Çıbıkçı, Kübra Çağla

In automotive, aviation, marine and advanced engineering applications, sandwich structures are increasingly preferred due to their superior properties such as high strength to weight ratio, wear resistance, and good surface quality. In this study, the natural frequencies and mode shapes of sandwich structures were investigated, and the effects of different boundary conditions and material combinations on their vibration characteristics were evaluated. Modal analyses were performed using the Finite Element Method (FEM) via ANSYS software, and the generated model was validated by comparing with existing literature data. In the model, carbon steel and carbon fiber reinforced plastic (CFRP) were used as the face sheet materials, while polyurethane elastomer matrix fiber and PVC foam were used as the core material. According to the results, the sandwich structure with a PVC foam core exhibited a 178.6% increase in the first natural frequency compared to the structure with a polyurethane elastomer matrix fiber core. Boundary conditions had a significant impact on natural frequencies, with the completely fixed (C–C–C– C) configuration yielding values 28.6% higher than the completely free (F–F–F–F) condition. Sandwich structures with fully CFRP face sheets showed a 27.7% higher natural frequency than those with hybrid face sheets. These findings demonstrate that the dynamic performance of sandwich structures is highly sensitive to boundary conditions as well as the types of core and face sheet materials, emphasizing the importance of appropriate material and geometric configuration in engineering design.

Aslay, Semi Emrah

In this study, contract price in public construction tenders are predicted using structural project parameters. The variables applied in the study are created by adding the quantities of columns, shear walls, and beams to variables commonly used in the literature for cost estimations. Six different machine learning algorithms are employed as machine learning algorithms. Preprocessing methods and a series of parameter optimizations are applied to enhance the predictive capability on datasets. These processes and the applied algorithms are evaluated with five different performance metrics. The SVM algorithm produced the best results, achieving an value of 0.8966, MAPE of 23.70, NSE of 0.8956, MAE of 0.4849, and RMSE of 0.6989. This study contributes to the literature by developing machine learning models and data analysis processes for contract price approaches.

Katipoğlu, Okan Mert; Acar, Resat

Ensuring more reliable and quality meteorological and climatological studies by providing data continuity and widening the data range. For this reason, missing values in meteorological data such as temperature, precipitation, evaporation must be completed. In this study, an artificial neural network (ANN) model was used to complete missing temperature data in the Horasan meteorology station. To establish the ANN model, monthly average temperature values of neighboring stations having similar climatic characteristics and altitude with Horasan were used as input. The monthly average temperature values of the Horasan station were used as output. Approximately 70% of the data was used for training, about 15% for testing, and about 15% for verification in the ANN model. Various statistical parameters were compared to determine the best network architecture and best model. As a result, the model's high determination coefficient (R2 = 0.99) and low mean absolute error (MAE = 0.61) showed that the ANN model can be used effectively in estimating missing temperature data.