In modern TBM tunnelling, vast amounts of data, like penetration, torque, thrust force and gripper force, are produced. Additionally, geophysical investigations and drilling programs are conducted to explore the geology beyond the tunnel face. Although this abundance of data offers many possibilities to analyze the encountered geology, utilizing it to its full extent is a difficult task for construction site personal and researchers as well.

Machine learning is a field of computer science, which applies algorithms to recognize patterns and structure in data. Recent breakthroughs of this discipline are on the one hand based on today's powerful computational capabilities, on the other hand on the massive amount of data that is available. Supervised learning, unsupervised learning and reinforcement learning are the three subdisciplines that concern classification and outcome prediction, finding hidden data structures or learning a series of action from the data.

The research goal of this project is to explore the applicability of various machine learning techniques on the analysis of advance investigations from recent major tunnelling projects. Strong focus on the practical use of algorithms is seen as key to optimize data interpretation workflows and efficiency of predictions. Ideally the findings can be used for correlating recorded data to observed geology, for enhancing the quality of predictions ahead of the tunnel face and to transfer learned knowledge from existing to future tunnel excavations.

Predicting the behaviour of rock mass is very important in rock engineering. The Institute of Rock Mechanics and Tunnelling (TUG), the Veitsch Radex GmbH & Co OG, the Institute of Applied Geosciences (TUG) and the Department of Geoinformatics - Z_GIS (University of Salzburg), work together in a project called "Devastating micro cracks: researching spontaneous rock failure with rock mechanical testing, µCT, OBIA and geostatistics (ROCKBURST)”.

This 3-year FFG-project presents an interdisciplinary approach to research the influence of micromechanical and microstructural characteristics and their individual effect on rock failure. State-of-the-art rock mechanical testing, data acquisition and simulation methods will be nested in an innovative work-flow: Following initial mineralogical investigations, artificial and natural rock samples will each be subject to destruction-free microstructure analysis and compression testing. From each cycle, micro computed tomography will provide 3D data on the sample's micro-fabric before a successive step of uniaxial testing controlled by acoustic emission testing is carried out. From the resulting series of µCT data, object-based image analysis will assemble a process model of microcrack evolution. Geostatistical simulations will be used to upscale the results from sample to excavation scale. New insights into relevant micromechanical processes in highly-stressed brittle rock and their quantitative comprehension are expected.

The Große Bischofsmütze is one of the famous peaks in the Salzburg mountain region. However, this peak slowly crumbles. Huge parts of the peak detach during major rock fall events. The mass movements can reach up to 50,000 m³. The latest two events happened in the 1990s and in 2007 at the southern and eastern mountainside.

These two events led to an intense monitoring programme and new discontinuities close to the peak indicate a new rock fall event. For this reason, the stability of the Große Bischofsmütze shall be investigated. Following questions shall be answered:

• Can a new rock fall at the Große Bischofsmütze be expected?
• What would be the volume of this new rock fall event?
• What are the sensitive parameters for the stability of the endangered area?
• What risk proposes a novel land slide to the adjacent infrastructure?

This thesis is part of a study to determine the volumetric block-size distribution and rock mass characteristics with the information of only one outcrop by using remote sensing. Tools for the elaboration of this thesis are statistical analysis, numerical simulations and close-range terrestrial digital photogrammetry.

Discontinuities dominantly influence the mechanical behaviour of rock masses. Thus, it is of crucial importance in rock mechanics to have a profound knowledge about the discontinuity network. Traditional measuring techniques provide a rough knowledge about a discontinuity network but are also limited within reachability, geological/geotechnical knowledge, time and scale. The results are subjective and not reproductive.

The application of remote sensing techniques like photogrammetry and laser-scanning, in geotechnics helped to collect data about the discontinuity network and reduce the bias, which is introduced by manual mapping. Together with numerical models, reliable predictions about the stability of the rock face and the discontinuous rock are nowadays possible. But there is still missing an automatic data collection, as well as an automatic implementation of the collected data in a numerical rock model.

In his research, Andreas Buyer focusses on the automatic mapping of discontinuities aided by remote sensing techniques using ShapeMetriX^3D (3GSM GmbH). This means: