NASCIMENTO, Manoel Henrique Reis2024-08-212024-08-212023-06-12FROTA, Arlindo Rubens De Oliveira. Estimativa do custo do metro quadrado habitacional popular na cidade de Manaus baseado nos principais insumos, usando redes neurais artificiais. 2023. 82p. Dissertação (de mestrado em Engenharia, Gestão de Processos, Sistemas e Ambiental) - Instituto de Tecnologia e Educação Galileo da Amazônia (ITEGAM), Manaus, 2023.https://rigalileo.itegam.org.br/handle/123456789/92Civil construction is one of the most expressive sectors in the economy, development and employability in the national territory. It is considered one of the branches that demonstrates the expansion and wealth of a country, where social housing is directly linked to socioeconomic development. According to the IBGE, in 2020, Manaus has 653,618 homes, of which 348,618 are classified as subnormal agglomerations, that is, stilt houses and unhealthy occupations and/or difficult to access. It can be said that one of the major obstacles to the construction of low-income housing is the lack of predictability of the behavior of costs during the execution of the work. This factor is even more pronounced in subdivisions and housing complexes, that is, in mass production, due to quantity. In order to mitigate these challenges, a tool was implemented, based on the concepts of ANN - Artificial Neural Network, which compiles 2 civil construction cost tables and predicts the cost of popular housing based on the value of the main inputs. This network seeks to estimate the cost per square meter of construction of popular housing in the city of Manaus. MATLAB® software was used, where data from the CUB and INCC tables were compiled. The inputs used were those contained in the so-called “basic batch”, recommended by the CUB. Quickly and practically without cost, the developed tool can predict the cost of the square meter of popular housing, in the city of Manaus, from the stipulation of the inputs. ANN was able to present a very strong correlation in the sources of its sample space, thus demonstrating that the tables, despite presenting different data collection and treatment, in addition to being elaborated by different institutes, present compatibility in their databases, which is reflected in the veracity and reliability of the data collected and processed. After estimating several statistical indices, it is clearly noted that this is a tool that has proven to be efficient and safe for estimating future costs.pdf.Rede NeuralHabitação popularInsumosCustoDissertação3.01.00.00-3 Engenharia Civil