Coupling Discriminating Statistical Analysis and Artificial Intelligence for Geotechnical Characterization of the Kampemba’s Municipality Soils (Lubumbashi, DR Congo)

Abstract

This study focuses on the determination of physical and mechanical characteristics based on in vitro tests, by using field samples for the Kampemba urban area in the city of Lubumbashi. At the end of this study, we identified the soils according to their parameters, and established the geotechnical classification by determining their bearing capacity by the group index method using from the identification tests carried out. By using the AASHTO classification method (American Association for State Highway Transportation Official), the results obtained after our studies revealed five classes of soil: A-2, A-4, A-5, A-6, A-7 in a general way, and particularly eight subgroups of soil: A-2-4, A-2-6, A-2-7, A-4, A-5, A-6, A-7-5 and A-7-6 for the concerned area. The latter has given statistical analysis and deep learning based on multi-layer perceptron, the global values of the physical parameters. It’s about: 31.77% ± 1.05% for the limit of liquidity; 18.71% ± 0.76% for the plastic limit; 13.06% ± 0.79% for the plasticity index; 83.00% ± 3.33% for passing of 2 mm sieve; 76.22% ± 3.2% for passing of 400 μm sieve; 89.07% ± 2.99% for passing of 4.75 mm sieve; 70.62% ± 2.39% passing of 80 μm sieve; 1.66 ± 0.61 for the consistency index; −0.67 ± 0.62 for the liquidity index and 8 ± 1 for the group index.