Stress Inversion Using LiDAR Tunnel Deformation Measurements

Far-field stresses are those present in a volume of rock prior to excavations being created. Estimates of the orientation and magnitude of far-field stresses, often used in mine design, are generally obtained by single-point measurements of stress, or large-scale, regional trends. Point measurements can be a poor representation of far-field stresses as a result of excavation-induced stresses and geological structures. For these reasons, far-field stress estimates can be associated with high levels of uncertainty. The purpose of this thesis is to investigate the practical feasibility, applications, and limitations of calibrating far-field stress estimates through tunnel deformation measurements captured using LiDAR imaging. A method that estimates the orientation and magnitude of excavation-induced principal stress changes through back-analysis of deformation measurements from LiDAR imaged tunnels was developed and tested using synthetic data. If excavation-induced stress change orientations and magnitudes can be accurately estimated, they can be used in the calibration of far-field stress input to numerical models. LiDAR point clouds have been proven to have a number of underground applications, thus it is desired to explore their use in numerical model calibration. The back-analysis method is founded on the superposition of stresses and requires a two-dimensional numerical model of the deforming tunnel. Principal stress changes of known orientation and magnitude are applied to the model to create calibration curves. Estimation can then be performed by minimizing squared differences between the measured tunnel and sets of calibration curve deformations. In addition to the back-analysis estimation method, a procedure consisting of previously existing techniques to measure tunnel deformation using LiDAR imaging was documented. Under ideal conditions, the back-analysis method estimated principal stress change orientations within ±5° and magnitudes within ±2 MPa. Results were comparable for four different tunnel profile shapes. Preliminary testing using plastic deformation, a rough tunnel profile, and profile occlusions suggests that the method can work under more realistic conditions. The results from this thesis set the groundwork for the continued development of a new, inexpensive, and efficient far-field stress estimate calibration method.

Molecular Origins of Higher Harmonics in Large-Amplitude Oscillatory Shear Flow: Shear Stress Response

Recent work has focused on deepening our understanding of the molecular origins of the higher harmonics that arise in the shear stress response of polymeric liquids in large-amplitude oscillatory shear flow. For instance, these higher harmonics have been explained by just considering the orientation distribution of rigid dumbbells suspended in a Newtonian solvent. These dumbbells, when in dilute suspension, form the simplest relevant molecular model of polymer viscoelasticity, and this model specifically neglects interactions between the polymer molecules [R.B. Bird et al., J Chem Phys, 140, 074904 (2014)]. In this paper, we explore these interactions by examining the Curtiss-Bird model, a kinetic molecular theory designed specifically to account for the restricted motions that arise when polymer chains are concentrated, thus interacting and specifically, entangled. We begin our comparison using a heretofore ignored explicit analytical solution [Fan and Bird, JNNFM, 15, 341 (1984)]. For concentrated systems, the chain motion transverse to the chain axis is more restricted than along the axis. This anisotropy is described by the link tension coefficient, ε, for which several special cases arise: ε = 0 corresponds to reptation, ε > 1/8 to rod-climbing, 1/2 ≥ ε ≥ 3/4 to reasonable predictions for shear-thinning in steady simple shear flow, and ε = 1 to the dilute solution without hydrodynamic interaction. In this paper, we examine the shapes of the shear stress versus shear rate loops for the special cases ε = (0,1/8, 3/8,1) , and we compare these with those of rigid dumbbell and reptation model predictions.