The effect of carbon nanotubes on the mechanical and damping properties of macro-defect-free cements

The effect of CNTs on the mechanical and damping properties of macro-defect-free (MDF) cements was studied, and polyvinyl alcohol (PVA) fibers were also studied as a contrast. It was found that the compressive strength of MDF cements was not significantly affected by the two types of fibers. The CNTs enhanced the flexural strength of MDF, while PVA fibers made negative contribution. The strengthening mechanism of flexural strength of MDF cements by CNTs can be summarized as fiber bridging, crack deflection and fiber slippage. For the damping properties, the proper contents of CNTs and PVA fibers improved the loss factor significantly. The interface transition zone (ITZ) between the PVA fibers and matrix was large, which was favorable for fiber slippage. The damping property of MDF cements with CNTs was mainly due to the slippage between the inner tubes of the CNTs rather than the slippage between the CNTs and matrix

Static mechanical properties and mechanism of C200 ultra-high performance concrete (UHPC) containing coarse aggregates

In this paper, C200 ultra-high performance concrete (UHPC) containing coarse aggregate was prepared. Firstly, four different maximum size and three different type of coarse aggregate having significant differences in strength, surface texture, porosity and absorption were used to prepared the mixtures. Secondly, the effect of maximum size and type of coarse aggregate on the workability of the fresh UHPC and the mechanical behaviour of harden UHPC were investigated. Finally, a series micro-tests including mercury intrusion porosimetry (MIP), scanning electron microscope (SEM), X-ray diffraction (XRD) were conducted and the mechanism of the C200 UHPC were discussed

A numerical Bayesian-calibrated characterization method for multiscale prepreg preforming simulations with tension-shear coupling

Carbon fiber reinforced plastics (CFRPs) are attracting growing attention in industry because of their enhanced properties. Preforming of thermoset carbon fiber prepregs is one of the most common production techniques of CFRPs. To simulate preforming, several computational methods have been developed. Most of these methods, however, obtain the material properties directly from experiments such as uniaxial tension and bias-extension where the coupling effect between tension and shear is not considered. Neglecting this coupling effect deteriorates the prediction accuracy of simulations. To address this issue, we develop a Bayesian model calibration and material characterization approach in a multiscale finite element preforming simulation framework that utilizes mesoscopic representative volume element (RVE) to account for the tension-shear coupling. A new geometric modeling technique is first proposed to generate the RVE corresponding to the close-packed uncured prepreg. This RVE model is then calibrated with a modular Bayesian approach to estimate the yarn properties, test its potential biases against the experiments, and fit a stress emulator. The predictive capability of this multiscale approach is further demonstrated by employing the stress emulator in the macroscale preforming simulation which shows that this approach can provide accurate predictions.Accepted Author Manuscript(OLD) MSE-

The Seventh Visual Object Tracking VOT2019 Challenge Results

The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOT-ST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2019 focused on long-term tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard short-term, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website(1).Funding Agencies|Slovenian research agencySlovenian Research Agency - Slovenia [J2-8175, P2-0214, P2-0094]; Czech Science Foundation Project GACR [P103/12/G084]; MURI project - MoD/DstlMURI; EPSRCEngineering &amp; Physical Sciences Research Council (EPSRC) [EP/N019415/1]; WASP; VR (ELLIIT, LAST, and NCNN); SSF (SymbiCloud); AIT Strategic Research Programme; Faculty of Computer Science, University of Ljubljana, Slovenia</p