This study experimentally examines the flexural performance, crack formation patterns, and failure mechanisms of glass fiber-reinforced polymer (GFRP) bar-reinforced concrete beams with varying concrete compressive strengths (low, moderate, and high), addressing a gap in the current literature. Furthermore, it employs an innovative machine learning approach to enhance analysis. Nine RC beams reinforced with GFRP bars, having concrete compressive strengths of low (CC20), moderate (CC30), and high (CC40), each measuring 150 × 200 × 1100 mm, were fabricated and tested under three-point bending conditions. Through the integration of three-point bending tests and machine learning-based prediction models, this study connects experimental findings with advanced analytical approaches. One of the key innovations in this study is the use of eighteen ML regression models implemented with Python’s PyCaret library, achieving an impressive average prediction accuracy of 91.5% for RC beam deflection values. In particular, the Ada Boost Regressor and Gradient Boosting Regressor models performed exceptionally well on GFRP bar-reinforced concrete beams, providing the highest number of consistent and highly accurate predictions, making them very useful tools for GFRP bar-reinforced beam ultimate load-carrying capacity/deflection predictions. The outcomes identified clear failure mechanisms: RC beams with CC20, CC30, and CC40 concrete compressive strengths typically developed a single, large flexural crack at the midpoint. Although the ultimate load-carrying capacity of GFRP bar RC beams improved with higher concrete compressive strength, CC20 and CC30 beams displayed more ductile failure behavior than CC40 beams. The ultimate load-carrying capacity of CC40 RC beams was determined to be approximately 74% higher than that of CC20 RC beams. Regardless of the concrete compressive strength class, the absence of shear cracks and the prevention of sudden failure under bending in GFRP bar-reinforced concrete beams are considered major advantages of using GFRP bar reinforcement.

The integration of cutting-edge technologies into reinforced concrete (RC) design is reshaping the construction industry, enabling smarter and more sustainable solutions. Among these, machine learning (ML), a subset of artificial intelligence (AI), has emerged as a transformative tool, offering unprecedented accuracy in prediction and optimization. This study investigated the flexural behavior of steel rebar RC beams, focusing on varying concrete compressive strengths via theoretical, experimental and ML analysis. Nine steel rebar RC beams with low (SC20), moderate (SC30) and high (SC40) concrete compressive strength, measuring 150 × 200 × 1100 mm, were produced and subjected to three-point bending tests. An average error of less than 5% was obtained between the theoretical calculations and the experiments of the ultimate load-carrying capacity of reinforced concrete beams. By combining three-point bending experiments with ML-powered prediction models, this research bridges the gap between experimental insights and advanced analytical techniques. A groundbreaking aspect of this work is the deployment of 18 ML regression models using Python’s PyCaret library to predict deflection values with an impressive average accuracy of 95%. Notably, the K Neighbors Regressor and Gradient Boosting Regressor models demonstrated exceptional performance, providing fast, consistent and highly accurate predictions, making them an invaluable tool for structural engineers. The results revealed distinct failure mechanisms: SC30 and SC40 RC beams exhibited ductile flexural cracking, while SC20 RC beams showed brittle shear cracking and failure with sudden collapse.

In light of growing concerns over sustainability, particularly in the wake of environmental disasters like the Aznalcollar dam break, the Spanish authorities have heightened their awareness of issues surrounding ore tailings management. The main aim of this paper is to study the dynamic behavior for the dry closure of the Almagrera dam under the action of an earthquake. This study was carried out with the Plaxis 2D v9.02 program, which uses the finite element (FE) method. The dynamic analysis of the dam was interpreted in terms of deformations, displacements and principal stresses. The construction of the Uniform Seismic Hazard Acceleration Response Spectrum (USHARS) and the selection of real accelerograms for the time-history dynamic calculations is a noted feature of this research. Numerical analyses show that the dam is safe enough because a failure surface has not been formed, although several plastic zones may appear in the dam. The FE study of deformations display that the tailings may attain large deformations, displacements and failure, although this does not jeopardize the safety of the dam where the displacements are smaller than 3 mm. Neither the tailings nor the dam are expected to suffer liquefaction. It was determined that the 0.09 g threshold value is not exceeded in the acceleration-time graphs on the old reservoir field surface, which is the most critical situation.

