Abstract

1. Abstract Wave propagation and diffusive transport phenomena could work as evidence of the mechanical discontinuities in material. For the problem of poor efficiency of the existing fracture simulation methods, this paper proposes crack-bearing material characterization approach by processing wave travel-time using seven data-driven classification techniques. To that end, we perform classification models to predict discontinuities orientation, dispersion, and spatial distribution prediction by learning from the different-waves simulation model. The travel-time measured by multiple sensors placed around the material perform as our input data of machine learning method. As a result, this work found that machine learning models exhibit best classification performance on classifying crack dominant orientations. Combination of compressional wave and shear wave are enough to capture the crack information in the material, however, the pressure diffusion also able to optimize our algorithms. Voting classifier and gradient boosting classifier perform the best for purposes of characterization. When compare the performance of different mechanical discontinuities, embedded closed discontinuities shows high accuracy than open discontinuities on the classification models.