Various human activities have been the main causes of surface water pollution. The uneven distribution of industrial enterprises in the territories of the main river basins of Ukraine do not always allow the real state of the water quality to be assessed. This article has three purposes: (1) the modification of the Ukrainian method for assessing the WQI, taking into account the level of negative impact of the most dangerous chemical elements, (2) the modeling of WQI assessment using fuzzy logic and (3) the creation of an artificial neural network model for the prediction of the WQI. The fuzzy logic model used four input variables and calculated one output variable (WQI). In the final stage of the study, six ANN models were analyzed, which differed from each other in various loss function optimizers and activation functions. The optimal results were shown using an ANN with the softmax activation function and Adam’s loss function optimizer (MAPE = 9.6%; R² = 0.964). A comparison of the MAPE and R² indicators of the created ANN model with other models for assessing water quality showed that the level of agreement between the forecast and target data is satisfactory. The novelty of this study is in the proposal to modify the WQI assessment methodology which is used in Ukraine. At the same time, the phased and joint use of mathematical tools such as the fuzzy logic method and the ANN allow one to effectively evaluate and predict WQI values, respectively.

The construction sector has a large impact on the environment and available resources. Natural resources and energy consumption occurs not only during the operation of the facility, but also during its construction. In addition, this situation often occurs when work already completed requires rework. In such cases, not only the reuse of resources and energy occurs but also generation of waste. Many studies support the relationship between communication and project efficiency, which is expressed in the cost of rework. At present there is no available tool to quantify the evaluation of this relationship. This study aims to fill this knowledge gap. The article purpose was to create ANNs (artificial neural networks) for assessing and predicting the impact of communication factors on rework costs in construction projects. During the data collection phase, 12 factors that influence communication were identified and assessed. The level of rework costs in 18 construction projects was also calculated. We used ANN, which is a two-layer feedforward network with a sigmoid transfer function in the hidden layer and a linear transfer function in the output layer. The network input layer consists of 12 neurons while the hidden layer consists of 10 neurons and one output neuron. The optimal results of the mean square error and correlation were shown by the Levenberg–Marquardt algorithm. The proposed model can be used by project management as the integration decision support tool aimed at decreasing the number of reworks and reducing energy and resource consumption in construction projects.

The purpose of the study is to use a social network analysis of a construction project’s participants to identify key participants using centrality measures and identify communities of participants in the network. This article analyses the communication network of a construction project that consists of 34 participants. Analysis of four centrality measures of the network’s nodes showed that there was a significant information load for a few key participants. The Eigenvector Centrality was chosen as the most appropriate basic measure of centrality because it takes into account the neighbouring nodes’ level of importance. The Louvain clustering method was found to be more effective than the Girvan–Newman method. The Louvain algorithm divided the project communication network into three communities, in which the participants are interconnected by the technological processes and the work performed. A hypothetical example is presented of how the clustering technique can be used to improve project communication. Adding a ‘Project Manager Assistant’ was selected for addition, and assumptions made to demonstrate how the load might be reduced and effectiveness assessed. These methods of assessing centrality and clustering show potential in project management to analyse a real communication network and when making managerial decisions.

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.

Exceeding the approved budget is often an integral part of the implementation of construction projects, especially those where unforeseen threats may occur. Therefore, each construction investment should contain elements of risk forecasting, mainly in terms of the cost of its implementation. Only a small number of institutions apply effective cost control methods, taking into account the specifics of a given industry. Especially small construction companies that participate in the structure of the implementation of large construction projects as subcontractors. The article presents a method by which it is possible to determine, with certain probability, the final cost of railway construction investments carried out in Poland. The method was based on a reliable database of risk factors published in sources. In this article, the main presumptions of the original method are presented, which take into account the impact of potential, previously recognized, risks specific to railway investments, and enable project managers to relate them to the conditions where the implementation of a specific object is planned. The authors assumed that such a relatively simple method, supported by a suitable computational program, would encourage teams that plan to implement railway projects to use it and increase the credibility of their schedules.

The properties and behavior of soils depend on many factors. The interaction of individual factors is difficult to determine by traditional statistical methods due to their interdependence. The paper presents a procedure of creating an artificial neural network (ANN) model to determine the value of the damping ratio (D) of clay soils. The main purpose of this paper is to compare the appropriateness of ANN model application with empirical formulas described in the literature. The ANN model was developed using a series of laboratory tests of the damping ratio performed in the Resonance Column. Predicted values of the damping ratio of clay soils obtained from the ANN model are characterized by high convergence (coefficient of determination R² = 0.976). In comparison with other published empirical formulas, the ANN model showed an improvement in the prediction accuracy. What is more, ANN models proved to be more flexible compared to formulas and relationships with a predetermined structure, and they were well suited to modeling the complex behavior of most geotechnical engineering materials, which, by their very nature, exhibit extreme variability. In conclusion, ANNs have the potential to predict the damping ratio (D) of clay soils and can do much better than traditional statistical techniques.