Les grands réaménagements urbains génèrent d’importants volumes de terres excavées, dont la valorisation est désormais possible si elles sont compatibles avec le fond pédo-géochimique du site receveur. Afin de faciliter cette valorisation, cette thèse s’intéresse à des méthodologies de traitement de données acquises à d'autres fins que la détermination du fond pédo-géochimique, afin de fournir un référentiel de qualité géochimique des sols urbains à l'échelle d'un quartier ou d'une ville. Les premiers tests montrent les limites des calculs statistiques, usuellement utilisés avec des données dédiées au fond pédo-géochimique du fait de leur sensibilité à la limite de quantification, à la loi de distribution ou encore au pourcentage d'anomalie. Le découpage du territoire en entités géographiques cohérentes à partir de la nature des sols et sous-sols et des pressions anthropiques (actuelles ou passées) permet la constitution de différents niveaux de fond suivant la qualité géochimique des sols, mais une des difficultés concerne le renseignement de l'ensemble des entités. La classification statistique des échantillons, même spatialisée, n'apporte, actuellement pas de résultats exploitables pour la gestion des terres excavées. Enfin, le filtrage géostatistique via un modèle linéaire de corégionalisation présente une alternative solide pour le filtrage des anomalies et l'estimation du fond à l'échelle d'un quartier. / Large urban redevelopments generate large volumes of excavated soils whose reusability is now possible if they are compatible with the pedo-geochemical background of the receiving site. In order to facilitate this valorization, this thesis focuses on processing methodologies on data acquired for other purposes than the determination of the pedo-geochemical background, in order to provide a geochemical quality reference system for urban soils at the scale of one neighborhood or city. The first tests show the limits of the statistical stresholds, usually used with data dedicated to the pedo-geochemical background because of their sensitivity to the limit of quantification, to the distribution law or to the percentage of anomaly. The segmentation of the territory into coherent geographical entities based on the nature of the soil and subsoil and the anthropogenic pressures (current or past) allows the constitution of different geochemical background levels according to the geochemical quality of the soils, but one of the difficulties concerns the information of all entities. The statistical classification of the samples, even if spatialized, does not currently provide usable results for the management of the excavated earth. Lastly, geostatistical filtering via a linear model of coregionalisation provides a solid alternative for anomaly filtering and background estimation at a neighborhood scale.

In South Central Cut Bank Sand Unit (SCCBSU) of Cut Bank field, primary production and waterflood projects have resulted in recovery of only 29 % of the original oil in place from heterogeneous, fluvial sandstone deposits. Using highresolution sequence stratigraphy and geostatistical analysis, I developed a geologic model that may improve the ultimate recovery of oil from this field. In this study, I assessed sequence stratigraphic concepts for continental settings and extended the techniques to analyze low-accommodation alluvial systems of the Cut Bank and Sunburst members of the lower Kootenai formation (Cretaceous) in Cut Bank field. Identification and delineation of five sequences and their bounding surfaces led to a better understanding of the reservoir distribution and variability. Recognition of stacking patterns allowed for the prediction of reservoir rock quality. Within each systems tract, the best quality reservoir rocks are strongly concentrated in the lowstand systems tract. Erosional events associated with falling baselevel resulted in stacked, communicated (multistory) reservoirs. The lowermost Cut Bank sandstone has the highest reservoir quality and is a braided stream parasequence. Average net-to-gross ratio value (0.6) is greater than in other reservoir intervals. Little additional stratigraphically untapped oil is expected in the lowermost Cut Bank sandstone. Over most of the SCCBSU, the Sunburst and the upper Cut Bank strata are valley-fill complexes with interfluves that may laterally compartmentalize reservoir sands. Basal Sunburst sand (Sunburst 1, average net-to-gross ratio ~0.3) has better reservoir quality than other Sunburst or upper Cut Bank sands, but its reservoir quality is significantly less than that of lower Cut Bank sand. Geostatistical analysis provided equiprobable representations of the heterogeneity of reservoirs. Simulated reservoir geometries resulted in an improved description of reservoir distribution and connectivity, as well as occurrences of flow barriers. The models resulting from this study can be used to improve reservoir management and well placement and to predict reservoir performance in Cut Bank field. The technical approaches and tools from this study can be used to improve descriptions of other oil and gas reservoirs in similar depositional systems.

