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 purpose of this research is to study the communication network among the participants in a construction project. The proposed approach contains the solving of the following tasks: identification of the key factors that affect the communication quality; calculation of the “communication quality” indicator between each pair of participants using fuzzy logic; and evaluation of the key measures of centrality using the tools of a Social Network Analysis. Solving the set tasks can help break the problem of the low level of efficiency of construction project deliveries. This study was conducted when the design stage was already complete and the construction project had 17 participants. We used the following factors influencing communication quality: timeliness, understanding, bidirectionality, use of IT tools in communication acts and adequacy. The use of fuzzy logic tools can minimise the subjectivity of the qualitative data obtained from the questionnaires. The analysis of five centrality measures showed that “Project manager”, “Chief engineer”, “Construction site manager”, “Construction works manager” and “Architect” had the greatest values in comparison with other network participants. On the one hand, this organisational structure of communication can be considered correct because the main information flows through the project managers, who make the decisions. On the other hand, this indicates a significant dependence of the construction project on these participants and, in the event of certain risk events, may lead to a decrease in the efficiency of its implementation. The methods presented in this article can be used in project management to analyse a real communication network.

For groundwater treatment, the technologies involving oxidation on MnO_x filter bed are beneficial, common, and effectively used. The presence of MnO_x is the mutual feature of filter media, both MnO_x-coated mineral materials like quartz sand and gravel, chalcedonite, diatomite, glauconite, zeolite, or anthracite along with consisting of MnO_x manganese ores. This review is based on the analysis of research and review papers, commercial data sheets, and standards. The paper aimed to provide new suggestions and useful information for further investigation of MnO_x filter media for groundwater treatment. The presented compilations are based on the characteristics of coatings, methods, and conditions of its obtaining and type of filter media. The relationship between the properties of MnO_x amendments and the obtained purification effects as well as the commonly used commercial products, their features, and applications have been discussed. The paper concludes by mentioning about improving catalytic/adsorption properties of non-reactive siliceous media opposed to ion-exchange minerals and about possible significance of birnessite type manganese oxide for water treatment. Research needs related to the assessment of the use MnO_x filter media to heavy metals removal from groundwater in field operations and to standardize methodology of testing MnO_x filter media for water treatment were identified.

Sustainability emphasises the importance of increasing the resource efficiency of infrastructure. The usage of geosynthetic materials in civil and environmental engineering can significantly influence sustainability at the planning and design stages of infrastructure construction projects. They are used in many different applications in construction and environmental engineering, as they provide a better and longer performance and less costly solutions than traditional materials (such as sand, gravel, concrete and cement). Additional benefits can be achieved by combining geosynthetics with various recycled materials as substitutes for high-quality natural materials. In this paper, the importance of sustainability in geosynthetics-based solutions is discussed. The possibilities of using geosynthetics in sustainable development have been analysed and the benefits resulting from their application, such as the reduction in carbon footprint and release of greenhouse gases and saving water and other natural resources, have been assessed. Innovative solutions that support mitigation measures, adaptation to climate change and achievement of sustainable development goals have been presented.

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 study aimed to address the complex and critical issue of surface water quality monitoring by proposing a diversified approach that incorporates a range of chemical indicators. (1) Background: the purpose of the study was to address the problem of surface water quality monitoring in relation to the toxic effects of ammonium on aquatic ecosystems by developing predictive models using fuzzy logic and artificial neural networks. (2) Water samples from the Styr River, influenced by the Rivne Nuclear Power Plant, were analyzed using certified standard methods and measured parameters, while fuzzy logic and artificial neural network models, including Mamdani’s algorithm and various configurations of activation functions and optimization algorithms, were employed to assess water quality and predict ammonium toxicity. (3) A fuzzy logic system was developed to classify water quality based on ammonia content and other parameters, and six Artificial Neural Network (ANN) models were tested, with the ANN#2 model (using ReLU activation and ADAM optimizer) showing the best performance. (4) This study emphasizes the critical need for precise monitoring and modeling of total ammonium in surface water, considering its variable toxicity and interactions with environmental factors, to effectively protect aquatic ecosystems, namely ichthyofauna.