THE FINE-GRAINED PLIO-PLEISTOCENE DEPOSITS IN ACHAIA – GREECE AND THEIR DISTINCTION IN CHARACTERISTIC GEOTECHNICAL UNITS

The fine grained Plio-Pleistocene sediments encountered along the Patras Ring Road project (PRR) were distinguished into two lithological units, the Upper Geotechnical and the Lower Geotechnical Unit, based on the detailed engineering geological – geotechnical mapping, at a scale of 1:5000, on fieldwork, as well as on data gained from the boreholes drilled during the design and construction of the project. These units are distinguishable, stratigraphically successive and present basic differences in lithological composition, consistency and permeability and therefore different mechanical behaviour during construction

ROCK SLOPE STABILITY PROBLEMS IN NATURAL SIGHTSEEING AREAS - AN EXAMPLE FROM ARVANITIA, NAFPLIO, GREECE

The morphological and geological setting of Greece, the active tectonics and the irrational human activities results to the fact that several natural sightseeing areas or even more, archaeological sites and monuments are located in areas with unfavourable geotechnical conditions. The selection of the proper support and protection measures in most of the cases appear to be very difficult because the applied measures must reassure the minimum aesthetic destruction of the sites. The natural sightseeing area of the Arvanitia walkway, in Nafplio city, is a typical example of site, with extensive human activities, manifesting serious rockfall stability problems. The applied stability analysis pointed out the geotechnical problems and allowed the suggestion of measures for the improvement of the geotechnical behaviour of the rock mass. The measures were planned with respect to the natural beauty and the historical character of the site. Further more, the stability problems located at the slopes of the Kastoria lake walkway are briefly presented. The differences between the two sites revealed the geotechnical problems arising when the landplaning engineers do not take under consideration the engineering geological conditions during the construction of infrastructures

Application of geostatistical simulation models in the charac- terization of complex geological structures

Η χρήση μεθόδων προσομοίωσης στη γεωστατιστική μπορεί να οδηγήσει στην ανάπτυξη αριθμητικών μοντέλων χωρικής κατανομής συνεχών γεωλογικών μεταβλητών (περιεκτικότητα, πάχος, πυκνότητα, κλπ) ή κατηγορικών μεταβλητών (γεωλογικοί σχηματισμοί και λιθολογικές φάσεις ή τύποι πετρωμάτων). Στην παρούσα εργασία, η ανασκόπηση των κλασικών μεθόδων προσομοίωσης, όπως η Sequential Indicator Simulation (SIS), αναδεικνύει ένα σημαντικό μειονέκτημα που προκύπτει από τις θεωρητικές δυσκολίες στην ανάπτυξη ενός έγκυρου μοντέλου συνδιασποράς. Αντιθέτως, ένα παρόμοιο μοντέλο μπορεί να οριστεί αυτόματα στο πλαίσιο της Truncated Gaussian Method (TGS). Η μέθοδος αυτή βασίζεται στη δημιουργία κατηγορικών μεταβλητών μέσω της αποκοπής μίας πολλαπλά κανονικής τυχαίας μεταβλητής σε διάφορα όρια. Η Plurigaussian Simulation Method (PGS) αποτελεί επέκταση της προηγούμενης με τη διαφορά στην ταυτόχρονη αποκοπή περισσότερων της μίας τυχαίων μεταβλητών. Στη συνέχεια της εργασίας παρουσιάζεται μία εφαρμογή αυτής της μεθόδου στην πεδιάδα της Δυτικής Θεσσαλίας. Τα αποτελέσματα δείχνουν ότι η μέθοδος είναι αποτελεσματική στην αναπαραγωγή των χωρικών χαρακτηριστικών των διαφόρων λιθολογικών σχηματισμών και της κατανομής τους στο χώρο.Geostatistical simulation methods are able to generate numerical models or relations of the spatial distribution of a continuous geologic variable (grade, thickness, density, etc.) or a categorical variable (geological units and lithofacies or rock types). In this work, a review of traditional simulation techniques, as the Sequential Indicator Simulation (SIS), reveals a major pitfall that comes from theoretical difficulties in the development of a valid cross covariance model. On the contrary, a valid indicator cross covariance model is automatically defined in the framework of the Truncated Gaussian Simulation Method (TGS). This method is based on the concept that the categorical variables are obtained by truncating one standard multigaussian random variable at different thresholds. Plurigaussian Simulation Method (PGS) is an extension of the TGS Method but based on the simultaneous truncation of several multigaussian variables. An application of Plurigaussian method to simulate the lithofacies in the alluvial formations of the West Thessaly Basin is finally presented. This method was shown to be effective in reproducing the spatial characteristics of the different lithofacies and their distribution across the studied area

