In the context of growing demand for road infrastructure expansion, sustainability is of key importance. Enhancing road construction technology ensures efficient, durable transportation infrastructure, vital for economic growth. Responsible road construction, especially in areas of historical and environmental significance, is essential. Preserving ecosystems and local heritage in road planning protects valuable areas and supports regional sustainability. Rural roads, often in forested areas, should maintain natural aesthetics. Economic challenges require flexible, efficient roads. Many existing unpaved roads, especially in rural and forest environments, fail to meet increasing traffic and load of heavy agricultural and maintenance vehicles. Soil stabilization methods, discussed in various publications, offer sustainable solutions. Research on ecofriendly binding additives provides the opportunity to reduce cement consumption and CO₂ emissions. Such innovations enhance road efficiency, reduce maintenance costs, and benefit the environment. Investing in modern road technologies mitigates climate change effects by reducing emissions and protecting ecosystems. Our research indicates that soil stabilization technologies significantly improve road strength and durability. Additives can increase compressive strength by over 50% with minimal cement content. Continued research into road quality and durability is vital for sustainable transport infrastructure development. The results presented in this paper were obtained after two periods of solidification: after 7 and 30 days, with three types of ads (ion-exchangeable, chemical, and polymeric) to cement. In all tests, compaction strength improved by at least 35% in samples with the lowest cement content.

Implementing rain garden (RG) designs is widespread worldwide to reduce peak flow rates, promote stormwater infiltration, and treat pollutants. However, inadequate RG design degrades its hydrological behaviour, requiring the development and validation of an appropriate hydrological model for the design and analysis of structures. This study aimed to improve a hydrological infiltration model based on Darcy’s law by taking into account the height of the water column (HWC) at the surface of the RG and the filtration coefficients of soil materials. The model was tested by simulating the hydrological characteristics of a rain garden based on a single rain event of critical intensity (36 mm/h). Using the validated model, design curves were obtained that predict the performance of the RG as a function of the main design parameters of the structure: water column height, ratio of catchment area to structure area, layer thickness, and soil filtration coefficient. The hydrological efficiency of the RG was evaluated in terms of the time of complete saturation, filling of the structure with water, and determining the change in HWC caused by changes in the parameters. The filtration coefficient and thickness of the upper and intermediate infiltration layers of the RG are the main parameters that affect the depth of saturation of the layers of the structure and the HWC on the surface. The model is not very sensitive to the model parameters related to the lower gravel layer. If the top layer’s thickness increases by 10 cm, it takes longer to fill the structure with water, and the HWC on the surface reaches 0.341 m. The rain garden’s performance improves when the filtration coefficient of the top layer is 7.0 cm/h. Complete saturation and filling of the structure with rainwater do not occur within 7200 s, and the water column reaches a height of 0.342 m at this filtration coefficient. However, the rain garden’s effectiveness decreases if the filtration coefficient of the upper and intermediate layers exceeds 15 cm/h and 25 cm/h, respectively, or if the catchment area to RG area ratio decreases to values below 15. The modelling results confirm that considering the HWC in RG hydrological models is essential for designing structures to minimise the risk of overflow during intense rainfall events.

The main task of the operation of engineering structures is to ensure the stability of structures with aggressive external influences, which have a complex probabilistic nature. The reliable functioning of bridge structures requires the development and application of modern systems for inspection and assessment of the technical condition of the structure to take timely measures to ensure the safe operation of the structure in changing operating conditions. With the rapid development of AI, modern approaches are increasingly adopted, offering distinct advantages compared to classical methods. The article aims to develop an AI-based model for quantifying the technical condition of bridge structural components based on data obtained from the survey. To achieve this goal, the authors analyzed existing approaches to the inspection and assessment of bridges and studied the experience of using AI in bridge assessment. Based on the Polish Principles of Bridge Technical Condition Assessment, three datasets were formed to quantify the condition of the bridge components made from reinforced concrete: bridge deck, span structures, and piers and abutments. This study created and compared the performance of five AI-based models: XGBoost, Decision Trees, Random Forest, Support Vector Regression, and Artificial Neural Networks (ANNs). The initial comparison revealed relatively low performance across all models, with the ANN model showing a slight advantage. Subsequently, nine ANN models were optimized to achieve higher performance levels. The performance of models was conducted based on a comparison of mean absolute percentage error (MAPE) and R² metrics. The ANN model with ReLU activation functions for hidden layers and the RMSprop optimizer achieves optimal performance at 100 epochs (MAPE = 3.5%; R² = 0.994). The practical implementation of such a model can considerably reduce uncertainties stemming from subjective expert judgments and enhance the accuracy of assessments.

