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Amphibolites from several localities have been studied chemically. The rocks were spectrographically analyzed for Cr, V, Ni, Co, Sc, Zr, Sr, and Ba and major element analyses were done on 20 amphibolites, sediments, and igneous rocks. The chemistry of sediments and igneous rocks which right isochemically metamorphose to amphibolite has been studied. Discriminant function analyses were performed on major and minor elements, to classify rocks of known and unknown origins. / Thesis / Master of Science (MS)

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A research report submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Engineering / Tunnelling projects in the weak Kalahari rock masses of the Northern Cape, South Africa present significant design challenges for both large and small-scale excavations. Most of the design of tunnelling and support carried out at present in the Kalahari is based on experience, analytical and empirical methods. The approach typically makes use of limited geotechnical information from the project site.The Kalahari basin is a complex geotechnical environment. Tunnelling projects are sensitive to the variable ground and groundwater conditions. A detailed site investigation to establish the geological and geotechnical model is critical in the selection of the appropriate excavation method and tunnel design. A review of early tunnelling projects revealed that in situ stresses and water infiltration is a long-term stability concern for inadequately lined tunnels through the red clay and weathered rock masses.This project explores the use of numerical modelling to predict the expected failure modes of the weak rock masses, with emphasis placed on concrete liner support for maintaining stability. The support models are analysed using 2D numerical models to determine the Factor of Safety of the liner. A support design criterion for reinforced and unreinforced concrete is introduced and appliedto the models to evaluate the lining thickness. The effect of using 2D plane strain models instead of3D analysis was also investigated. The total displacement of numerical models built in RS2 wascompared to RS3 models. / NG (2020)

Carbonate strata preserve indicators of local marine environments through time. Such indicators often include microfossils that have relatively unique conditions under which they can survive, including light, nutrients, salinity, and especially water temperature. As such, microfossils are environmental proxies. When these microfossils are preserved in the rock record, they constitute key components of depositional facies. Spence et al. (2004, 2007) has proposed several approaches for determining the facies of a given stratigraphic succession based upon these proxies. Cluster analysis can be used to determine microfossil groups that represent specific environmental conditions. Identifying which microfossil groups exist through time can indicate local environmental change. When new observations (microfossils) are found, classification analysis can be used to predict group membership. Kristen Briggs (2005) identified the microfossils present in sedimentary strata within a specific time interval (Morrowan) of Pennsylvanian-age rocks. In this study we expand analysis to overlying Atokan and Desmoinesian strata. The Bird Spring Formation in Arrow Canyon, Nevada records cycles of environmental change as evidenced by changes in microfossils. Our research investigates cluster and classification analyses as tools for determining the marine facies succession. Light, nutrients, salinity, and water temperature are very dependent on water depth; therefore, our analyses essentially indicate the relative rise and fall of sea-level during Early to Middle Pennsylvanian time.

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Yes / Decision tree is a simple but powerful learning technique that is considered as one of the famous learning algorithms that have been successfully used in practice for various classification tasks. They have the advantage of producing a comprehensible classification model with satisfactory accuracy levels in several application domains. In recent years, the volume of data available for learning is dramatically increasing. As a result, many application domains are faced with a large amount of data thereby posing a major bottleneck on the computability of learning techniques. There are different implementations of the decision tree using different techniques. In this paper, we theoretically and experimentally study and compare the computational power of the most common classical top-down decision tree algorithms (C4.5 and CART). This work can serve as part of review work to analyse the computational complexity of the existing decision tree classifier algorithm to gain understanding of the operational steps with the aim of optimizing the learning algorithm for large datasets.