This study is devoted to solving the problem of determining the career guidance of future university student using artificial intelligence systems. It is proposed Fully Connected Feed-Forward Neural Network (FNN) architecture and performed empirical modeling to create a Data Set. Model of artificial intelligence system allows evaluating the processes in an FNN during the execution of multi-label classification of professional areas. A method has been developed for the training process of a machine learning model, which reflects the internal connections between the components of an artificial intelligence system that allow it to “learn” from training data. To train the neural network, a data set of 29 input parameters and 23 output parameters was used; the amount of data in the set is 936 data lines. The software product was developed by Python and uses Keras, Numpy and Pandas libraries. Results of this research can be used to further improve the knowledge and skills necessary for successful professional realization.

This study is devoted to solving the problem to determine the professional adaptive capabilities of construction management staff using artificial intelligence systems. It is proposed fully connected feed-forward neural network (FCF-FNN) architecture and performed empirical modeling to create a data set. Model of artificial intelligence system allows evaluating the processes in an FCF-FNN during the execution of multi-value classification of professional areas. A method has been developed for the training process of a machine learning model, which reflects the internal connections between the components of an artificial intelligence system that allow it to “learn” from training data. To train the neural network, a data set of 35 input parameters and 29 output parameters was used; the amount of data in the set is 936 data lines. Neural network training occurred in the proportion of 10% and 90%, respectively. Results of this study research can be used to further improve the knowledge and skills necessary for successful professional realization.

This study presents a multi-stage approach for building object models (BOMs) on a construction site, aimed at creating an “evolutionary” digital twin. The integration of building information modeling (BIM) and artificial intelligence is used to achieve this goal, with the use of photo modeling using moving cameras and the potential integration of IoT technologies also discussed. A comprehensive artificial intelligence system, combining Convolutional Neural Network (CNN) and Feed Forward Neural Network (FFNN) architectures, has been developed to detect, categorize, and evaluate BIM projects throughout their life cycle. The scalability prospects for point cloud and mesh models, as well as the use of big data technology to optimize the representation of the digital twin, are also addressed. The study determines the effectiveness of construction site conformance detection during the construction of a BIM model, providing consistency and a quantitative evaluation of the processes taking place on the construction site. The findings of this research can be used to enhance BIM modeling methods and concepts toward a multi-stage representation of the digital twin of the construction site.

This study presents a fuzzy production model (FPM) for risk assessment of the construction enterprise management system. The task of the research is to explore the feasibility of risk analysis model based on fuzzy logic, which allows it to be adapted to existing risk management models and modified taking into account the real conditions for the implementation of an investment and construction project. The authors suggest FPM with a set of input parameters that characterize risk factors, a set of output parameters that characterize the risks indicators and base of fuzzy production rules. The result is presented by the process of implementing fuzzy modeling of the rule base using the developed ModelingFuzzyLogic system based on the Mamdani algorithm and allows getting a linguistic description of the degree of risk and its numerical value. The results of the study are recommended for use in the management system of a construction enterprise to identify potential risks in the implementation of an investment and construction project.

Accurate estimation of long-term average (LTA) discharge is fundamental for water resource assessment, infrastructure planning, and hydrological modeling, yet it remains a significant challenge, particularly in data-scarce or ungauged basins. This study introduces an advanced machine learning framework to estimate long-term average discharge using globally available hydrological station metadata from the Global Runoff Data Centre (GRDC). The methodology involved comprehensive data preprocessing, extensive feature engineering, log-transformation of the target variable, and the development of multiple predictive models, including a custom deep neural network with specialized pathways and gradient boosting machines (XGBoost, LightGBM, CatBoost). Hyperparameters were optimized using Bayesian techniques, and a weighted Meta Ensemble model, which combines predictions from the best individual models, was implemented. Performance was rigorously evaluated using R², RMSE, and MAE on an independent test set. The Meta Ensemble model demonstrated superior performance, achieving a Coefficient of Determination (R²) of 0.954 on the test data, significantly surpassing baseline and individual advanced models. Model interpretability analysis using SHAP (Shapley Additive explanations) confirmed that catchment area and geographical attributes are the most dominant predictors. The resulting model provides a robust, accurate, and scalable data-driven solution for estimating long-term average discharge, enhancing water resource assessment capabilities and offering a powerful tool for large-scale hydrological analysis.

The current stage of the development of the construction industry is its digitalization, which requires the transformation of processes and models based on the use of modern information technologies (IT) for the development of digital platforms and duplicates. Urban planning is a complex process of controlled development of the city, and the problem of digitalization of the city as a system as a whole has not lost its relevance. The research examines the problem of selection, application and integration of modern information technologies for modeling the urban environment and creating digital duplicates of city objects as components of the Smart City system. An overview of the main IT for the application of the methodological platform of information modeling of cities (City Information Modeling) based on Building Information Modeling (BIM), Geographic Information System (GIS) and Internet of Things (IoT) technologies, which make it possible to combine city data of various spatial scales and time dimensions. A conceptual IT scheme for urban space management is proposed based on the integration of three components: the Artificial Intelligence system, Big Data technology, and the CIM methodological platform. The requirements for the selection of information technology for the creation of a digital double of an urban object were studied, which made it possible to propose an integration scheme of IT modeling of the urban environment and the creation of a digital double of the city. Such integration will help to efficiently process large and complex data sets, use information about the past and present to predict the future, implement digital reproduction of a physical object and create a digital urban spatial object, namely a CIM object, which should become a digital basis for intelligent planning, construction, management and operation of a smart city.