An integrated system dynamics - Cellular automata model for distributed water-infrastructure planning

PublishedJournal ArticleThis is the author accepted manuscript. The final version is available from IWA Publishing via the DOI in this record.© IWA Publishing 2016.Modern distributed water-Aware technologies (including, for example, greywater recycling and rainwater harvesting) enable water reuse at the scale of household or neighbourhood. Nevertheless, even though these technologies are, in some cases, economically advantageous, they have a significant handicap compared to the centralized urban water management options: It is not easy to estimate a priori the extent and the rate of the technology spread. This disadvantage is amplified in the case of additional uncertainty due to expansion of an urban area. This overall incertitude is one of the basic reasons the stakeholders involved in urban water are sceptical about the distributed technologies, even in the cases where these appear to have lower cost. In this study, we suggest a methodology that attempts to cope with this uncertainty by coupling a cellular automata (CA) and a system dynamics (SD) model. The CA model is used to create scenarios of urban expansion including the suitability of installing water-Aware technologies for each new urban area. Then, the SD model is used to estimate the adoption rate of the technologies. Various scenarios based on different economic conditions and water prices are assessed. The suggested methodology is applied to an urban area in Attica, Greece.This research has been co-financed by the European Union (European Social Fund– ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: THALES. Investing in knowledge society through the European Social Fund. Hydropolis: Urban development and water infrastructure - Towards innovative decentralized urban water management

GIS- BASED APPLICATION FOR GEOTECHNICAL DATA MANAGING

The need to provide data management capabilities in geotechnical projects, makes data visualization in a more understanding way vital, while improvements in computer science, have created an opportunity to rethink the manner in which such data is archived and presented. Geographic Information Systems are considered nowadays as principal methods for analysis, utilizing their ability of manipulating, compiling and processing spatial data, such as geotechnical one. In this paper, the development of Borehole Analysis System (BAS) a specific Graphical User Interface (GUI) application is proposed to access geotechnical data with the aim of a relational database and an open source GIS platform, embodied in the application. The BAS, is able to integrate multiple layers of gathered information and to derive additional knowledge by applying statistical and data mining algorithms with the use of spatial query tools. These can give reasonable conclusions and better representation in 2-D and 3-D environment. The presented application is illustrated with an example from field practice, testifying its ability to be a useful tool for management and presentation of geological and geotechnical borehole data

KNN vs. Bluecat—Machine Learning vs. Classical Statistics

Uncertainty is inherent in the modelling of any physical processes. Regarding hydrological modelling, the uncertainty has multiple sources including the measurement errors of the stresses (the model inputs), the measurement errors of the hydrological process of interest (the observations against which the model is calibrated), the model limitations, etc. The typical techniques to assess this uncertainty (e.g., Monte Carlo simulation) are computationally expensive and require specific preparations for each individual application (e.g., selection of appropriate probability distribution). Recently, data-driven methods have been suggested that attempt to estimate the uncertainty of a model simulation based exclusively on the available data. In this study, two data-driven methods were employed, one based on machine learning techniques, and one based on statistical approaches. These methods were tested in two real-world case studies to obtain conclusions regarding their reliability. Furthermore, the flexibility of the machine learning method allowed assessing more complex sampling schemes for the data-driven estimation of the uncertainty. The anatomisation of the algorithmic background of the two methods revealed similarities between them, with the background of the statistical method being more theoretically robust. Nevertheless, the results from the case studies indicated that both methods perform equivalently well. For this reason, data-driven methods can become a valuable tool for practitioners