Low velocity impact response of rc beam with artificial polyethylene aggregate as concrete block infill

In structural design, an ideal situation for saving materials would be to reduce the weight of the structure without having to compromise on its strength and serviceability. A new lightweight composite reinforced concrete section was developed with a novel use of a lightweight concrete block as infill utilizing Artificial Polyethylene Aggregate (APEA and MAPEA). The concrete near the neutral axis acts as a stress transfer medium between the compression and tension zones. Partial replacement of the concrete near the neutral axis could create a reduction in weight and savings in the use of materials. In this experimental work, APEA and MAPEA were utilized as replacement for normal aggregates (NA) at percentages of 0%, 3%, 6%, and 9%, 12%, and 100% in the concrete mix. In this study, the concrete block infill uses the 100% MAPEA as a replacement for coarse aggregate. A total of sixteen beams were prepared measuring 170 mm × 250 mm × 1000 mm, in which four specimens were used as control samples (NRC) and twelve specimens were the reinforced concrete beam incorporated with different size of concrete block infill (RCAI) consisting of 100% MAPEA. All beams were tested with 100 kg steel weight dropped vertically from a height of 0.6 m and 1.54 m, which was equivalent to 3.5 m/s and 5.5 m/s respectively. Based on the experimental results, the impact force, displacement and crack patterns were affected by the impact load. For RCAI specimens, the impact force was larger but smaller displacement value was observed, compared to the NRC specimens. Furthermore, the width of the cracks generated in the RCAI specimens near the mid-span was less than that on the NRC specimen. All experiment results were validated against FEM. The transient impact force histories, displacement and crack patterns obtained from FEM matched reasonably well with the experiment results. The error reported a range of 1% to 15%. The results showed that the proposed use of concrete block infill produced desirable results under the impact loads. The main advantages of the concrete block infill that utilized MAPEA from waste plastic bags due to the weight reduction about 6% in the concrete beams

Conformal mapping of unbounded multiply connected regions onto canonical slit regions

We present a boundary integral equation method for conformal mapping of unbounded multiply connected regions onto five types of canonical slit regions. For each canonical region, three linear boundary integral equations are constructed from a boundary relationship satisfied by an analytic function on an unboundedmultiply connected region. The integral equations are uniquely solvable. The kernels involved in these integral equations are the modified Neumann kernels and the adjoint generalized Neumann kernels

Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad-hoc methods are often used.In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically-weighted ensemble of convolutional neural networks, we show that a weighted ensemble of classifiers using a genetic algorithm yields in a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment.Comment: arXiv admin note: substantial text overlap with arXiv:1706.0949

Modeling and Simulation of Regenerative Braking Energy in DC Electric Rail Systems

Regenerative braking energy is the energy produced by a train during deceleration. When a train decelerates, the motors act as generators and produce electricity. This energy can be fed back to the third rail and consumed by other trains accelerating nearby. If there are no nearby trains, this energy is dumped as heat to avoid over voltage. Regenerative braking energy can be saved by installing energy storage systems (ESS) and reused later when it is needed. To find a suitable design, size and placement of energy storage, a good understanding of this energy is required. The aim of this paper is to model and simulate regenerative braking energy. The dc electric rail transit system model introduced in this paper includes trains, substations and rail systems