Due to increased activities in civil engineering, and especially in the transport infrastructure, and thus an increase in the use of additional construction materials, innovative solutions are needed in finding alternative materials for the preservation of natural resources. One of the solutions for preserving natural building materials is the application of recycled material, which will also meet the required standards in civil engineering. In the transport infrastructure, there is a great opportunity for the use of recycled material, starting from the subbase, through the embankment and all the way to the superstructure and the concrete structures. One of the recycled materials that can be used in construction is fly ash. Fly ash is the most promising waste material when it comes to the wide range of applications in construction. Fly ash is obtained by burning coal in thermal power plants to produce electricity. Due to variations in the composition and characteristics of fly ash, several divisions and classifications have been created. One of the main classifications is according to the American standard ASTM C 618-05 where it is divided into class C and class F fly ash. This research aims to investigate the application of fly ash for subgrade stabilization and to determine the difference from a financial and environmental perspective for the application of fly ash compared to natural stone aggregate. The main research was carried out in connection with the increased use of natural stone aggregate, which appeared as a need during the rehabilitation of state road R1206, section Karpalak - Zelino. Apart from the use of large quantities of stone aggregate for the stabilization of the subgrade, it also had an impact on the financial framework for the realization of the road project. In Macedonia, an analysis of the chemical composition of 4 samples of fly ash obtained in REC Bitola was carried out. From the obtained results, it can be concluded that it belongs to the classification of F-class fly ash and with the use of an activator (lime or cement) it can be used in the stabilization of the subgrade.

The influence of road and environmental characteristics on traffic safety is a key aspect of the analysis of traffic accidents. To gain a more detailed understanding of this relationship, this study analyzes the factors that contribute the most to their frequency and severity, with an emphasis on their identification, quantitative assessment, and potential mitigation. The research focuses on the analysis of 161 sections of the main road network in the Republic of North Macedonia, with a total length of approximately 1300 km, using data related to road geometric characteristics, pavement conditions, vertical and horizontal signage, climatic influences, and traffic intensity. To assess their impact on the weighted accident index (Wi), multiple statistical and machine learning methods are applied, including correlation analysis and algorithms such as AdaBoost, Random Forest, Bagging Regressor, and Gradient Boosting, along with validation techniques. Thorough data processing and analysis enable the detection of critical factors with the greatest impact on safety, leading to the development of a methodological approach for predicting hazardous segments of the road network. The obtained results and the developed model can serve as effective tools for enhancing existing strategies for road safety assessment, allowing timely planning of interventions and reducing the risk of traffic accidents. This research represents a step towards the systematic identification of factors contributing to traffic accidents and provides a scientifically grounded approach to improving road safety measures.

The most of the existing road network in the Republic of North Macedonia consists of sections that in the past were designed to provide the most durable and permanent engineering solutions of the carriageway, with minimal commitment to the road geometry. The visibility on the road is a key condition that would significantly contribute to better driving dynamics as well as to road safety. An analysis of Stopping Sight Distance (SSD) on all main roads was conducted within the paper. The visibility is checked simultaneously, through video recordings and CAD drawings, and is recorded tabularly by sections. In the analysis, checks are treated in two projections with additional subtypes: Horizontal solution (open route and intersections) and vertical solution (for convex and concave curves). The obtained values are quantified according to the length of each section, thus determining the individual sizes for each type. According to the resulting diagram of all the sections and the range from minimum to maximum, an economically acceptable visibility limit is defined through which all existing and future roads are weighted and it is defined whether they have a satisfactory visibility or not.

Maintaining the geometric quality of railway tracks is essential for ensuring the safety and efficiency of railway operations. This study presents a comparative analysis of Multiple Linear Regression (MLR) and Random Forest (RF) models for predicting the Track Quality Index (TQI), based on historical inspection data collected from the mountainous Kolašin–Podgorica railway section between 2017 and 2022, with data from 2024 reserved for independent validation. The dataset includes high-resolution measurements divided into 20-meter homogeneous units, incorporating infrastructure, geometric, operational, and maintenance-related variables. Both models were trained on scaled input features, and their performance was evaluated using the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The results demonstrate that the machine learning approach significantly outperformed the statistical baseline; the RF model achieved a higher goodness-of-fit (R2 = 0.69 vs. 0.57) and reduced the average prediction error (MAE) by approximately 15% compared to the MLR model. Furthermore, RF exhibited superior stability in capturing severe localized degradation trends. These findings highlight the potential of ensemble machine learning methods to mitigate large prediction errors and enhance data-driven, proactive track maintenance planning in geometrically complex railway networks.