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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. 

Computational tools have been used in structural engineering design for numerous objectives, typically focusing on optimizing a design process. We first provide a detailed literature review for optimizing truss structures with metaheuristic algorithms. Then, we evaluate an effective solution for designing truss structures used in structural engineering through a method called the mountain gazelle optimizer, which is a nature-inspired meta-heuristic algorithm derived from the social behavior of wild mountain gazelles. We use benchmark problems for truss optimization and a penalty method for handling constraints. The performance of the proposed optimization algorithm will be evaluated by solving complex and challenging problems, which are common in structural engineering design. The problems include a high number of locally optimal solutions and a non-convex search space function, as these are considered suitable to evaluate the capabilities of optimization algorithms. This work is the first of its kind, as it examines the performance of the mountain gazelle optimizer applied to the structural engineering design field while assessing its ability to handle such design problems effectively. The results are compared to other optimization algorithms, showing that the mountain gazelle optimizer can provide optimal and efficient design solutions with the lowest possible weight.