Increased concentrations of chemicals in surface waters affect the development of fish and the state of water bodies in general. In turn, the human consumption of fish that have accumulated heavy metals can cause toxicological hazards and endanger health. The importance of this area and the lack of water quality assessment methods in Ukraine based on the fluctuating asymmetry level of fish and the chemical parameters of water informed the object and aim of the current research. The object of this study was the use of fish populations as a bioindicator of water quality. The study had three purposes: (1) the determination of the dominant fish species and a comparison of their fluctuating asymmetry in the studied rivers; (2) the evaluation of the sensitivity/tolerance of the selected fish populations for assessing water quality; and (3) the creation of a model for assessing the water quality of the studied rivers based on the determined fluctuating asymmetry of the typical fish populations. Each of the studied fish populations had different frequency of fluctuating asymmetry (FFA) levels: the common roach had the highest value, and the silver crucian carp had the lowest. The final stage of the study was building an artificial neural network (ANN) model for predicting water quality based on the FFA of meristic features. Optimal results were obtained for the ANN model with the ReLU activation function and SGD optimization algorithm (MAPE = 6.7%; R² = 0.97187). Such values for the MAPE and R² indicators demonstrated that the level of agreement between the target and forecast data was satisfactory. The novelty of this research lay in the development of a model for assessing water quality based on the comparison of the fluctuating asymmetry values of the typical fish populations in the studied rivers.

Bridges in Ukraine are one of the most important components of the infrastructure, requiring attention from government agencies and constant funding. The object of the study was the methodology for quantifying the condition of bridge components. The Artificial Neural Network-based (ANN) tool was developed to quantify the technical condition of bridge components. The literature analysis showed that in most cases the datasets were obtained during the inspection of bridges to solve the problems of assessing the current technical condition. The lack of such a database prompted the creation of a dataset on the basis of the Classification Tables of the Operating Conditions of the Bridge Components (CT). Based on CTs, five datasets were formed to assess the condition of the bridge components: bridge span, bridge deck, pier caps beam, piers and abutments, approaches. The next step of this study was creating, training, validating and testing ANN models. The network with ADAM loss function and softmax activation showed the best results. The optimal values of MAPE and R² were achieved at the 100th epoch with 64 neurons in the hidden layer and were equal to 0.1% and 0.99998, respectively. The practical application of the ANN models was carried out on the most common type of bridge in Ukraine, namely, a road beam bridge of small length, made of precast concrete. The novelty of this study consists of the development of a tool based on the use of ANN model, and the proposal to modify the methodology for quantifying the condition of bridge components. This will allow minimizing the uncertainties associated with the subjective judgments of experts, as well as increasing the accuracy of the assessment.

The article is devoted to the development of methodological basis, methodological support and applied tools of digital administration of operational activities, development and transformations of management systems of construction companies. The results of the work together provide a modern methodological system for managing the transformation processes of construction companies in terms of digitalization of the economy. The methodology is implemented in scientific and applied tools for decision-making on the content, tools, diagnostic indicators and the expected result of the transformation of the operating system and organizational structure of the construction company, which carries out and adjusts its activities in a complex multi-project operating field. The conceptual principles of business process renewal, management systems for a construction company based on digitalization (digital administration), which are positioned as a means of achieving competitive advantages in the multi-project field of construction development, are substantiated. The paper identifies the impact of the transformation of modern global socio-economic processes on the methodology of managing innovative business development in a digital economy. The significance of innovations for increasing business efficiency is revealed and the concept of innovation is developed. The content of the main terminological definitions in the form of a terminological thesaurus "transformation of the operating system of a construction company" has been clarified. The influence of the institutional and organizational environment on the innovative development of business in Ukraine is determined. The author 's methodological approach to the formation of the conceptual foundations of the digital economy is substantiated. Principles and roadmap for digital business transformation have been developed. Theoretical and methodological approaches to modeling the information technology architecture of the organization are proposed. The methodology of digital administration of construction stakeholder enterprises introduced in the work combines modern principles of strategic and operational management of enterprises with "cloud" and BIM-technologies, applied embodiments of functional-economic diagnostics, cost-oriented management, object-functional and functional-functional and functional management. with the purpose of their integrated use for the implementation of qualitative transformations of enterprise management systems and the microenvironment of development projects.

The article investigates the target principles of information modeling concepts in construction (BIM) as an innovative direction of updating the operating systems of construction companies in an unstable economic business environment. Based on the world experience, the preconditions and necessity of introduction of the concept of digital transformation of construction organizations are determined and substantiated. The main obstacles to the digital transformation of construction organizations are identified and characterized: transformational, innovative and managerial. The main stages of formation of a successful digital strategy of a construction organization are formed. To develop a unified vision of the digital transformation of the organization, it is proposed to radically restructure the processes in order to get the most out of digitalization, staff training and recruitment, taking into account the requirements of their digital literacy. The difficulties and benefits that may arise when implementing information modeling are analyzed and systematized. The competitive advantages that participants of the investment and construction process receive from the use of BIM are formulated. Information modeling technologies in construction can be an important tool for big data analysis, information generation and knowledge, because after the completion of each stage of the construction project remains large data sets. Accumulated data can be a corporate asset, the acquisition and use of which allows you to make better forecasts and make the right management decisions. In addition, the knowledge gained in the project and tested in practice can be considered more reliable than the data of experiments or simulations, because they contain more basic knowledge of reality.