Η παρούσα έρευνα εντάσσεται στο πλαίσιο εκπόνησης διδακτορικής διατριβής στο Εργαστήριο του Τμήματος Αρχιτεκτόνων Μηχανικών του Πανεπιστημίου Πατρών. Η χαρτογράφηση της εγκληματικότητας (Crime Mapping) είναι η διαδικασία χρήσης της τεχνολογίας των Γεωγραφικών Συστημάτων Πληροφοριών και της εφαρμογής μεθόδων και τεχνικών της Χωρικής Ανάλυσης και της Χαρτογραφίας για την μελέτη και ανάλυση εγκληματικών συμβάντων. Ένα μεγάλο σύνολο ανθρωπίνων δραστηριοτήτων αναπτύσσεται, παρατηρείται και καταγράφεται στους αστικούς χώρους. Η επίδραση που ασκεί ο χώρος στην ανθρώπινη συμπεριφορά αλλά και η επίδραση της ανθρώπινης συμπεριφοράς που ασκείται πάνω στον χώρο είναι μια σχέση αμφίδρομη και αποτελεί σημαντικό στοιχείο για το σχεδιασμό. Συνεπώς, η δομή αυτή καθ’ αυτή των πόλεων, οι ήδη διαμορφωμένοι δημόσιοι αστικοί χώροι και οι κοινωνικό-οικονομικές αλλαγές που συντελούνται, συνιστούν μια πρόκληση για τους σύγχρονους σχεδιαστές του χώρου αυτού. Η έρευνα αφορά τη μελέτη της χωρικής κατανομής εγκλημάτων ιδιοκτησίας (απόπειρες, κλοπές, διαρρήξεις, ληστείες) στον αστικό ιστό της πόλης σε σχέση με χωρικά χαρακτηριστικά και κυρίως με πολεοδομικές παραμέτρους, ενώ παράλληλα εστιάζει με τρόπο ώστε να είναι δυνατή η εφαρμογή και ενσωμάτωση πορισμάτων της εγκληματολογίας στην διαδικασία του πολεοδομικού σχεδιασμού και γενικότερα του σχεδιασμού του χώρου. Ολοκληρώνεται, με συστηματική μελέτη περίπτωσης, μέσω αναλυτικής στατιστικής ανάλυσης και ανάλυσης γεωστατιστικής σχετικών δεδομένων που αφορούν το Σχέδιο Πόλεως Πατρών και τη δημιουργία γεωσυνόλων και θεματικών χαρτών που απεικονίζουν τη χωρική κατανομή του φαινομένου. Για την ανάλυση των χαρακτηριστικών της μελέτης περίπτωσης χρησιμοποιούνται χωρικές βάσεις δεδομένων με στοιχεία που η Γενική Αστυνομική Διεύθυνση Περιφέρειας Δυτικής Ελλάδος διέθεσε για την εκπόνηση της εργασίας: 4.770 απογραφικά δελτία εγκληματικών συμβάντων (εγκλήματα ιδιοκτησίας) που αφορούν το σύνολο τεσσάρων ετών από το 2007 έως και το 2010. Η εκπόνηση της έρευνας χρηματοδοτήθηκε από το Επιχειρησιακό Πρόγραμμα Εκπαίδευση και Δια Βίου Μάθηση, Ηράκλειτος ΙΙ, Υπουργείο Παιδείας και Θρησκευμάτων, ΕΣΠΑ 2007 – 2013, Ευρωπαϊκό Κοινωνικό Ταμείο. / This thesis has been elaborated in the Laboratory of Urban and Regional Planning, Department of Architecture, University of Patras. Crime Mapping is the process of using GIS technology and the implementation of Spatial Analysis and Mapping methods for studying and analysis of criminal incidents. In recent years, the rapid evolution of GIS technology and the availability of digital spatial data have strengthened the significant role of spatial analysis and GIS in crime analysis. A great deal of human activity is developed, observed and recorded in urban areas. The human impact is implemented on the urban areas as well as the urban areas have an impact on human behavior, setting a correlation that is an important design/planning characteristic. Consequently, the structure of modern cities, the existing urban public spaces along with the socioeconomic changes that happen constitute a challenge for contemporary designers and planners. The research is based on the study of spatial criminal distribution of property crimes (attempts, thefts, burglaries, robberies) in the urban web of a city, in terms of spatial characteristics and urban planning parameters while it focuses on criminology findings so as they can be incorporated and applied in urban planning in order to design and identify strategic orientations and create safer urban areas. It concludes with a systematic case study through an analytical statistical and geostatistical analysis of relevant data concerning the Master Plan of city of Patras and the development of geosets and thematic maps depicting the spatial distribution of the phenomenon. For that purpose, the Central Police Department of Patras offered to the university Laboratory all census forms of criminal acts and events (property crimes): 4.770 reports that took place at the city of Patras (Greece) during the years 2007 – 2010. 