Journal of Structural Engineering & Applied Mechanics

Muhammet Karabulut

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 degrees, and at 500 degrees 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 degrees and 300°C degrees, a 35% reduction was calculated at 300 °C degrees.

https://doi.org/10.31462/jseam.2025.01001012

Yılmaz Yılmaz Safa Nayır Şakir Erdoğdu

The compressive strength (CS) of concrete is a critical parameter for the safety and longevity of structures as it directly affects the load-bearing capacity and durability. However, determining the CS by conventional methods is time-consuming, costly, and requires a large amount of sample preparation. This study aims to quickly and cost-effectively predict and classify the CS of concrete and determine the influence of components on strength using machine learning (ML) algorithms as an alternative to traditional methods. Support Vector Machines (SVM), Decision Trees (DT), Multilayer Perceptron (MLP) machine learning algorithms, and Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB) ensemble learning models were trained on a dataset consisting of 1030 data points of concrete containing fly ash (FA) and blast furnace slag (BFS). The dataset was split into 75% training and 25% testing, and the Grid Search method and 5-fold cross-validation were applied in the training process. According to the results of the study, the XGB model showed the most robust performance in the prediction and classification of the CS of concrete with an R2 of 0.931 and an accuracy of 0.901. However, SVM and DT demonstrated inferior performance relative to the other four models. In addition, the models classified normal-strength concrete more successfully than low and high-strength concrete. It was determined that the two most effective factors on CS were concrete age and cement dosage. While the increase in concrete age and cement dosage increased the strength, the increase in water content decreased the strength.

https://doi.org/10.31462/jseam.2025.01013030

Muhammet Karabulut

Standards and approaches play a crucial role in assessing any engineering issue. This study highlights the differences between the maximum permitted seismic coefficients in various standards and approaches and examines how these differences affect the performance of clay-core rockfill dams. Specifically, the paper compares the behavior of a Clay Core Rockfill (CCR) dam under nine different pseudo-seismic coefficient standards. The dam is evaluated under two material conditions: elastic and plastic. For the analysis, the Düzçam CCR Dam, located in Karabük, Turkey, is chosen as a case study. The Düzçam Dam, with a height of 54 meters and an irrigation area capacity of 5,615 decares annually, is modeled for evaluation. The most critical section of the dam is selected for the two-dimensional model, which is constructed using the finite element method. The Düzçam Dam's two-dimensional finite element model is created using Phase2 software, and the material and soil mechanical properties are derived from experimental data. Numerical analyses are performed in four stages for each of the nine different standards: first, gravity loading; second, dam body construction; third, water application; and finally, the application of the pseudo-seismic coefficient to the dam body and rock foundation model. In cases where the pseudo seismic coefficient is 0.15 and 0.5, the displacement increases by approximately 61.5%, while the principal tensile and compressive stress increases by approximately 30% and 33%, respectively. The impact of selecting the maximum pseudo-seismic coefficient on the results is demonstrated.

https://doi.org/10.31462/jseam.2025.01031048