18 resultados para Precast slabs

em Cambridge University Engineering Department Publications Database


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We present experimental measurements on Silicon-on-insulator (SOI) photonic crystal slabs with an active layer containing Er3+ ions-doped Silicon nanoclusters (Si-nc), showing strong enhancement of 1.54 μm emission at room temperature. We provide a systematic theoretical analysis to interpret such results. In order to get further insight, we discuss experimental data on the guided luminescence of unpatterned SOI planar slot waveguides, which show enhanced light emission in transverse-magnetic (TM) modes over transverse-electric (TE) ones. ©2007 IEEE.

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An engineer assessing the load-carrying capacity of an existing reinforced concrete slab is likely to use elastic analysis to check the load at which the structure might be expected to fail in flexure or in shear. In practice, many reinforced concrete slabs are highly ductile in flexure, so an elastic analysis greatly underestimates the loads at which they fail in this mode. The use of conservative elastic analysis has led engineers to incorrectly condemn many slabs and therefore to specify unnecessary and wasteful flexural strengthening or replacement. The lower bound theorem is based on the same principles as the upper bound theorem used in yield line analysis, but any solution that rigorously satisfies the lower bound theorem is guaranteed to be a safe underestimate of the collapse load. Jackson presented a rigorous lower bound method that obtains very accurate results for complex real slabs.

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A series of tests on filigree slab joints was performed with the aim of assessing whether such joints can be reliably used in the construction of two-way spanning reinforced concrete slabs. The test results were compared with code requirements. Adequate joint performance is shown to be achievable when the joints are appropriately detailed. Further research is recommended for the formulation of a more generic understanding when the design parameters are varied from those studied in this work.

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The US National Academy of Engineering recently identified restoring and improving urban infrastructure as one of the grand challenges of engineering. Part of this challenge stems from the lack of viable methods to map/label existing infrastructure. For computer vision, this challenge becomes “How can we automate the process of extracting geometric, object oriented models of infrastructure from visual data?” Object recognition and reconstruction methods have been successfully devised and/or adapted to answer this question for small or linear objects (e.g. columns). However, many infrastructure objects are large and/or planar without significant and distinctive features, such as walls, floor slabs, and bridge decks. How can we recognize and reconstruct them in a 3D model? In this paper, strategies for infrastructure object recognition and reconstruction are presented, to set the stage for posing the question above and discuss future research in featureless, large/planar object recognition and modeling.