Search for: All records

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 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. 

Pultruded FRP composites have emerged as a promising alternative to traditional materials like concrete, steel, and timber, especially in corrosive environmental conditions. However, the unique properties of these composites necessitate careful consideration during their implementation, as they differ significantly from conventional materials. Proper testing and characterization of FRP pultruded materials is key for their efficient and safe implementation. However, the existing specifications are not unified, resulting in ambiguity among stakeholders. This paper aims to bridge this gap by thoroughly reviewing current destructive and non-destructive test methods for FRP pultruded materials, specifications, quality control, and health monitoring of FRP structures. Each subsection is further divided into subtopics, providing a comprehensive overview of the subject. By shedding light on these crucial aspects, this article aims to accelerate the adoption and utilization of these innovative materials in practical applications. 

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.