Shear capacity of reinforced concrete beams using neural network

NoOptimum multi-layered feed-forward neural network (NN) models using a resilient back-propagation algorithm and early stopping technique are built to predict the shear capacity of reinforced concrete deep and slender beams. The input layer neurons represent geometrical and material properties of reinforced concrete beams and the output layer produces the beam shear capacity. Training, validation and testing of the developed neural network have been achieved using 50%, 25%, and 25%, respectively, of a comprehensive database compiled from 631 deep and 549 slender beam specimens. The predictions obtained from the developed neural network models are in much better agreement with test results than those determined from shear provisions of different codes, such as KBCS, ACI 318-05, and EC2. The mean and standard deviation of the ratio between predicted using the neural network models and measured shear capacities are 1.02 and 0.18, respectively, for deep beams, and 1.04 and 0.17, respectively, for slender beams. In addition, the influence of different parameters on the shear capacity of reinforced concrete beams predicted by the developed neural network shows consistent agreement with those experimentally observed

Fluctuation-Driven Vortex Fractionalization in Topologically Ordered Superfluids of Cold Atoms

We have studied spin structures of fluctuation-driven fractionalized vortices and topological spin order in 2D nematic superfluids of cold sodium atoms. Our Monte Carlo simulations suggest a softened pi-spin disclination structure in a half-quantum vortex when spin correlations are short ranged; in addition, calculations indicate that a unique non-local topological spin order emerges simultaneously as cold atoms become a superfluid below a critical temperature. We have also estimated fluctuation-dependent critical frequencies for half-quantum vortex nucleation in rotating optical traps and discussed probing these excitations in experiments.Comment: 5 pages, 2 figures; revised version accepted by Europhysics Letter

On the predominant mechanisms active during the high power diode laser modification of the wettability characteristics of an SiO2/Al2O3-based ceramic material

The mechanisms responsible for modifications to the wettability characteristics of a SiO2/Al2O3-based ceramic material in terms of a test liquid set comprising of human blood, human blood plasma, glycerol and 4-octonol after high power diode laser (HPDL) treatment have been elucidated. Changes in the contact angle, , and hence the wettability characteristics of the SiO2/Al2O3-based ceramic were attributed primarily to: modifications to the surface roughness of the ceramic resulting from HPDL interaction which accordingly effected reductions in ; the increase in the surface O2 content of the ceramic after HPDL treatment; since an increase in surface O2 content intrinsically brings about a decrease in , and vice versa and the increase in the polar component of the surface energy, due to the HPDL induced surface melting and resolidification which consequently created a partially vitrified microstructure that was seen to augment the wetting action. However, the degree of influence exerted by each mechanism was found to differ markedly. Isolation of each of these mechanisms permitted the magnitude of their influence to be qualitatively determined. Surface energy, by way of microstructural changes, was found to be by far the most predominant element governing the wetting characteristics of the SiO2/Al2O3-based ceramic. To a much lesser extent, surface O2 content, by way of process gas, was also seen to influence to a changes in the wettability characteristics of the SiO2/Al2O3-based ceramic, whilst surface roughness was found to play a minor role in inducing changes in the wettability characteristics

Wetting and bonding characteristics of selected liquid-metals with a high power diode laser treated alumina bioceramic

Changes in the wettability characteristics of an alumina bioceramic occasioned by high power diode laser (HPDL) surface treatment were apparent from the observed reduction in the contact angle. Such changes were due to the HPDL bringing about reductions the surface roughness, increases in the surface O2 content and increases in the polar component of the surface energy. Additionally, HPDL treatment of the alumina bioceramic surface was found to effect an improvement in the bonding characteristics by increasing the work of adhesion. An electronic approach was used to elucidate the bonding characteristics of the alumina bioceramic before and after HPDL treatment. It is postulated that HPDL induced changes to the alumina bioceramic produced a surface with a reduced bandgap energy which consequently increased the work of adhesion by increasing the electron transfer at the metal/oxide interface and thus the metal-oxide interactions. Furthermore, it is suggested that the increase in the work of adhesion of the alumina bioceramic after HPDL treatment was due to a correlation existing between the wettability and ionicity of the alumina bioceramic; for it is believed that the HPDL treated surface is less ionic in nature than the untreated surface and therefore exhibits better wettability characteristics