Deep mixing (DM) is the modification of in situ soil to increase strength, control deformation, and reduce permeability. Multi–axis augers and mixing paddles are used to construct overlapping columns strengthened by mixing cement with in situ soils. This method has been used for excavation support to increase bearing capacity, reduce movements, prevent sliding failure, control seepage by acting as a cut–off barrier, and as a measure against base heave. DM is effectively used in excavations both in conjunction with and in substitution of traditional techniques, where it results in more economical and convenient solutions for the stability of the system and the prevention of seepage. Although DM is currently used for excavation control in numerous projects, no standard procedure has been developed and the different applications have not been evaluated. As this technique emerges as a more economical and effective alternative to traditional excavation shoring, there is a need for guidelines describing proven procedures for evaluation of design, analysis and construction. The main objective of this research is to develop a methodology to design retaining systems using deep mixing technology. The method will be evaluated using numerical analysis of one selected case history.

This research investigates the stress anslysis of a tower skirt support respectively by the earthquake and the wind pressure loading. The researcher used the computer-aided design software Solidworks to set up the tower model and he used finite element software ANSYS to analyze the structure. This study aims to analyze the safety of a tower support for different models. By the finite element analysis, this study found the largest displacement of tower, the von Mises stresses, and the safety factor for the skirt support. Among them, the butted-flared skirt support has the smallest displacement. According to the analysis result, the empty crate type design of skirt support revealed the safety factor of stress greater than two, which ensure that the equipment safety and material-savings. It is expected the study findings can provide a reference for designing or repairing a tower skirt support.

Thesis (Ph. D.)--University of Missouri-Columbia, 2002. / Typescript. Vita. Includes bibliographical references (leaves 134-140). Also available on the Internet.

Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 331-353). Available also in a digital version from Dissertation Abstracts.

Thesis--School of Advanced Airpower Studies, Maxwell Air Force Base, Ala., 1992-93. / Title from title screen (viewed Oct. 21, 2003). "August 1994." Includes bibliographical references.

Thesis (Ph. D.)--University of Missouri-Columbia, 2002. / Typescript. Vita. Includes bibliographical references (leaves 134-140). Also available on the Internet.

Thesis (M. Sc.)--University of Hong Kong, 1995. / Includes bibliographical references.

Thesis (M.S. in Information Technology Management)--Naval Postgraduate School, June 2003. / Thesis advisor(s): Daniel R. Dolk, Glenn Cook. Includes bibliographical references (p. 113-114). Also available online.

In an effort to improve understanding of the role that support groups such as the National Stuttering Association (NSA) play in helping people who stutter, speech-language pathologists who participate in the NSA were asked to participate in a research survey. Previous research and anecdotal evidence has shown that support groups not only benefit people who stutter, but also the professionals who work with them. Participation in the NSA can enhance SLPs' understanding of stuttering, and their comfort level and competence working with people who stutter. However, the relationship between support groups and professionals has not been adequately studied. / text

Many computer vision problems involve building automatic systems by extracting complex high-level information from visual data. Such problems can often be modeled using structural models, which relate raw input variables to structural high-level output variables. Structural support vector machine is a discriminative method for learning structural models. It allows a flexible feature construction with good robustness against overfitting, and thus provides state-of-the-art prediction accuracies for structural prediction tasks in computer vision. This thesis first studies the application of structural SVMs in interactive image segmentation. A novel interactive image segmentation technique that automatically learns segmentation parameters tailored for each and every image is proposed. Unlike existing work, the proposed method does not require any offline parameter tuning or training stage, and is capable of determining image-specific parameters according to some simple user interactions with the target image. The segmentation problem is modeled as an inference of a conditional random field (CRF) over a segmentation mask and the target image. This CRF is parametrized by the weights for different terms (e.g., color, texture and smoothing). These weight parameters are learned via a one-slack structural SVM, which is solved using a constraint approximation scheme and the cutting plane algorithm. Experimental results show that the proposed method, by learning image-specific parameters automatically, outperforms other state-of-the-art interactive image segmentation techniques. This thesis then uses structural SVMs to speed up large scale relatively-paired space analysis. A new multi-modality analysis technique based on relatively-paired observations from multiple modalities is proposed. Relative-pairing information is encoded using relative proximities of observations in a latent common space. By building a discriminative model and maximizing a distance margin, a projection function that maps observations into the latent common space is learned for each modality. However, training based on large scale relatively-paired observations could be extremely time consuming. To this end, the training is reformulated as learning a structural model, which can be optimized by the cutting plane algorithm where only a few training samples are involved in each iteration. Experimental results validate the effectiveness and efficiency of the proposed technique. / published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy