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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 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.