Tensile Strength of Artificially Cemented Sandstone Generated via Microbially Induced Carbonate Precipitation

Artificially bio-cemented sands treated with microbially induced calcite precipitation are weakly cemented rocks representing intermediate materials between locked and carbonate sands. Variations in cementation significantly affect the strength of sample, particularly tensile stregth. The modes of fracture and the surface characteristics resulting from the indirect tensile strength tests (Brazilian tests) are strongly correlated with the specimen strength and consequently the degree of cementation. This study examines the tensile strength of bio-cemented fine and coarse sands (average particle diameter 0.18 and 1.82 mm, respectively) and investigates failure modes by recording fracture evolution at both sides of specimen and surface characteristics of the reconstructed surfaces. The dimensionless slope parameter Z2 provided the best fit with respect to tensile strength while the power spectral density was a good indicator of surface anisotropy. Finally, wavelet decomposition allowed for comparison of fracture surface characteristics of the two sands ignoring the grain size effects.

Rough or Noisy? Metrics for Noise Estimation in SfM Reconstructions

Structure from Motion (SfM) can produce highly detailed 3D reconstructions, but distinguishing real surface roughness from reconstruction noise and geometric inaccuracies has always been a difficult problem to solve. Existing SfM commercial solutions achieve noise removal by a combination of aggressive global smoothing and the reconstructed texture for smaller details, which is a subpar solution when the results are used for surface inspection. Other noise estimation and removal algorithms do not take advantage of all the additional data connected with SfM. We propose a number of geometrical and statistical metrics for noise assessment, based on both the reconstructed object and the capturing camera setup. We test the correlation of each of the metrics to the presence of noise on reconstructed surfaces and demonstrate that classical supervised learning methods, trained with these metrics can be used to distinguish between noise and roughness with an accuracy above 85%, with an additional 5–6% performance coming from the capturing setup metrics. Our proposed solution can easily be integrated into existing SfM workflows as it does not require more image data or additional sensors. Finally, as part of the testing we create an image dataset for SfM from a number of objects with varying shapes and sizes, which are available online together with ground truth annotations.

Correlating fracture toughness and fracture surface roughness via correlation length scale

Fracture toughness of a material depends on its microstructure and the imposed loading conditions. Intuitively, the resultant fracture surfaces must contain the information about the interlacing of these intrinsic (microstructure) and extrinsic (imposed loading) characteristics. Mandelbrot’s revelation that fracture surfaces are fractals, excited both the scientific and engineering communities, spurring a series of works focused at correlating the fracture toughness and the fracture surface roughness. Unfortunately, these studies remained inconclusive and later on it was shown that the fractal dimension of the fracture surface roughness is in fact universal. Here, we show that by going beyond the universality, a definite correlation between the fracture toughness and indices of the fracture surface roughness is obtained. To this end, fracture experiments on an aluminum alloy were carried over a wide range of loading rates (10-2 – 106 ), and the resulting fracture surface were reconstructed using stereography. The correlation lengths, extracted from the reconstructed surfaces, were found to be linearly correlated with the measured fracture toughness. The correlation unraveled in our work, along with the proposed mechanistic interpretation, revives the hope of correlating fracture toughness and fracture surface roughness, allowing quantitative failure analysis and a potential reconstructive approaches to designing fracture resistant materials.