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: Heracleitus II.

Das Ziel der vorliegenden Arbeit war die Erstellung eines dreidimensionalen Untergrundmodells der Region Göttingen basierend auf einer geotechnischen Klassifikation der unkosolidierten Sedimente. Die untersuchten Materialen reichen von Lockersedimenten bis hin zu Festgesteinen, werden jedoch in der vorliegenden Arbeit als Boden, Bodenklassen bzw. Bodenkategorien bezeichnet. Diese Studie evaluiert verschiedene Möglichkeiten durch geostatistische Methoden und Simulationen heterogene Untergründe zu erfassen. Derartige Modellierungen stellen ein fundamentales Hilfswerkzeug u.a. in der Geotechnik, im Bergbau, der Ölprospektion sowie in der Hydrogeologie dar. Eine detaillierte Modellierung der benötigten kontinuierlichen Parameter wie z. B. der Porosität, der Permeabilität oder hydraulischen Leitfähigkeit des Untergrundes setzt eine exakte Bestimmung der Grenzen von Fazies- und Bodenkategorien voraus. Der Fokus dieser Arbeit liegt auf der dreidimensionalen Modellierung von Lockergesteinen und deren Klassifikation basierend auf entsprechend geostatistisch ermittelten Kennwerten. Als Methoden wurden konventionelle, pixelbasierende sowie übergangswahrscheinlichkeitsbasierende Markov-Ketten Modelle verwendet. Nach einer generellen statistischen Auswertung der Parameter wird das Vorhandensein bzw. Fehlen einer Bodenkategorie entlang der Bohrlöcher durch Indikatorparameter beschrieben. Der Indikator einer Kategorie eines Probepunkts ist eins wenn die Kategorie vorhanden ist bzw. null wenn sie nicht vorhanden ist. Zwischenstadien können ebenfalls definiert werden. Beispielsweise wird ein Wert von 0.5 definiert falls zwei Kategorien vorhanden sind, der genauen Anteil jedoch nicht näher bekannt ist. Um die stationären Eigenschaften der Indikatorvariablen zu verbessern, werden die initialen Koordinaten in ein neues System, proportional zur Ober- bzw. Unterseite der entsprechenden Modellschicht, transformiert. Im neuen Koordinatenraum werden die entsprechenden Indikatorvariogramme für jede Kategorie für verschiedene Raumrichtungen berechnet. Semi-Variogramme werden in dieser Arbeit, zur besseren Übersicht, ebenfalls als Variogramme bezeichnet. IV Durch ein Indikatorkriging wird die Wahrscheinlichkeit jeder Kategorie an einem Modellknoten berechnet. Basierend auf den berechneten Wahrscheinlichkeiten für die Existenz einer Modellkategorie im vorherigen Schritt wird die wahrscheinlichste Kategorie dem Knoten zugeordnet. Die verwendeten Indikator-Variogramm Modelle und Indikatorkriging Parameter wurden validiert und optimiert. Die Reduktion der Modellknoten und die Auswirkung auf die Präzision des Modells wurden ebenfalls untersucht. Um kleinskalige Variationen der Kategorien auflösen zu können, wurden die entwickelten Methoden angewendet und verglichen. Als Simulationsmethoden wurden "Sequential Indicator Simulation" (SISIM) und der "Transition Probability Markov Chain" (TP/MC) verwendet. Die durchgeführten Studien zeigen, dass die TP/MC Methode generell gute Ergebnisse liefert, insbesondere