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.

Discontinuities dominantly influence the mechanical behaviour of rock masses. Thus, it is important in rock mechanics to have a profound knowledge about the discontinuity network. Traditional mapping techniques are limited within time and scale. Furthermore, the results are rather subjective and not reproductive.

The application of remote sensing in geotechnics helps to collect data about the discontinuity network and reduces the bias, intro-duced by manual mapping. Together with numerical models, reliable predictions about the stability of the discontinuous rock are possible. But there is still missing an automatic data collection and analysis process, as well as an automatic implementation of the collected data in a numerical rock simulation.

This research focusses on the automatic mapping of discontinuities aided by remote sensing techniques. This means:

► Discontinuity extraction from coloured images (RGB) and digital surface models

► Numerical simulation of discontinuous rock masses for stability analyses

Fault zone – an expression which has a grave meaning in tunnelling. Neither a geologist nor a geotechnical engineer needs a more detailed description to know which geotechnical situations are connected with this term. Although many fault zones have already been excavated more or less successfully, the gained knowledge is seldom documented or shared. Thus, it is not surprising that major problems occur consistently and evidences for upcoming fault zones are ignored or misinterpreted. Not at least because fault zones are one of the most challenging ground conditions in tunnelling.

What possibilities does the engineer have to predict an upcoming fault zone at site and in time? What measures must be applied to cross the fault zone as favourable as possible (concerning the utilization of the lining and the costs)? What chosen measures by the engineer have more than unfavourable consequences? To answer these questions, former excavations in fault zones shall be investigated to draw geotechnical as well as constructional relevant conclusions, validated by theoretical and scientific considerations. Consecutively, general guidelines for excavations in fault zones shall be elaborated, focussing on their practicability.

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.