (GFRP) bars are composite materials that, in the field of civil engineering, serve as an alternative for the internal steel reinforcement of concrete structures. The study and development of these material systems in construction are relatively new, requiring targeted research and development to achieve greater adoption. In this scenario, research and standardization play crucial roles. The development and publication of new test methods, material specifications, and other standards, as well as the improvement of the existing ones, allow for quality control, validation, and acceptance. One of these improvements is the evaluation of precision statements of the different ASTM standards related to the physical-mechanical and durability characterization of GFRP bars used as internal concrete reinforcement. Precision refers to how closely test results obtained under specific conditions agree with each other. A precision statement allows potential users to assess the test method’s general suitability for their intended applications. It should provide guidance on the type of variation that can be expected between test results when the method is used in one or more competent laboratories. The present study aims to enhance the precision statements in ASTM standards pertaining to the geometric, material, mechanical, and physical properties required for GFRP bars in concrete reinforcement, including ASTM standards like ASTM D7205M-21, Standard Test Method for Tensile Properties of Fiber Reinforced Polymer Matrix Composite Bars; ASTM D7617M-11(2017), Standard Test Method for Transverse Shear Strength of Fiber-Reinforced Polymer Matrix Composite Bars; and ASTM D7913M-14(2020), Standard Test Method for Bond Strength of Fiber-Reinforced Polymer Matrix Composite Bars to Concrete by Pullout Testing, while in accordance with the statistical procedures and calculation methods outlined in ASTM Practices ASTM E177-20, Standard Practice for Use of the Terms Precision and Bias in ASTM Test Methods, and ASTM E691-22, Standard Practice for Conducting an Interlaboratory Study to Determine the Precision of a Test Method. 

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.