Stefan Vranjevac

This paper presents perhaps the shortest possible algorithm for drafting a turbo block in a standard CAD environment. Though not defining the complete turbo roundabout geometry (the legs are defined by their centerlines only), turbo block construction stays at the very core of its geometrical definition. Relevant national policies elaborate turbo block construction at various levels of detail. Some policies skip certain definitions of spirals’ centers and some even allow for slight discontinuities in spiraling lanes’ geometries. But, as our computer program had to be based on precisely defined geometrical features, no question, be it a location of a particular center or relation between the interconnecting arcs, could be left unanswered. Consequently, while explaining the basics on which the program relies, all the elements of the turbo block are discussed. Even the problems not addressed in renowned national policies (such as alternatively matching marking and curb lines) had to be resolved before being incorporated into the program. After resolving and elaborating turbo block roundabout technics in detail, a new cyclic algorithm for turbo block construction is presented. The program is very short and could be easily understood and modified by the average programmer.

The low-speed turning maneuverability of vehicles is closely related to a well-known offtracking phenomenon which occurs when the rear wheels of a turning vehicle deviate towards the inside of a horizontal curve. Although numerous mathematical models and computer programs for vehicle swept path analysis have been developed in the past, only a few of them can calculate the maximum transient offtracking of a turning vehicle, yet with limited accuracy. The authors were motivated by this fact to find a new mathematical solution for maximum transient offtracking calculation of a single-unit vehicle when negotiating circular curves. In the first stage, a transcendental equation defining vehicle maximum transient offtracking position is derived and numerically solved by Python 3.10.12. In the second stage, the polynomial regression model predicting accurate numerical solutions of the transcendental equation with the desired level of accuracy was developed and tested. The new calculation method is simple enough to simply take the vehicle datum length, circular curve radii, and turn angle, while instantly producing the maximum transient offtracking value, without the need to draw any of the vehicle movement trajectories.

The lifespan of a structure encompasses its initial construction, followed by its operational phase, and ultimately, its rehabilitation. To accurately determine the optimal timing for rehabilitation, it is essential to continuously monitor the current state of the structure. Applied to road infrastructure, this monitoring process entails the systematic collection of data on pavement damage. This paper provides an analysis of existing models for detecting pavement cracks and introduces a detection method leveraging artificial intelligence techniques, specifically deep neural networks, and transfer learning. During the selection process, various deep neural network architectures, including ResNET 50 and ResNET 34, were evaluated. Comparative analysis revealed that the ResNET 50 network achieved the highest accuracy (Acc) in crack detection