Metodologia de baixo custo para mapeamento geotécnico aplicado à pavimentação

Abstract

This paper presents a low-cost methodology for forecasting and mapping of CBR values (California Bearing Ratio) of soils in the energies normal compression (CBR-N) and inter-mediate (CBR-I), which contribute to the decision-making process as to their use for paving purposes. GIS, Artificial Neural Networks (ANN) and modeling techniques as well as biophysical and spatial variables were used as explanatory of the modeled phenom-enon. The researched characteris%cs (pedology, geology, geomorphology, vegeta%on, hypsometry and position) correlated with CBR values of soils in both energy compaction. CBR data were extracted from pre-existing projects and studies in the study area, in this case, the metropolitan area of Fortaleza (MAF). Thus, they were calibrated, validated and tested in many different ANN to find the two models best fit, for the generation of CBR-N estimates and CBR-I, of the soil MAF from the studied biophysical variables. The geotechnical characteristics estimated by these models enabled the development of two Neural Geotechnical Maps stratified to predict the values, CBR-N and CBR-I. The results show that ANN technique is promising to predict the mechanical properties of soils and can assist in making decisions regarding the use of these in road projects.