im Vergleich zur SISIM Methode. Vergleichend werden alternative Methoden für ähnlichen Fragestellungen evaluiert und deren Ineffizienz aufgezeigt. Eine Verbesserung der TP/MC Methoden wird ebenfalls beschrieben und mit Ergebnissen belegt, sowie weitere Vorschläge zur Modifikation der Methoden gegeben. Basierend auf den Ergebnissen wird zur Anwendung der Methode für ähnliche Fragestellungen geraten. Hierfür werden Simulationsauswahl, Tests und Bewertungsysteme vorgeschlagen sowie weitere Studienschwerpunkte beleuchtet. Eine computergestützte Nutzung des Verfahrens, die alle Simulationsschritte umfasst, könnte zukünftig entwickelt werden um die Effizienz zu erhöhen. Die Ergebnisse dieser Studie und nachfolgende Untersuchungen könnten für eine Vielzahl von Fragestellungen im Bergbau, der Erdölindustrie, Geotechnik und Hydrogeologie von Bedeutung sein.

Made available in DSpace on 2014-08-20T14:33:01Z (GMT). No. of bitstreams: 1 tese_viviane_santos_silva_terra.pdf: 3226542 bytes, checksum: 468c5162baa20a455b268e2abda6ef7e (MD5) Previous issue date: 2012-12-27 / The fruitculture is an important component of Brazilian agribusiness assuming prominence in the economy of the state of Rio Grande do Sul (RS). The municipality of Pelotas, located in the south part of RS state, is responsible for 90% of peach production having adequate conditions of climate and soil for its production. One of the main problems faced by fruit farmers is the management of the peach orchard, which has been homogeneously managed, without considering the spatial and temporal variability of soil, plant and climate attributes in the peach orchard. Therefore, some aspects deserve attention from researchers, among them we can highlight the knowledge of the physical environment in which the peach tree grows. This work had as objectives: i. to evaluate the relationship between soil hydric-physical and crop attributes through the canonical correlation analysis, ii. to determine the spatial and temporal structures of soil hydric-physical, crop and climate attributes, applying geostatistics analysis. The work was conducted in an experimental area, located in the city of Morro Redondo-RS, cultivated with 18 line peach crops, cv. Esmeralda, totaling 1,450 plants. In this area was established a experimental grid consisted of 101 experimental peach trees. At a distance of 0.50m to the right side of each crop tree, trenches were opened to collect disturbed and undisturbed soil samples to determine the following soil hydric-physical attributes, in the 0.00-0.10m and 0.10-0.20m soil layers: sand, silt and clay contents, soil bulk density, soil total porosity, soil macro- and micro-porosities, and volumetric soil water content. We also evaluated the following peach crop attributes in the years of 2010 and 2011: trunk diameter, size of the fruit/plant, total number of fruits/plant, fresh mass of fruits, fruit firmness, brix content and crop productivity. The attributes of climate air temperature and relative humidity were also evaluated in the peach orchard. All data sets were submitted to descriptive statistical analysis. The correlation between soil and crop attributes was evaluated by canonical correlation analysis. The spatial and temporal variability structures of all data sets were evaluated by geostatistics as well as the relationship between them. Results show that the canonical correlation analysis identified the highest correlation coefficients of soil micro-porosity and clay content variables and peach productivity, and that the geostatistical