Oleg Gavrysh

The construction industry is a crucially important element of the Ukrainian economy, since its development and performance affect other industries. The economic recession consequences and the unforeseen recent events, caused by different types of risks, have adversely affected the construction industry development and necessitated the search for modern methods of risk management. The study is based on a sample of five projects from five construction industry enterprises and covered the period of 2010–2018. A set of project risks, investigated by the group of experts, was analyzed based on fuzzy set theory, and included seven phases of the fuzzy set model construction to assess project risks of construction industry enterprises. Based on the identified elements of a fuzzy set model and a set of significant project risks, a value classifier of significant project risks for construction industry enterprises was developed. This allowed to estimate the current values of project risk indicators and to identify them by levels of their fuzzy subset membership. Besides, a classifier for the quantitative assessment of the total project risks level for investment projects was developed, which allowed estimating the value of the aggregate indicator. In order to improve the existed methodology, the study suggested introducing probabilistic values for the risk of project failure depending on the significance of the overall project risks. Accordingly, the paper identifies the probability of significant project risks simultaneous occurring during the project implementation. However, the higher the likelihood of risk, the higher the probability of investment project failure.

The study aims to improve methodical approach for formalizing the sustainable development models for progressive countries by suggesting the relevant representative indicators. The study is performed using the statistical approach to determine the suitability of data for further modeling using indicators of variation, taking into account the normality of the population distribution as the main criteria of the data set quality. The study highlights the results of processing measurable quantitative economic, social, and environmental indicators of different countries that may be used for identifying possible changes in the world’s sustainable development. The authors select the indicators for scenario modeling of the sustainable development of Brazil, India, China, Republic of Korea, and the USA, as well as suggest a set of relevant affecting factors. To confirm the meaningful impact of different factors, such as biological balance, conflicts intensity, corruption perception and other, a neural network is developed, and its preliminary training on the test data is conducted. The obtained results can be used to predict economic changes in the world under the influence of specific economic, social, and environmental factors.