Nowadays, the utilization of composite reinforcement instead of steel rebars in construction is considered as an alternative and many methods are being intensively studied on composite bars. The superior tensile strength, lightness, corrosion resistance, and long service life of GFRP composite reinforcements have made them a strong competitor to steel rebars. Reinforced concrete structures constitute a large part of the existing building stock in Turkey and around the world. Therefore, composite bars have the potential to be widely utilized. This study investigates the flexural strength of GFRP composite bars after exposure to distinct thermal stress. In the study conducted, 47 GFRP bar bending specimens with a length of 150 mm and a diameter of ϕ10 mm were tested with a 3-point bending test after the thermal process was completed until GFRP bars reached the ultimate load-carrying capacities. During the experiments, GFRP bars were exposed to 9 distinct temperature loads: 22°C, 100°C, 150°C, 175°C, 200°C, 225°C, 250°C, 300°C, and 500°C, respectively. As a result of the tests performed, GFRP bars reached the highest bending load value at 200°C, which is 13% higher than the average of the reference specimens tested at 22°C room temperatures. It is seen that in possible situations such as fire, the load-carrying capacity of GFRP bars will start to decrease after 200°C, and at 500°C, the resin completely melts, and the GFRP bars lose their rigidity and strength. When the average deflection values of the GFRP bar specimens are compared for 22°C and 300°C, a 35% reduction was calculated at 300°C.

Earthquake response of arch dams should be calculated under strong ground motion effects. In this study, three-dimensional linear response of an arch dam is investigated. Different ground motion effects and also foundation conditions are considered in the finite element analyses. For this purpose, the Type 3 double curvature arch dam was selected for numerical examples. All numerical analyses are carried out by SAP2000 program for empty reservoir cases. According to numerical analyses, maximum horizontal displacements and maximum normal stresses are presented by dam height in the largest section. These results are evaluated for five different elastic foundation conditions. The selected foundation conditions of the all models have different sandstone material parameters. Furthermore, near-fault and far-field ground motion effects on the selected arch dam are taken into account by different accelerograms obtained from the Loma Prieta earthquake at various distances.

Glass Fiber Reinforced Polymer (GFRP)-reinforced concrete beams have gained significant prominence in structural engineering due to their advantageous mechanical and durability characteristics. However, the influence of training–testing data partitioning on machine learning (ML)-based deflection prediction for such members remains insufficiently explored. This study addresses this gap by evaluating the predictive performance of the K-Nearest Neighbors (KNN) regression algorithm in estimating the load–deflection behavior of GFRP-reinforced high-strength concrete beams. The experimental program comprised nine beams manufactured with concrete strength classes C45, C50, and C65, followed by ML-based deflection analyses using multiple data-splitting strategies. Findings indicate that the KNN model employing an 80:20 training–testing ratio provides the most accurate deflection predictions, achieving approximately 80% agreement with experimental results, while a higher prediction accuracy of approximately 85% was observed for beams with the highest concrete compressive strength (C65). Experimentally recorded deflections ranged from approximately 20 mm to values exceeding 50 mm, depending on the concrete strength class and loading level. Owing to its superior performance, the KNN model with an 80:20 training–testing ratio is recommended for predicting the deflection capacities of GFRP-reinforced high-strength concrete members. The study further examined the structural response associated with the use of GFRP as longitudinal tensile reinforcement. A consistent failure mechanism was observed across all beams, characterized by the formation of a single, wide vertical crack initiating at the beam’s soffit, regardless of concrete strength class. These observations contribute to a deeper understanding of the flexural behavior and fracture characteristics of GFRP-reinforced high-strength concrete beams and provide a foundation for future modeling efforts.