Generalized Approach to Soil Strength Prediction With Machine Learning Methods

摘要

Current methods for evaluating the suitability of potential landing sites for fixed-wing aircraft require a direct measurement of soil bearing capacity. In contingency military operations, the commitment of ground troops to carry out this mission prior to landing poses problems in hostile territory, including logistics, safety, and operational security. Developments in remote sensing technology provide an opportunity to make indirect measurements that may prove useful for inferring basic soil properties. However, methods to accurately predict strength from other fundamental geotechnical parameters are lacking, especially for a broad range of soil types under widely-varying environmental conditions. To support the development of new procedures, a dataset of in situ soil pit test results was gathered from airfield pavement evaluations at forty-six locations worldwide that encompass a broad variety of soil types. Many features associated with soil strength including gradation, moisture content, density, specific gravity and plasticity were collected along with California bearing ratio (CBR), a critical strength index used to determine the traffic loading that the ground can support.