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.