Browsing by Subject "SCC"
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Item Analysis of Short and Long Term Deformations in a Continuous Precast Prestressed Concrete Girder(2014-12-09) Sarremejane, TristanA precast prestressed concrete girder using in-span splices to extend the span length is constructed to investigate performance under service and ultimate load conditions. Continuity is provided through the splices by a combination of mild steel reinforcement plus post-tensioned prestress. The thesis focuses on the study of short and long term deformations in the test specimen between the time the pretensioned prestressed segments were first cast, through splicing, deck construction and curing, and then initial testing. To support these observations, three creep frames are set up and shrinkage readings are taken. Previous research is reviewed to determine what models should be used for the analysis of the experimental results. A time-dependent Matlab program based on AAASHTO recommendations is developed to predict the prestress losses due to the short and long-term deformations. Experimental observations from the test specimen are compared to those predictions. The predictions by most models available for assessing long-term deformations due to creep and shrinkage are overestimated when compared to the experimental observations. Unreliable predictions of prestress losses due to long-term deformations may have significant repercussions on a long-span structure; an over-estimation may lead to a design being too conservative, while an under-estimation may lead to cracking and thereby excessive deflections under service loading. It appears that the over-estimation is, in part, due to the girder units being constructed with self-consolidating concrete (SCC). It is concluded that improved estimates of deformations for such structures composed of SCC girders can be achieved if a correction factor of 0.6 is applied to the AASHTO recommendations.Item High Performance Information Filtering on Many-core Processors(2013-12-06) Tripathy, AalapThe increasing amount of information accessible to a user digitally makes search difficult, time consuming and unsatisfactory. This has led to the development of active information filtering (recommendation) systems that learn a user?s preference and filter out the most relevant information using sophisticated machine learning techniques. To be scalable and effective, such systems are currently deployed in cloud infrastructures consisting of general-purpose computers. The emergence of many-core processors as compute nodes in cloud infrastructures necessitates a revisit of the computational model, run-time, memory hierarchy and I/O pipelines to fully exploit available concurrency within these processors. This research proposes algorithms & architectures to enhance the performance of content-based (CB) and collaborative information filtering (CF) on many-core processors. To validate these methods, we use Nvidia?s Tesla, Fermi and Kepler GPUs and Intel?s experimental single chip cloud computer (SCC) as the target platforms. We observe that ~290x speedup and up to 97% energy savings over conventional sequential approaches. Finally, we propose and validate a novel reconfigurable SoC architecture which combines the best features of GPUs & SCC. This has been validated to show ~98K speedup over SCC and ~15K speedup over GPU.