Data-Driven Prediction of Cement-Stabilized Soils Tensile Properties

Abstract

The indirect tensile strength of two geomaterials treated with variable cement contents, degrees of compaction and water contents were tested after several curing times. A statistical review through an analysis of variance allows for identifying the significant variables and generating prediction models. The distribution of associated uncertainties was measured. Based on these probabilistic results, numerical models were constructed using Latin Hypercube Sampling as the space filling technique. Predictions from the numerical sampling were in accordance with the experimental results. The numerical results suggest that the net gain in accuracy was not affected by the soil type. In addition, it increases rapidly as a function of the sampling size. The proposed approach is broad. It can help to highlight the physical mechanisms involved in behaviors of multi-component materials.