The purpose of this study is to present the current changing situation of construction markets in selected European countries. Current trends in construction production are presented in relation to changes resulting from the coronavirus pandemic and other factors using the examples of France, the Netherlands, Belgium, Austria, Germany, Spain and Poland. The EU Construction Confidence Index (CCI) and data on the number of building permits issued in selected countries were used for inference. Additional sources of knowledge were publicly available information on ongoing and planned campaigns and government actions affecting local construction markets. The analysis presented shows a clear impact of the pandemic on construction production trends, an increase in optimism in the industry at its extinction and renewed declines in optimism due to the increasing destabilisation of economies as a result of inflation, rising energy prices and the war in Ukraine. Despite the difficulties, construction markets are slowly regaining their pre-pandemic growths, although the aforementioned factors are slowing them down. Strong mature economies, supported by additional government funding, will recover much faster than developing countries where uncertainty is greater. The negative indicators for Poland forecast a slowdown and a longer period of return to pre-pandemic COVID-19 growth.

The treatment of contaminants from road infrastructure poses significant challenges due to their variable composition and the high concentrations of chloride ions, heavy metals, and oil-derived substances. Traditional methods for protecting groundwater environments are often insufficient. A promising alternative is permeable reactive barrier (PRB) technology, which utilizes recycled materials and construction waste as reactive components within the treatment zone of the ground. This paper delves into the potential of employing a composite (MIX) consisting of modified construction aggregate (as recycled material) and activated carbon (example of reactive material) to address environmental contamination from a mixture of heavy metals and chloride. The research involved chemical modifications of the road aggregate, activated carbon, and their composite, followed by laboratory tests in glass reactors and non-flow batch tests to evaluate the kinetics and chemical equilibrium of the reactions. The adsorption process was stable and conformed to the pseudo-second-order kinetics and Langmuir, Toth, and Redlich–Peterson isotherm models. Studies using MIX from a heavy metal model solution showed that monolayer adsorption was a key mechanism for removing heavy metals, with strong fits to the Langmuir (R² > 0.80) and Freundlich models, and optimal efficiencies for Cd and Ni (R² > 0.90). The best fit, at Cd, Cu, Ni = 0.96, however, was with the Redlich–Peterson isotherm, indicating a mix of physical and chemical adsorption on heterogeneous surfaces. The Toth model was significant for all analytes, fitting Cl and Cd well and Pb and Zn moderately. The modifications made to the composite significantly enhanced its effectiveness in removing the contaminant mixture. The test results demonstrated an average reduction of chloride by 85%, along with substantial removals of heavy metals: lead (Pb) by 90%, cadmium (Cd) by 86%, nickel (Ni) by 85%, copper (Cu) by 81%, and zinc (Zn) by 79%. Further research should focus on the removal of other contaminants and the optimization of magnesium oxide (MgO) dosage.

The use of composite materials in additive manufacturing has significant potential and prospects for development. However, the 3D printing of composite materials also has some challenges, such as tool path planning and optimization, material distribution and planning, optimization of printing parameters, and others. Graph theory may be suitable for solving some of them. Many practical problems can be modeled as problems of identifying subsets of graph vertices or edges with certain extremal properties. Such problems belong to the category of graph extremal problems. Some of these problems can be represented as integer linear programming problems, for which, in order to solve, modifications of simplex method can be used. These methods are supported by MS Excel Solver add-in, which suggests the possibility of solving these problems effectively with its help. The task of implementing procedures for solving such problems by means of standard engineering software seems to be possible. This paper aims to develop efficient spreadsheet models of some extremal problems for graphs of higher strength in order to prove the feasibility and to unify the procedures of solving such problems via the MS Excel Solver add-in. Several spreadsheet models based on the graph representation by its expanded incidence matrix, while specifying a vector of unknowns as the vector of binary variables associated with vertices or edges of the sought parts of the graph, have been developed and proven to be efficient for solving such problems by simplex method via the MS Excel Solver add-in.