analysis allowed establishing homogeneous management zones of soil, crop and climate attributes as well as to infer, visually, spatial inter-relationships between variables. The association of soil, crop and climate maps indicates that future differentiated managements of agricultural practices on the peach orchard should be based on the spatial distribution of these variables aiming rational water and fertilizer applications to preserve natural resources in the agricultural farmers. / A fruticultura é um importante componente do agronegócio brasileiro assumindo destaque na economia do estado do Rio Grande do Sul (RS). O município de Pelotas, localizado no sul do RS, é responsável por 90% da produção de pêssego, possuindo condições adequadas de clima e solo para a sua produção. Um dos principais problemas enfrentados pelos produtores de frutas é o manejo do pomar, que tem sido realizado de forma homogênea, sem considerar a variabilidade espacial e temporal dos atributos do solo, da planta e do clima dentro do pomar. Desta forma, alguns aspectos merecem atenção dos pesquisadores, dentre os quais, pode-se destacar o conhecimento do ambiente físico no qual o pessegueiro se desenvolve. Este trabalho teve como objetivos avaliar a relação entre os atributos físico-hídricos do solo e da planta por meio da análise de correlação canônica, determinar a estrutura de correlação espacial e temporal dos atributos físico-hídricos do solo, da planta de pessegueiro e do clima, por meio da técnica de geoestatística. O trabalho foi conduzido em uma área experimental, localizada no município do Morro Redondo-RS, cultivada com a cultivar de pêssego Esmeralda com 18 linhas, totalizando 1.450 plantas, onde foi estabelecida uma malha experimental composta por 101 plantas. A 0,50m do lado direito da cada planta, foram abertas trincheiras para a coleta de amostras deformas e indeformadas de solo para determinação dos atributos físico-hídricos, nas profundidades de 0,00-0,10m e de 0,10-0,20m: teores de areia, silte e argila, densidade do solo, porosidade total, macro e microporosidade do solo e umidade volumétrica. Também foram avaliados os seguintes atributos da planta nos anos de 2010 e 2011: diâmetro do tronco, tamanho do fruto/planta, número total de frutos/planta, massa fresca dos frutos, firmeza de polpa, teor de brix e produtividade. Os atributos do clima temperatura do ar e umidade relativa do ar também foram avaliados dentro do pomar. Todos os conjuntos de dados foram submetidos a análise exploratória por meio da estatística descritiva. A análise de correlação canônica foi aplicada para analisar as inter-relações entre as variáveis do solo e da planta, enquanto que, a geoestatística foi aplicada para avaliar a estrutura de correlação espacial e temporal de todos os dados bem como relação entre eles. Resultados mostram que a análise de correlação canônica permitiu identificar as variáveis do solo argila e microporosidade como as mais fortemente correlacionadas com a produtividade do pessegueiro e que a análise geoestatística permitiu delimitar as zonas homogêneas dos atributos do solo, da planta e do clima bem como inferir visualmente sobre as inter-relações espaciais entre as variáveis. A associação dos mapas das variáveis do solo, da planta e do clima indica que os futuros manejos diferenciados de práticas agrícolas deverão se basear na distribuição espacial das variáveis no pomar de pessegueiro visto que isto possibilitará a racionalização da aplicação de insumos e de água e a preservação dos recursos naturais na propriedade.

