Análise comparativa de modelos para previsão e mapeamento de propriedades geotécnicas dos solos da microrregião de Mossoró-RN

• Ano de defesa: 2016
• Autor(a) principal: Guilherme, Ana Tália Pinto
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Palavras-chave em Português: Transportes; Redes neurais; Estatística; Mapeamento geotécnico
• Link de acesso: http://www.repositorio.ufc.br/handle/riufc/23174

The absence of an adequate tool to assist future road projects in the Mossoró Microregion has motivated the interest of proposing alternatives for prior recognition of the geotechnical characteristics of the soils for paving purposes. This research presents, applies and compares two modeling techniques for the location of soils and their geotechnical characteristics, using as study area the Microregion of Mossoró - RN. It is hoped that the resulting models can aid in the decision-making process in paving works, minimizing the costs and the time of performing geotechnical studies, especially in the pre-design phase. In the development of the models was used Geoprocessing for the composition of the georeferenced database and the Regression Statistics Techniques and Artificial Neural Networks for the modeling. In order to reach the proposed objectives, secondary data such as biophysical variables (Pedology, Geology, Vegetation and Geomorphology), geomorphometric variables (slope elevation, aspect, illumination, curvature plane, curvature profile, flow contribution and direction and Length of drainage) and the Geographic Location (Coordinates) to explain the modeled phenomena. These characteristics were correlated to the estimated geotechnical variables (AASHTO - American Association of State Highway and Transportation Officials and CBR - California Bearing Ratio) through the two modeling techniques. At the end of the study, 76% R² was obtained for statistical model and 91% for the neural model for CBR estimation, and for AASHTO Classification, the models presented correct levels of 42% for the statistical model and 89% for the model neural. Despite the confidence level disparity in the soil class prediction models, the two models indicated that the A-2-4 soil is predominant in the region. The correlation indexes for the CBR estimation were closer, and it was possible to observe that more than 50% of the soils of the studied region present good subbase capacity without stabilization (CBR values greater than 20%). Additionally, the geotechnical characteristics estimated by these models enabled the elaboration of Geotechnical Maps, stratified to predict the values of CBR and AASHTO Classification.