Conceptualizing sophisticated measurement set-ups as well as testing and evaluation procedures for laboratory experiments on anisotropic rocks requires a basic understanding of the potential specimen ...behavior. The focus of the present work was therefore to investigate the influence of different transversely isotropic parameters and their ratios on the elastic behavior of cylindrical rock samples in uniaxial compression tests. Parameter sets corresponding to soft anisotropic rocks were chosen based on naturally observed ranges for the five elastic transversely isotropic constants. Analytical results for the radial and vertical strain distributions around the sample circumference and a comparison with finite element simulations are presented. Further, the effect of interface friction between samples and loading platens was analyzed within the numerical models. The results suggest that radial strains around cylindrical anisotropic samples are rarely uniform except for specific combinations of parameters and isotropy plane inclinations. The effect of interface friction was found to have a clear influence on the developing elastic stress and strain distributions for samples with inclined isotropy planes. Nevertheless, no significant influence of frictional boundary conditions on the back-calibrated values of the elastic parameters could be identified, suggesting that friction-reducing measures in uniaxial compression tests on transversely isotropic samples with predominantly linear behavior are not required.
Rock glaciers (RG) are landforms that occur in high latitudes or elevations and — in their active state — consist of a mixture of rock debris and ice. Despite serving as a form of groundwater ...storage, they are an indicator for the occurrence of (former) permafrost and therefore carry significance in the research for the ongoing climate change. For these reasons, the past years have shown rising interest in the establishment of RG inventories to investigate the extent of permafrost and quantify water storages. Creating these inventories, however, usually involves manual, laborious, and subjective mapping of the landforms based on aerial image - and digital elevation model analysis. We propose an approach for RG mapping based on supervised machine learning which can help to increase the mapping efficiency and permits rapid RG mapping in vast and not yet covered areas. We found deep convolutional artificial neural networks (ANN) that are specifically designed for image segmentation (U-Net architecture) to be well suited for this classification problem. The general workflow consists of training the ANNs with orthophotos and slope maps of digital elevation models as input. The output (RG label-maps) is derived from a recently published RG inventory of the Austrian Alps that features 5769 individual RGs and was compiled manually by several scientists. To increase the generalization capabilities, we use live data augmentation during training. Based on this inventory, the ANNs have learned the average expert opinion and the RG map generated by the ANN can be used to increase the consistency and completeness of already existing RG inventories. Moreover, this ANN approach might be valuable for other landform mapping tasks beyond rock glaciers (e.g., other mass movements).
•Artificial neural network based rock glacier mapping support.•Tool to aid mapping of vast areas by delineating rock glacier suspected landforms.•U-Net based image segmentation of orthophotos and digital elevation models.•Deriving average expert opinions by learning from whole rock glacier inventories.
The assumption of an isotropic material behaviour is still common practice for tunnel design. Strictly speaking, this assumption is only valid if the influence of directional dependencies on the ...resulting deformations and stresses is marginal. In lithologies that have a high degree of anisotropy, such as shales and phyllites, the orientation characteristics of material properties such as strength and stiffness should be taken into account in order to avoid serious misinterpretations of the bearing capacity and deformation characteristic of the surrounding rock and the tunnel lining. The main focus is to accurately distinguish between the different types and terminology of anisotropy.
The dataset contains 1339 cone penetration tests (CPT, CPTu, SCPT, SCPTu) executed within Austria and Germany by the company Premstaller Geotechnik ZT GmbH. As a first processing step, core ...drillings, located within a maximum distance of approximately 50 m to the insitu tests, were assigned to these cone penetration tests, which allow an interpretation of the insitu measurements based on its grain size distribution. In a second step, the software Geologismiki was used to calculate various normalized measures, which can e.g. be used as input parameters for soil behaviour type charts. The present data can be utilized by researches for example to develop new approaches related to soil classification based on cone penetration test. Furthermore, it provides a framework for combining insitu measurements (qc, fs, Rf, u2, Vs), normalized measures (i.e. Qt, Bq, U2) and soil classifications.
In tunneling, predictions of the rockmass conditions ahead of the face are of great interest to be able to take appropriate countermeasures at the right time. Besides investigations like drilling or ...geophysics, new approaches in mechanized tunneling aim at forecasting the geology ahead via Machine Learning models. These models are trained to forecast tunnel boring machine data by learning from recorded data in already excavated parts of the tunnel. Simply judging from high accuracies, these results may look promising at the first sight, but forecasts like this are mostly just delayed and slightly altered versions of the input data and no predictive value can result from them. This paper shows deficits in the current practice of data driven forecasts ahead of the tunnel face and raises impetus for further research in this particular field and TBM data analysis in general.
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•Forecasts of TBM operational data based on time delayed prediction is pointless.•One step ahead predictions cannot contain valuable information for TBM tunneling.•Heterogeneous TBM data point spacing poses a challenge for machine learning.•Special accuracy measures and close up plots are needed for forecast evaluation.
•MSAC: a new data driven classification system for TBM tunneling is developed.•The goal of MSAC is to work as an unbiased aid for construction site personnel.•MSAC utilizes statistics and techniques ...of unsupervised machine learning.•MSAC shows promising results in comparison to other classification systems.
Rockmass classification systems are an integral part of today’s geotechnical design process. Many of these classification systems are however based on subjective or semiquantitative assessments which leads to a call for more objective classification systems.
One way to achieve this goal in mechanized (TBM) tunneling is to use the TBM operational data – or computed parameters thereof - as a basis to decide whether or not “regular advance” is at hand. We support this data driven approach by presenting MSAC (Multivariate sequence Segmentation, Abstraction and Classification) which is a computational framework that ultimately tells how “regular” the present TBM data is. MSAC utilizes several techniques of unsupervised machine learning and consists of multiple steps: 1. Computation of parameters like the specific penetration; 2. Preprocessing of the data (e.g. filtering, noise reduction); 3. Segmentation of the multivariate sequence into subsequences; 4. Abstraction/feature engineering from the subsequences; 5. Classification of the sequences by applying a statistical threshold on the Mahalanobis distances of each abstracted feature.
We compare MSAC to another TBM data driven rockmass classification system which is based on computation of the torque ratio and discriminates “hindered” from “regular” advance via a fixed threshold. The comparison shows that MSAC yields more comprehensible results, is less afflicted by construction site specific specialties and is less sensitive towards outliers, sensory malfunctions or data noise.
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