La grotte de Lascaux (inscrite au patrimoine mondial de l’UNESCO et l’une des plus connues au monde) nécessite, dans le cadre de sa conservation et suite aux aléas subis depuis sa découverte, une attention particulière tant pour elle-même que pour son environnement. L’utilisation d’une méthode géophysique, la Tomographie de Résistivité électrique (TRE) qui est une méthode non intrusive par excellence, est donc proposée pour la reconnaissance du milieu karstique l’environnant.Un suivi temporel par TRE a été mise en oeuvre pour caractériser l’amont hydraulique de la grotte et surveiller la variation temporelle de la résistivité des terrains. A l’aide d’analyses statistiques, et en couplant les données microclimatiques aux résultats d’un suivi temporel de vingt-deux mois, nous avons montré la capacité de ce type de mesures à caractériser l’environnement épikarstique de la grotte, particulièrement les zones d’alimentation et celles permettant l’infiltration des eaux pluviales. Une modélisation du débit d’un écoulement situé à l’entrée de la grotte est proposée à partir des données de résistivité dans la zone d’alimentation identifiée au cours du suivi. Ce modèle a la capacité de prédire les arrêts et les reprises des écoulements dans la grotte, éventuellement des évènements journaliers.A partir des données de résistivité issues d’une prospection 3D du site, des modélisations géostatistiques par krigeage ordinaire et par indicatrice ont été effectuées permettant des représentations spatiales en fonction de la résistivité des terrains. Ces modèles se sont révélés extrêmement instructifs par l’imagerie de l’environnement karstique de la grotte de Lascauxqui en a résulté. Les limites des formations détritiques et des calcaires sont identifiées à l’est et l’ouest du site. A l’intérieur des calcaires, on retrouve, bien sûr, les anomalies conductrices déjà identifiées au cours du suivi temporel mais aussi leur extension spatiale. Ainsi, on a pu mettre en évidence la continuité spatiale de certaines anomalies.Le suivi temporel par TRE a permis la compréhension de la structure et du fonctionnement de l’alimentation de l’épikarst. Les modèles géostatistiques 3D ont montré leur efficacité pour la caractérisation de l’environnement de la grotte. Les résultats aideraient à proposer des conseils pour la protection du milieu environnant la grotte et ainsi pour la préservation de cette dernière. / The Lascaux cave, one of the most important prehistoric caves in the world, located in Dordogne (24, France) needs particular attention both for itself and for the environment interms of conservation and vulnerability since its discovery. Geophysical methods in particular Electrical Resistivity Tomography (ERT) enable us, in a non-invasive way, to monitor the karsticenvironment.A Time-Lapse monitoring by ERT was carried out next to the cave. Together with analysis of the local effective rainfall (ground water recharge) and the flow in the cave, the monitoringhelped us to identify an area where upstream underground water is probably stored e.g. arecharge zone. There is a large electrical contrast between the surrounding limestone and theprobable recharge zone. Then, a multivariate analysis through the resistivity values allowed usto characterize the model blocks, showing a specific behavior over time, especially the blockswith the lowest electrical resistivity. A prediction model of the flow in relation with the recharge zone succeeded to predict the beginning and the end of flow, even the daily event withextremely high value of flow.In order to visualize the environment in 3D condition, a geostatistical modelling was then applied to the resistivity values. The geostatistical models can emphasize the limit betweenthe limestone promontory and the clayey/sandy formations to the east/west part of the site. In the limestone promontory, the models also showed the possible connection between theanomalous conductive areas that may have a special consequence in this karstic environment.The Time-Lapse monitoring by ERT allows us to understand the karstic structures andrecharge phenomena. The 3D geostatistical modeling showed efficiency for the characterization of the cave environment. Those results can help to provide advices for the cave preservation.

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No description available.

