Engineering Geology and Tunnels

Currently, knowledge and understanding of the role of geological material and its implication in tunnel design is reinforced with advances in site investigation methods, the development of geotechnical classification systems and the consequent quantification of rock masses. However, the contribution of engineering geological information in tunnelling cannot be simply presented solely by a rock mass classification value. What is presented in this chapter is that the first step is not to start performing numerous calculations but to define the potential failure mechanisms. After defining the failure mechanism that is most critical, selection of the suitable design parameters is undertaken. This is then followed by the analysis and performance of the temporary support system based on a more realistic model. The specific failure mechanism is controlled and contained by the support system. A tunnel engineer must early assess all the critical engineering geological characteristics of the rock mass and the relevant mode of failure, for the specific factors of influence, and then decide either he or she will rely on a rock mass classification value to characterise all the site-specific conditions. Experiences from the tunnel behaviour of rock masses in different geological environments in Alpine mountain ridges are presented in this chapter

Sustainable treatment method of a high concentrated NH3 wastewater by using natural zeolite in closed-loop fixed bed systems

The aim of this study is to investigate ammonium removal from a wastewater resulted after homogenization and anaerobic digestion of a mixture of wastes and wastewater from animal processing units and sewage sludge, by using natural zeolite clinoptilolite. Batches as well as closed loop fixed bed system (CLFB) are studied, offering an alternative to conventional fixed bed systems. The experimental results showed that the optimum pH is in the vicinity of 6.48, where the achieved removal in the batch system reached 46%. The CLFB system, under the same experimental conditions and relative flow rate of 2.56 BV h−1, reached a removal of 55%, which is almost 22% higher. In the CLFB the removal of ammonia could be further increased by diluting the initial solution by 1/8, reaching the level of 96%. The achieved zeolite loading, for all studied systems, is between 2.62 and 13 mg g−1. This kind of operation is very useful for relatively high concentration and small volumes of wastewater and in systems that there is no need for continuous flow operation

A comparative study on phyllosilicate and tectosilicate mineral structural properties

Natural minerals are widely used in numerous environmental applications, mainly as sorbents in ion exchange and sorption processes. Minerals, such as zeolites and clays, can be found all over the world, but they are mined containing a variety of different impurities; this prevents their accurate characterization. The present study examines various methods used for the characterization of three common natural silicate minerals, one zeolite (clinoptilolite) and two clays (montmorillonite and vermiculite). Their characterization was performed through a series of analytical measurements so as to gather all the information needed regarding their structural properties. Therefore, “similar” minerals such as clinoptilolite vs. heulandite and vermiculite vs. hydrobiotite can be distinguished; revealing important properties when comes to their practical application. The methods used in the present study are X-ray powder diffraction (XRD), X-ray fluorescence, Fourier transform infrared (FTIR) spectroscopy, TG/DTG/DTA and N2-porosimetry (BET). An extensive literature review of the natural silicate minerals has been conducted and the relevant results and methods are comparatively reported. The analytical results enabled the distinguish of the examined minerals. XRD, FTIR, TG/DTG/DTA showed that all three minerals have characteristic bands that can be used to easily distinguish from others

Landslide Hazard and Risk Assessment for a Natural Gas Pipeline Project: The Case of the Trans Adriatic Pipeline, Albania Section

The paper focuses on the assessment of landslide hazard and risk along or across the Trans Adriatic Pipeline (TAP) natural gas pipeline project in Albania. TAP is a natural gas pipeline that will transport gas from the Caspian Sea to Europe, crossing Northern Greece and Southern Albania. It has long been recognised that landsliding is a major factor for TAP’s pipeline route selection in mountainous regions, especially the challenging area of central Albania. Experience from similar major pipelines has shown that hazard avoidance is generally the most cost- and time-efficient strategy to minimise the landslide risk since geohazard-related decision-making is usually risk-based. For landslides, the risk profile is expected to be dominated by the upslope expansion of existing landslides, resulting in a loss of ridge crest (where the Right of Way (RoW) is usually located), possibly leading to pipeline rupture. However, it is still possible that new landslides could develop under static and/or seismic conditions, especially on steep ridge flanks along the route. An expert determination approach was adopted to define a consensus for the estimate of the risk (i.e., chance of rupture) for the pipeline at eighty-two (82) identified landslide sites in Albania, to identify “hot spots” along the route, where risk-reduction measures could be prioritised. Ten landslides were characterised as “High Risk”, fifteen as “Medium Risk” and nineteen as “Low Risk”. Following this risk assessment, two large re-routings, as well as several local re-routings, were considered. Further investigation was required to identify the site-specific geotechnical conditions and probable remedial measures in cases where landslides could not be avoided by rerouting

Evaluation of Machine Learning Algorithms for Object-Based Mapping of Landslide Zones Using UAV Data

Landslides are a critical geological phenomenon with devastating and catastrophic consequences. With the recent advancements in the geoinformation domain, landslide documentation and inventorization can be achieved with automated workflows using aerial platforms such as unmanned aerial vehicles (UAVs). As a result, ultra-high-resolution datasets are available for analysis at low operational costs. In this study, different segmentation and classification approaches were utilized for object-based landslide mapping. An integrated object-based image analysis (OBIA) workflow is presented incorporating orthophotomosaics and digital surface models (DSMs) with expert-based and machine learning (ML) algorithms. For segmentation, trial and error tests and the Estimation of Scale Parameter 2 (ESP 2) tool were implemented for the evaluation of different scale parameters. For classification, machine learning algorithms (K- Nearest Neighbor, Decision Tree, and Random Forest) were assessed with the inclusion of spectral, spatial, and contextual characteristics. For the ML classification of landslide zones, 60% of the reference segments have been used for training and 40% for validation of the models. The quality metrics of Precision, Recall, and F1 were implemented to evaluate the models’ performance under the different segmentation configurations. Results highlight higher performances for landslide mapping when DSM information was integrated. Hence, the configuration of spectral and DSM layers with the RF classifier resulted in the highest classification agreement with an F1 value of 0.85

Evaluation of Machine Learning Algorithms for Object-Based Mapping of Landslide Zones Using UAV Data

Landslides are a critical geological phenomenon with devastating and catastrophic consequences. With the recent advancements in the geoinformation domain, landslide documentation and inventorization can be achieved with automated workflows using aerial platforms such as unmanned aerial vehicles (UAVs). As a result, ultra-high-resolution datasets are available for analysis at low operational costs. In this study, different segmentation and classification approaches were utilized for object-based landslide mapping. An integrated object-based image analysis (OBIA) workflow is presented incorporating orthophotomosaics and digital surface models (DSMs) with expert-based and machine learning (ML) algorithms. For segmentation, trial and error tests and the Estimation of Scale Parameter 2 (ESP 2) tool were implemented for the evaluation of different scale parameters. For classification, machine learning algorithms (K-Nearest Neighbor, Decision Tree, and Random Forest) were assessed with the inclusion of spectral, spatial, and contextual characteristics. For the ML classification of landslide zones, 60% of the reference segments have been used for training and 40% for validation of the models. The quality metrics of Precision, Recall, and F1 were implemented to evaluate the models’ performance under the different segmentation configurations. Results highlight higher performances for landslide mapping when DSM information was integrated. Hence, the configuration of spectral and DSM layers with the RF classifier resulted in the highest classification agreement with an F1 value of 0.85