•There is no noticeable change in the compressive strength and elastic modulus of UHPFRC compared to those of UHPC in this study.•Steel fibers with higher aspect ratio result in a better improvement ...of post-peak behavior in compression.•The formulae for estimating the axial strain at the peak stress and the elastic modulus were proposed.•The fiber activation stage of the stress-crack opening relationship in tension is greatly affected by steel fiber content and aspect ratio.•A relationship between the fiber efficiency σcf0 and the fiber factor K was established.
This study investigates the effect of steel fiber contents of 1.5% and 3% with different aspect ratios on the uniaxial tensile and compressive behavior of ultra high performance concrete (UHPC). Compressive tests on concrete cylinders of 150mm×300mm and direct tension tests on notched prisms of 40mm×40mm×80mm were conducted. The test results indicated that there is no noticeable change in the compressive strength and elastic modulus with incorporation of steel fibers, however the post-peak behavior under compression is substantially affected by steel fiber content and aspect ratio. In terms of notched prisms under tension, there is no influence of steel fiber in the linear elastic stage, whereas the increase in steel fiber content results in not only a significant effect on the fiber activation stage but also higher values of fiber efficiency. Furthermore, the strain at the peak stress and the elastic modulus obtained from compressive tests were also evaluated by the comparison with some previous formulae. Finally, a relationship between the fiber efficiency σcf0 and the fiber factor K was proposed.
In general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance ...(albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neural network (ShadingNet) is proposed that operates in a fine-to-coarse manner with a specialized fusion and refinement unit exploiting the fine-grained shading model. It is designed to learn specific reflectance cues separated from specific photometric effects to analyze the disentanglement capability. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with fine-grained intrinsic image ground-truths. Large scale experiments show that our approach using fine-grained shading decompositions outperforms state-of-the-art algorithms utilizing unified shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD datasets.
We use nationally representative data from the UK Time-Use Survey 2014/2015 to investigate how a person’s employment status is related to time use and cognitive and affective dimensions of subjective ...well-being. We do not find clear indications that employed and unemployed persons experience different average levels of emotional well-being when they engage in the same kinds of activities. For the employed, working belongs to one of the least enjoyable activities of their day. They also spend a large share of their time at work and on work-related activities. The unemployed, instead, spend more time on leisure and more enjoyable activities. When looking at duration-weighted average affective well-being over the entire waking time of the day, the unemployed experience, on average, more enjoyment than the employed. For the employed, the more hours they have to work on a specific day, the lower the average enjoyment they experience on that day. Differentiating the analyses by weekdays and weekends supports the finding that being able to freely allocate one’s non-work time is associated with higher levels of affective well-being. In line with previous studies on cognitive well-being, we find that the unemployed report substantially lower levels of life satisfaction than the employed.
For applications in automotive, aviation and renewable energy industries temperature and power requirements have been significantly increased for electronic components. In particular, capacitors have ...been identified as the most critical materials considering the fulfillment of these requirements. Ceramics are the most promising materials for high temperature capacitors but no ceramic has been able to meet the necessary electrical properties so far. In this work, Na1/2Bi1/2TiO3 (NBT) solid solutions are investigated to optimize the respective electrical properties. A reduction of bismuth vacancy and oxygen vacancy concentration by increasing the initial Bi content leads to a significant decrease in dielectric loss. Additionally, energy efficiencies of up to 97% can be achieved for the composition Na1/2Bi1/2O3–BaTiO3–CaZrO3 (NBT–BT–CZ) and the temperature range of stable high permittivity together with low dielectric loss (tan δ ≤ 0.02) extends from −67 °C to 362 °C. Hence, optimization of the defect chemistry of NBT-materials results in highly stable electrical properties over a large temperature and electric field range, which leads to the fulfillment of industrial requirements.