Site characterization is an important task in Geotechnical Engineering. In situ tests based on standard penetration test (SPT), cone penetration test (CPT) and shear wave velocity survey are popular among geotechnical engineers. Site characterization using any of these properties based on finite number of in-situ test data is an imperative task in probabilistic site characterization. These methods have been used to design future soil sampling programs for the site and to specify the soil stratification. It is never possible to know the geotechnical properties at every location beneath an actual site because, in order to do so, one would need to sample and/or test the entire subsurface profile. Therefore, the main objective of site characterization models is to predict the subsurface soil properties with minimum in-situ test data. The prediction of soil property is a difficult task due to the uncertainities. Spatial variability, measurement ‘noise’, measurement and model bias, and statistical error due to limited measurements are the sources of uncertainities. Liquefaction in soil is one of the other major problems in geotechnical earthquake engineering. It is defined as the transformation of a granular material from a solid to a liquefied state as a consequence of increased pore-water pressure and reduced effective stress. The generation of excess pore pressure under undrained loading conditions is a hallmark of all liquefaction phenomena. This phenomena was brought to the attention of engineers more so after Niigata(1964) and Alaska(1964) earthquakes. Liquefaction will cause building settlement or tipping, sand boils, ground cracks, landslides, dam instability, highway embankment failures, or other hazards. Such damages are generally of great concern to public safety and are of economic significance. Site-spefific evaluation of liquefaction susceptibility of sandy and silty soils is a first step in liquefaction hazard assessment. Many methods (intelligent models and simple methods as suggested by Seed and Idriss, 1971) have been suggested to evaluate liquefaction susceptibility based on the large data from the sites where soil has been liquefied / not liquefied. The rapid advance in information processing systems in recent decades directed engineering research towards the development of intelligent models that can model natural phenomena automatically. In intelligent model, a process of training is used to build up a model of the particular system, from which it is hoped to deduce responses of the system for situations that have yet to be observed. Intelligent models learn the input output relationship from the data itself. The quantity and quality of the data govern the performance of intelligent model. The objective of this study is to develop intelligent models [geostatistic, artificial neural network(ANN) and support vector machine(SVM)] to estimate corrected standard penetration test (SPT) value, Nc, in the three dimensional (3D) subsurface of Bangalore. The database consists of 766 boreholes spread over a 220 sq km area, with several SPT N values (uncorrected blow counts) in each of them. There are total 3015 N values in the 3D subsurface of Bangalore. To get the corrected blow counts, Nc, various corrections such as for overburden stress, size of borehole, type of sampler, hammer energy and length of connecting rod have been applied on the raw N values. Using a large database of Nc values in the 3D subsurface of Bangalore, three geostatistical models (simple kriging, ordinary kriging and disjunctive kriging) have been developed. Simple and ordinary kriging produces linear estimator whereas, disjunctive kriging produces nonlinear estimator. The knowledge of the semivariogram of the Nc data is used in the kriging theory to estimate the values at points in the subsurface of Bangalore where field measurements are not available. The capability of disjunctive kriging to be a nonlinear estimator and an estimator of the conditional probability is explored. A cross validation (Q1 and Q2) analysis is also done for the developed simple, ordinary and disjunctive kriging model. The result indicates that the performance of the disjunctive kriging model is better than simple as well as ordinary kriging model. This study also describes two ANN modelling techniques applied to predict Nc data at any point in the 3D subsurface of Bangalore. The first technique uses four layered feed-forward backpropagation (BP) model to approximate the function, Nc=f(x, y, z) where x, y, z are the coordinates of the 3D subsurface of Bangalore. The second technique uses generalized regression neural network (GRNN) that is trained with suitable spread(s) to approximate the function, Nc=f(x, y, z). In this BP model, the transfer function used in first and second hidden layer is tansig and logsig respectively. The logsig transfer function is used in the output