TY - JOUR

T1 - Neural network algorithms optimizing the development of residential neighborhoods

AU - Prokhorov, Vitalii

AU - Salnikov, Viktor

AU - Pridvizhkin, Stanislav

PY - 2024/1/1

Y1 - 2024/1/1

N2 - 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. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).

AB - 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. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85183304636

U2 - 10.1051/e3sconf/202447402037

DO - 10.1051/e3sconf/202447402037

M3 - Conference article

VL - 474

JO - E3S Web of Conferences

JF - E3S Web of Conferences

SN - 2261-236X

M1 - 02037

T2 - 10th International Annual Conference on Industrial Technologies and Engineering, ICITE 2023

Y2 - 9 November 2023

ER -