Neural network algorithms optimizing the development of residential neighborhoods

Abstract

Modern urban planning involves the creation of a comfortable living environment. The success of new neighborhoods depends on factors such as size, location, services, and transport accessibility. However, issues such as project cost and feasibility often limit innovative urban development projects. A systematic analysis, including a morphological approach, can reveal the complexities of such projects. To optimize the efficiency of construction, the project documentation contains input parameters for calculations. Calculations of economic efficiency should take into account the phasing of the project and the stages of the life cycle. To evaluate the effectiveness of a neighborhood, fuzzy logic is used to process parameters such as the attractiveness of the neighborhood. This research focuses on creating a model of a residential neighborhood using neural network algorithms to optimize economic efficiency by adjusting the parameters of design, construction and operation, taking into account the specifics of the life cycle of a construction and investment project. The article suggests the use of neural network algorithms to improve the development of residential neighborhoods, presenting the appropriate model and discussing its features. The relevance and possibilities of developing this approach in the context of planning new neighborhoods are highlighted.