Investigation into the influence of excavation of twin-bored tunnels within weak rock masses adjacent to slopes

The focus of this investigation is twin tunnelling projects designed and constructed adjacent to slopes. To this end, several state-of-the-art two-dimensional (2-D) and three-dimensional (3-D) numerical models for multiple scenarios were utilized. The novelty of this research (further highlighted by two case studies) explicitly demonstrates that for 2-D and 3-D elastic and plastic analyses for weak rock masses, the interaction between the two tunnel branches for various conditions show how the pillar width minimally influences such interaction past two diameter widths of separation. As well, the 3-D sequential longitudinal tunnel excavation of the twin tunnel does not significantly influence the crown displacements of the two tunnels prior to construction of the mid-span of the twin tunnel. Slope stability analysis was conducted by employing the novel shear strength reduction (SSR) method. These findings confirm that changes in the location of the twin tunnels with respect to the slope and one another in combination with the natural angle of the slope can improve the overall stability without any other special measures taken. As well, by having the twin tunnel pillar width constant, the vertical translation of the tunnels (i.e., increase in the overburden) is more beneficial than that of a horizontal translation, resulting in a higher factor of safety.

Using Google Earth and Google Street View To Rate Rock Slope Hazards

Rockfall hazard from cut slopes along highways are caused primarily by unfavorable orientations of discontinuities, presence of unconsolidated cobble/boulder deposits, undercutting of strong rocks by weaker rocks, or degradation of weak rock masses. The rockfall hazard rating system (RHRS) was introduced in Oregon to evaluate the hazard and associated risk to an adjacent transportation facility for a cut slope's potential for releasing rockfalls. RHRS is a numerical score–based rating of parameters that characterize rockfalls. The parameters include slope geometry (height, angle, roughness, orientation), geologic information (discontinuity characterization, undercutting susceptibility), driver's line of sight, and climate. Geologic information, such as discontinuity orientation data, is traditionally collected using a transit compass and measuring tape at the site. The method is time consuming and expensive and can be dangerous. This study tests the use of Google Earth and Google Street View tools to remotely collect data for selected parameters that characterize rockfall hazard. The selected parameters are categorized under slope profile, geologic characteristics, and impact factor parameters, which are quantitatively and qualitatively measurable using Google Street View and Google Earth. A section of U.S. 33 with a high density of road cuts and two more sites along Interstate 64, all located in Virginia, were selected for the study. Sites were evaluated by using a combination of measurement tools available in Google Earth and a visual inspection of the rock units in Google Street View. The results of seven of the sites were re-evaluated using field-derived data.

Predicting Tunnel Squeezing Using Multiclass Support Vector Machines

Tunnel squeezing is one of the major geological disasters that often occur during the construction of tunnels in weak rock masses subjected to high in situ stresses. It could cause shield jamming, budget overruns, and construction delays and could even lead to tunnel instability and casualties. Therefore, accurate prediction or identification of tunnel squeezing is extremely important in the design and construction of tunnels. This study presents a modified application of a multiclass support vector machine (SVM) to predict tunnel squeezing based on four parameters, that is, diameter (D), buried depth (H), support stiffness (K), and rock tunneling quality index (Q). We compiled a database from the literature, including 117 case histories obtained from different countries such as India, Nepal, and Bhutan, to train the multiclass SVM model. The proposed model was validated using 8-fold cross validation, and the average error percentage was approximately 11.87%. Compared with existing approaches, the proposed multiclass SVM model yields a better performance in predictive accuracy. More importantly, one could estimate the severity of potential squeezing problems based on the predicted squeezing categories/classes.

Comparison of the Results of Analytical and Numerical Models of Pre-Reinforcement in Shallow Tunnels

The steel pipe umbrella is a widely used technology when tunnelling in weak soils in order to create pre-support ahead of the tunnel face. The design of steel pipes is frequently done through simplified analytical approaches which are easy to apply but require proper assessment of the loads acting on the pipe. To provide information on this key design aspect, the results of the comparison between a three-dimensional numerical model developed with the code FLAC 3D and an analytical model based on the approach of a beam on yielding supports is presented and discussed. The comparison refers to a shallow tunnel with an overburden of three times its diameter for two different types of weak rock masses. The obtained results provide suggestions about the load that has to be applied in the analytical model for the design phase.