Vehicle classification is a crucial task owing to vehicles' diverse and intricate features, such as edges, colors, shadows, corners, and textures. The accurate classification of vehicles enables their detection and identification on roads and facilitates the development of an electronic tollcollection system for smart cities. Furthermore, vehicle classification is useful for traffic signal control strategy. However, achieving accurate vehicle classification poses significant challenges due to the limited processing time for real-time applications, image resolution, illumination variations in the video, and other interferences. This study proposes a method for automated automobile detection, recognition, and classification using statistics derived from approximately 11,000 images. We employ SURF-based detection and different classifiers to categorize vehicles into three groups. The Traffic Management System (TMS) is crucial for studying mobility and smart cities. Our study achieves a high automobile classification rate of 91% with the medium Gaussian Support Vector Machine (SVM) classifier. The paper's main objective is to analyze five object classifiers for vehicle recognition: Decision Tree, Discriminant Analysis, SVM, K-Nearest Neighbor Classifier (KNN), and Ensemble Classifier. In the discussion section, we present the limitations of our work and provide insights into future research directions.

Using limited materials for numerical modeling of structural samples and comparing the behavior of samples with each other in order to reduce the number of straw test samples in order to reduce laboratory costs is one of the best methods today. ANSYS is one of the finite element software that is used for modeling reinforced concrete elements by Solid65 element. As a case study, a concrete-filled steel column under cyclic loading is examined and the best value for the Crushed Stiffness Factor (CSTIF) parameter that is one of Solid65 element parameters is suggested to confirm the modeling. By investigating the results, it can be concluded that this parameter is one of the most important factors that plays a significant role in convergence in ANSYS software for modeling CFT columns.

This paper presents the results of detailed geological investigations of the Middle Triassic dolomite deposit of Nikolin Potok, which is located west of Bugojno. Based on the established borders of surface distribution and research results, geological reserves of about 4.6 million m3 have been determined in the wider area of the deposit. The dolomite reserves that have been established so far in this area are at a low level of geological exploration. For this reason, the level of research should be significantly increased, because the calculated and confirmed reserves are very modest compared to the potential possibilities. The paper contains a description of the geological characteristics of the area and the qualitativequantitative characteristics of dolomite. The results of the conducted research point that the general geological and technical-exploitation factors are favorable and indicate profitable exploitation of the deposit in the coming period as well. Taking into account the significant raw material potential, and the possibility of expanding the existing raw material base, this area has a special significance for the perspective development of dolomite exploitation and its use in the production of technical-building stone.