The influence of spatial variance on rock strength and mechanism of failure

The heterogeneity in the rock formation affects both rock behavior and the strength. The effect of heterogeneity is observed both at the laboratory scale and at the rockmass level. The mechanical properties of intact rock vary considerably at laboratory scale and often an average value is used for design purposes. Similarly, the values are arbitrarily scaled when used at rockmass level. In underground coal mines, the effect of variability of properties is often observed with the erratic roof failure events that occur throughout the mine. The approach often was to use the deterministic values from limited site data to estimate the rock strength and ignoring the inherent variability of rockmass properties. However, current numerical models have successfully captured the global behavior showing the effect of in-situ stress, geology, operational parameters, etc. This dissertation proposes a probabilistic approach that assumes that rockmass properties as random variables and examines its effect on underground coal mine. The effect of random properties was examined by comparing the deterministic and completely random models which showed the importance of using randomness factor in rocks. Subsequently a spatially correlated random model investigated the influence of rock heterogeneity on rock strength and failure propagation. A random field database with specific spatial correlation was created for each physico-mechanical property using laboratory data and Extreme Value stochastic model in MATLAB. Two scale-measured parameters defined the correlation length, which controls the spatially correlated random data. Then, to verify the importance of the four parameters, friction, cohesion, and correlation length along the horizontal and vertical axes, one hundred and fifty two random sample data are generated. The stress for each specimen is tracked at different loading steps with different spatial correlation factors. This approach determined the effect of material model parameters affect the internal stress distribution for intact rocks. The models were further validated by predicting the behavior of rocks from controlled triaxial tests. Results from the laboratory tests were matched the predicted behavior from numerical models verifying the proposed stochastic method. The stochastic method was then implemented in the three-dimensional numerical model to investigate a longwall mine operating in Pittsburgh seam. The influence of random field data on entry roof in the longwall mining system was investigated. Based on Extreme Value stochastic model, the realistic random field database added two scale-measured parameters from both horizontal and vertical directions to control the spatial correlation length. This model also considered a number of cutting sequences, for identifying the effect of the spatial variance on the roof behavior. Finally, the outcome of the dissertation was to use probabilistic approach for demonstrating the heterogeneous characteristic of rock and the influence of spatial variance on the failure mechanism of both intact rock and rockmass

Structural Material Property Tailoring Using Deep Neural Networks

Advances in robotics, artificial intelligence, and machine learning are ushering in a new age of automation, as machines match or outperform human performance. Machine intelligence can enable businesses to improve performance by reducing errors, improving sensitivity, quality and speed, and in some cases achieving outcomes that go beyond current resource capabilities. Relevant applications include new product architecture design, rapid material characterization, and life-cycle management tied with a digital strategy that will enable efficient development of products from cradle to grave. In addition, there are also challenges to overcome that must be addressed through a major, sustained research effort that is based solidly on both inferential and computational principles applied to design tailoring of functionally optimized structures. Current applications of structural materials in the aerospace industry demand the highest quality control of material microstructure, especially for advanced rotational turbomachinery in aircraft engines in order to have the best tailored material property. In this paper, deep convolutional neural networks were developed to accurately predict processing-structure-property relations from materials microstructures images, surpassing current best practices and modeling efforts. The models automatically learn critical features, without the need for manual specification and/or subjective and expensive image analysis. Further, in combination with generative deep learning models, a framework is proposed to enable rapid material design space exploration and property identification and optimization. The implementation must take account of real-time decision cycles and the trade-offs between speed and accuracy