layer. The maximum epoch has been set to 30000. A Levenberg-Marquardt algorithm has been used for BP model. The performance of the models obtained using both techniques is assessed in terms of prediction accuracy. BP ANN model outperforms GRNN model and all kriging models. SVM model, which is firmly based on the theory of statistical learning theory, uses regression technique by introducing -insensitive loss function has been also adopted to predict Nc data at any point in 3D subsurface of Bangalore. The SVM implements the structural risk minimization principle (SRMP), which has been shown to be superior to the more traditional empirical risk minimization principle (ERMP) employed by many of the other modelling techniques. The present study also highlights the capability of SVM over the developed geostatistic models (simple kriging, ordinary kriging and disjunctive kriging) and ANN models. Further in this thesis, Liquefaction susceptibility is evaluated from SPT, CPT and Vs data using BP-ANN and SVM. Intelligent models (based on ANN and SVM) are developed for prediction of liquefaction susceptibility using SPT data from the 1999 Chi-Chi earthquake, Taiwan. Two models (MODEL I and MODEL II) are developed. The SPT data from the work of Hwang and Yang (2001) has been used for this purpose. In MODEL I, cyclic stress ratio (CSR) and corrected SPT values (N1)60 have been used for prediction of liquefaction susceptibility. In MODEL II, only peak ground acceleration (PGA) and (N1)60 have been used for prediction of liquefaction susceptibility. Further, the generalization capability of the MODEL II has been examined using different case histories available globally (global SPT data) from the work of Goh (1994). This study also examines the capabilities of ANN and SVM to predict the liquefaction susceptibility of soils from CPT data obtained from the 1999 Chi-Chi earthquake, Taiwan. For determination of liquefaction susceptibility, both ANN and SVM use the classification technique. The CPT data has been taken from the work of Ku et al.(2004). In MODEL I, cone tip resistance (qc) and CSR values have been used for prediction of liquefaction susceptibility (using both ANN and SVM). In MODEL II, only PGA and qc have been used for prediction of liquefaction susceptibility. Further, developed MODEL II has been also applied to different case histories available globally (global CPT data) from the work of Goh (1996). Intelligent models (ANN and SVM) have been also adopted for liquefaction susceptibility prediction based on shear wave velocity (Vs). The Vs data has been collected from the work of Andrus and Stokoe (1997). The same procedures (as in SPT and CPT) have been applied for Vs also. SVM outperforms ANN model for all three models based on SPT, CPT and Vs data. CPT method gives better result than SPT and Vs for both ANN and SVM models. For CPT and SPT, two input parameters {PGA and qc or (N1)60} are sufficient input parameters to determine the liquefaction susceptibility using SVM model. In this study, an attempt has also been made to evaluate geotechnical site characterization by carrying out in situ tests using different in situ techniques such as CPT, SPT and multi channel analysis of surface wave (MASW) techniques. For this purpose a typical site was selected wherein a man made homogeneous embankment and as well natural ground has been met. For this typical site, in situ tests (SPT, CPT and MASW) have been carried out in different ground conditions and the obtained test results are compared. Three CPT continuous test profiles, fifty-four SPT tests and nine MASW test profiles with depth have been carried out for the selected site covering both homogeneous embankment and natural ground. Relationships have been developed between Vs, (N1)60 and qc values for this specific site. From the limited test results, it was found that there is a good correlation between qc and Vs. Liquefaction susceptibility is evaluated using the in situ test data from (N1)60, qc and Vs using ANN and SVM models. It has been shown to compare well with “Idriss and Boulanger, 2004” approach based on SPT test data. SVM model has been also adopted to determine over consolidation ratio (OCR) based on piezocone data. Sensitivity analysis has been performed to investigate the relative importance of each of the input parameters. SVM model outperforms all the available methods for OCR prediction.

This research work concentrate on developing digital soil maps to support field based plant phenotyping research. We have developed soil organic matter content (OM), cation exchange capacity (CEC), natural soil drainage class, and tile drainage line maps using topographic indices and aerial imagery. Various prediction models (universal kriging, cubist, random forest, C5.0, artificial neural network, and multinomial logistic regression) were used to estimate the soil properties of interest.