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In the present study, the precipitation data measured at 23 rain gauge stations over the Achaia County, Greece, were used to estimate the spatial distribution of the mean annual precipitation values over a specific catchment area. The objective of this work was achieved by programming an Artificial Neural Network (ANN) that uses the feed-forward back-propagation algorithm as an alternative interpolating technique. A Geographic Information System (GIS) was utilized to process the data derived by the ANN and to create a continuous surface that represented the spatial mean annual precipitation distribution. The ANN introduced an optimization procedure that was implemented during training, adjusting the hidden number of neurons and the convergence of the ANN in order to select the best network architecture. The performance of the ANN was evaluated using three standard statistical evaluation criteria applied to the study area and showed good performance. The outcomes were also compared with the results obtained from a previous study in the area of research which used a linear regression analysis for the estimation of the mean annual precipitation values giving more accurate results. The information and knowledge gained from the present study could improve the accuracy of analysis concerning hydrology and hydrogeological models, ground water studies, flood related applications and climate analysis studies.

Prediction is a vague concept that is why we need to conceptualize it specifically for underground deformation time-series data. For this impending issue, this paper employs an advanced deep learning model Bi-LSTM-AM to address it. The results show its applicability for practical engineering. The proposed model is compared with other basic deep learning models including long short-term memory (LSTM), Bi-LSTM, gated recurrent units (GRU), and temporal convolutional networks (TCN). These models cover the most common three forms of deep learning for time-series prediction: recurrent neural networks (RNN) and convolutional neural networks (CNN). This research is supposed to benefit the underground deformation time-series prediction.

The planned radioactive waste repository in Switzerland will be located within a ca. 900 m deep and 100 m thick layer of Opalinus Clay. Low- and intermediate-level waste will be emplaced in caverns with a diameter of 14 m. The great depth of cover and the rather low strength of Opalinus Clay (uniaxial compressive strength in the order of 3-5 MPa) are expected to result in squeezing conditions, which will be demanding to handle on account of the large dimensions of the cavern cross-sections. The paper investigates alterative concepts for the tunnel support, considering the uncertainties and peculiarities of the Opalinus Clay’s response to tunnelling – inter alia its very pronounced time-dependency. The development of relevant deformations will not cease even after several decades, while in the presence of a stiff lining the rock pressure would reach very high values within the service life of the caverns (in the order of about 3-4 MPa within a period of 20-30 years after excavation)., Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World, ISBN:978-1-003-34803-0

On account of their low permeability, clays respond to tunnel excavation with a considerable delay. This is favourable for the interplay between ground, tunnel support and tunnelling equipment and, inter alia, also for the stability of the tunnel face. Nevertheless, even when tunnelling through practically impermeable clay deposits, the face may fail under certain conditions. The latter represents the subject of the present paper. Specifically, based upon a simple but accurate face stability model and a well-known empirical relationship, this paper provides generic answers to two important questions: which geotechnical conditions would result in an unstable face, thus necessitating, e.g., closed-mode TBM operation? and which conditions would be prohibitive in this respect, i.e. the required face support pressure would be beyond today’s technical feasibility limits, so that soil improvement measures would become indispensable?

The National Cooperative for the Disposal of Radioactive Waste (Nagra) in Switzerland has designated Opalinus clay as a host rock for a deep geological repository. The drifts for high-level waste storage (∅ 3.5 m) are to be constructed with a shielded tunnel boring machine (TBM) at depths where squeezing conditions may occur and the ground is fully saturated. This paper studies the TBM shield jamming hazard during excavation or long TBM standstills by means of 3D, transient, coupled hydraulic-mechanical Finite Element computations. An anisotropic linearly elastic and perfectly plastic constitutive model with a Mohr Coulomb yield condition is adopted for the Opalinus clay. Of practical interest is the influence of anisotropy and ground desaturation on the time-dependent ground deformations after excavation. The study concludes that shield jamming is not critical. The results offer practical guidance for the identification of critical design aspects for underground systems in pronouncedly anisotropic ground., Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World, ISBN:978-1-003-34803-0

The energy transition process in Switzerland foresees a move away from nuclear energy. This calls for an expansion of alternative energy sources such as solar or wind. Since these types of energy generation depend on the weather, the energy generated must be stored to be available on demand. In addition to pumped storage plants, compressed air energy storage (CAES) in underground cavities offers a potential solution for this problem. The economic viability of a CAES system depends on the investment costs as well as the overall efficiency of the system, i.e., storage and recovery of the energy. The paper describes the search for a suitable site in Switzerland for an envisioned adiabatic, high pressure (100 bar) CAES with the potential to store 500 MWh of energy. First, the minimum dimensions and possible arrangements of all the cavities required by the CAES facility are determined. Subsequently, relevant hazard scenarios are identified and analysed from the geotechnical viewpoint. A determination is then made of the requirements, such as rock strength, overburden or distance to the valley flanks of the high-pressure underground chamber. In addition, potential damage to the chamber’s sealing is analysed and the plug of the high-pressure chamber is dimensioned structurally. Finally, potential sites are selected with the help of a GIS system incorporating the geological and topographical map of Switzerland., Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World, ISBN:978-1-003-34803-0