Most of the traditional work on intrinsic image decomposition rely on deriving priors about scene characteristics. On the other hand, recent research use deep learning models as in-and-out black box ...and do not consider the well-established, traditional image formation process as the basis of their intrinsic learning process. As a consequence, although current deep learning approaches show superior performance when considering quantitative benchmark results, traditional approaches are still dominant in achieving high qualitative results. In this paper, the aim is to exploit the best of the two worlds. A method is proposed that (1) is empowered by deep learning capabilities, (2) considers a physics-based reflection model to steer the learning process, and (3) exploits the traditional approach to obtain intrinsic images by exploiting reflectance and shading gradient information. The proposed model is fast to compute and allows for the integration of all intrinsic components. To train the new model, an object centered large-scale datasets with intrinsic ground-truth images are created. The evaluation results demonstrate that the new model outperforms existing methods. Visual inspection shows that the image formation loss function augments color reproduction and the use of gradient information produces sharper edges. Datasets, models and higher resolution images are available at https://ivi.fnwi.uva.nl/cv/retinet.
Solid solutions of sodium bismuth titanate (NBT) belong to the most performant lead-free dielectric ceramics for energy storage. However, the defect chemistry of NBT is very complex, and acceptor ...doping can lead to an unexpected and extraordinarily high oxygen ionic conductivity. This can be attributed to a non-linear change in the formation of defect associates between acceptor and oxygen vacancy with increasing acceptor doping. Using different acceptor dopants with varying concentrations, we elucidate the interaction between acceptors and oxygen vacancies in this work. With the help of total energy calculations based on density functional theory and molecular dynamics simulations, the experimentally observed differences in conductivity can be rationalized.
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Despite the popularity of deep neural networks in various domains, the extraction of digital terrain models (DTMs) from airborne laser scanning (ALS) point clouds is still challenging. This might be ...due to the lack of the dedicated large-scale annotated dataset and the data-structure discrepancy between point clouds and DTMs. To promote data-driven DTM extraction, this article collects from open sources a large-scale dataset of ALS point clouds and corresponding DTMs with various urban, forested, and mountainous scenes. A baseline method is proposed as the first attempt to train a deep neural network to extract DTMs directly from ALS point clouds via rasterization techniques, coined DeepTerRa. Extensive studies with well-established methods are performed to benchmark the dataset and analyze the challenges in learning to extract DTM from point clouds. The experimental results show the interest of the agnostic data-driven approach, with submetric error level compared to methods designed for DTM extraction. The data and source code are available online at https://lhoangan.github.io/deepterra/ for reproducibility and further similar research.
The axially compressive behavior of Steel Tube Confined Concrete (STCC) columns has been experimentally investigated by many researchers throughout the world. However, it is extremely complicated to ...measure the stresses of steel tubes and concrete core in real tests. Therefore, to investigate the fundamental behavior of STCC columns under axial compression, this paper presents a numerical study that explores the stress distribution in steel tubes and concrete core. The circular STCC columns with the use of Normal Strength Concrete (NSC), High Strength Concrete (HSC), and Ultra-High Strength Concrete (UHSC) were simulated in a Finite Element Model (FEM) in ABAQUS. The material model for confined concrete incorporating a wide range of concrete strength values was developed in the simulation. The obtained from FEM curves of load versus strain of circular STCC columns were compared with those measured in real tests to verify the accurateness of the FEM. Deriving from the results of FEM, the stress states and their distribution in outer steel tubes and concrete core along the column height were described. Also, the longitudinal stresses on the cross-section of the concrete core were calculated corresponding with the load stage to quantify the strength enhancement of the concrete core due to the confinement effect from the steel tube. Furthermore, the confining pressure provided by the outer steel tube and impacting on the concrete core was plotted. Based on the findings in this paper, the effect of various concrete strengths on the stress distribution in circular STCC columns was investigated.
This research presents a numerical investigation of circular Concrete Encased Steel Composite (CESC) columns. To simulate the circular CESC columns under axial compression in the previous tests, a ...Finite Element Model (FEM) with some modifications of material models for the steel and concrete was established in ABAQUS software. The curves of load versus longitudinal displacement and the ultimate loads obtained from the FEM were compared with those measured in previous tests. The numerical results agreed well with the test results. Furthermore, the distribution of the stresses on the cross-section at different heights and the effect of initial imperfections were observed by the FEM results. A highly confined concrete zone enclosed by steel web and steel flanges was observed. Finally, the established FEM was used in the parametric study that investigated the influence of concrete strength, steel yield strength, and spacing of the spiral hoops.
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