Статті КТМ (ДІІТ)

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Asphalt Pavement Damage Detection through Deep Learning Technique and Cost-Effective Equipment: a Case Study in Urban Roads Crossed by Tramway Lines
(MDPI, 2024) Guerrieri, Marco; Parla, Giuseppe; Khanmohamadi, Masoud; Neduzha, Larysa
ENG: Asphalt pavements are subject to regular inspection and maintenance activities over time. Many techniques have been suggested to evaluate pavement surface conditions, but most of these are either labour-intensive tasks or require costly instruments. This article describes a robust intelligent pavement distress inspection system that uses cost-effective equipment and the ‘you only look once’ detection algorithm (YOLOv3). A dataset for flexible pavement distress detection with around 13,135 images and 30,989 bounding boxes of damage was used during the neural network training, calibration, and validation phases. During the testing phase, the model achieved a mean average precision of up to 80%, depending on the type of pavement distress. The performance metrics (loss, precision, recall, and RMSE) that were applied to estimate the object detection accuracy demonstrate that the technique can distinguish between different types of asphalt pavement damage with remarkable accuracy and precision. Moreover, the confusion matrix obtained in the validation process shows a distress classification sensitivity of up to 98.7%. The suggested technique was successfully implemented in an inspection car. Measurements conducted on urban roads crossed by tramway lines in the city of Palermo proved the real-time ability and great efficacy of the detection system, with potentially remarkable advances in asphalt pavement examination efficacy due to the high rates of correct distress detection.
Development of a Base Material–Barrier Coating System Using Affordable Raw Materials for the Sustainable Production of Critical Railway Components
(MDPI (Basel, Switzerland), 2026) Kniaziev, Sergey; Guerrieri, Marco; Kniazieva, Hanna; Trembach, Bohdan; Babyak, Mykola O.; Neduzha, Larysa
ENG: The promising potential of porous metallic materials for railway applications (e.g., conductive materials, materials for braking systems) is due to their unique combination of low density, high specific surface area, and high energy absorption capabilities. Porous multi-phase silicide coatings (FeSi, Si2CN4) provide a synergistic effect, doubling surface hardness and establishing a stable diffusion barrier. The article proposes a comprehensive approach to replacing materials for critical railway transport components, involving the development of a base material and a barrier coating. The use of widely available induction-melting components to produce a base material with superior mechanical properties is demonstrated. The material exhibits high static strength and hardness while maintaining acceptable impact toughness and ductility. To enhance wear, corrosion, and scale resistance, technology for forming a barrier layer via silicide coatings is proposed. The coating formation technology enables the regulation of porosity through the formation of nitrogen-containing phases. It is shown that pores can serve as “containers” for fillers that impart functional properties to the coatings (e.g., adjusting the friction coefficient or electrical conductivity). The new base material–barrier coating system can serve as a foundation for the sustainable production of critical rolling stock parts and other devices for railway transportation systems.