PROPORTIONING AND PROPERTIES OF ULTRA-HIGH PERFORMANCE CONCRETE MIXTURES FOR APPLICATION IN SHEAR KEYS OF PRECAST CONCRETE BRIDGES

Ultra-high performance concrete (UHPC) is defined as a cementitious based composite material with compressive strengths above 150 MPa, pre-and post-cracking tensile strengths above 5 MPa, and enhanced durability. To achieve desired properties, UHPC is typically produced with low water-cementitious materials (w/cm) ratio (i.e. w/cm \u3c 0.25), high cementitious materials content (i.e. \u3e1000 kg/m3), high quality aggregate, high dosage of high-range water reducing admixture (HRWRA) and reinforcing fibers. UHPC has distinct advantages in applications where narrow formwork and dense reinforcement are inevitable, high compressive strength concrete material is required and the surrounding environment is aggressive. The construction of shear keys in precast bridges is one of the important applications for UHPC. Although many previous research studies have focused on developing UHPC using a range of materials, evaluating UHPC properties and exploring different application for UHPC, the choice of commercial UHPC mixture is very limited, proprietary and expensive. This hinders the widespread use of UHPC in construction. The principal objective of this study was to investigate the feasibility of developing UHPC using locally available materials to achieve desirable properties for application in construction of shear keys in precast bridges. This study was carried out in three parts. In the first part of the study, each of the component materials including portland cement, high range water reducing admixtures, supplementary cementitious materials (SCM), sand and reinforcing fibers was studied, focusing on their influence on the properties of UHPC under different proportions. The specific aspects of the component materials studied include different types of high range water reducing admixtures, sand characteristics, alkali content of portland cement, different types of supplementary cementitious materials, different types of reinforcing fibers and the interaction of sand and fibers on the segregation of steel fibers in UHPC matrix. The investigated properties of UHPC included mixing time to achieve fluid mixture, workability, setting time, autogenous shrinkage, compressive strength, tensile/flexural strength, drying shrinkage, rapid chloride permeability, volume of permeable voids, alkali silica reaction and bulk electrical resistivity. Techniques such as thermogravimetric analysis, loss-on-ignition (LOI) and scanning electron microscopy were used to identify and explain the material behavior of UHPC. Test results from the first part of the study showed that a w/cm of 0.20 was low enough to produce high quality UHPC mixtures. Low alkali (\u3c 0.7% Na2Oeq) portland cement was found to be better suited for UHPC than high alkali cement as the latter resulted in reduction in the workability, compressive strength, and increase in the drying shrinkage. A powder form Poly-carboxylate ether-based HRWRA, such as Melflux® 4930F, was found to be suitable to produce a self-consolidating UHPC at very low w/cm. A low carbon (low LOI) silica fume was found to be the ideal SCM compared to fly ash and meta-kaolin from the consideration of improving the compressive strength and durability of UHPC. Silica flour was not a necessary component in UHPC, as its only beneficial effect was to improve the early age compressive strength of UHPC. The ternary use of meta-kaolin, fly ash and cement could overcome the reduction in the 1-day compressive strength and the increase in the drying shrinkage due to the binary use of fly ash and cement, and address the reduction in the workability and the increase in mixing time due to the binary use of meta-kaolin and cement. Properly proportioned ternary blend of meta-kaolin, fly ash and cement could produce cementitious paste suitable for use in UHPC with higher workability, higher 28-day compressive strength and lower drying shrinkage than a paste containing binary blend of silica fume and cement particularly when high SCM contents (0.3 and 0.4 by mass of cement) was used. An optimal proportion of fly ash and meta-kaolin was identified by using the desirability functions from the considerations of workability, compressive strength, drying shrinkage and SCM content. Natural siliceous sand with its natural gradation meeting ASTM C33 requirements was suitable for producing UHPC. The increase in the sand content decreased the workability, drying shrinkage and chloride ion permeability of mortar. It also reduced the cost of UHPC. Steel micro fibers (SMF) performed better than polyvinyl alcohol micro fibers (PVAMF) in UHPC formulation, as they could significantly improve the post-crack tensile strength of hardened UHPC and resulted in less reduction in the workability of fresh UHPC than PVAMF. Certain minimum sand content (i.e. sand-to-cementitious materials ratio of 1.25 by mass) was required to prevent severe segregation of SMF in UHPC. A strong correlation between the bulk electrical resistivity and rapid chloride ion permeability of UHPC was found in this investigation. This indicated that the chloride ion permeability results obtained from the rapid chloride ion permeability method was affected by the bulk electrical resistivity of the specimen. In the second part of the study, selected component materials and their proportions were used to produce UHPC mixtures, based on the results in the first part of the study. Chemical admixtures were used to further improve the properties of UHPC. The test results showed that several UHPC mixtures were developed by adding sand and SMF at certain proportion into selected cementitious paste formulations (containing silica fume or containing meta-kaolin and fly ash). The drying shrinkage of UHPC could be further reduced and without significantly sacrificing the 1-day compressive strength by combined use of a liquid form shrinkage reducing admixture and a chemical accelerator. In the third part of the study, the influence of substrate surface roughness, surface moisture condition, surface cleanliness and surface roughening pattern on the bond performance between UHPC and precast concrete was investigated. The test results showed that third-point flexural bond test was an easy and reliable method of evaluating the bond performance between UHPC and precast concrete, compared to the slant shear and pull-off test methods. The roughness of the substrate surface of precast concrete prepared by sandblasting could be evaluated by both sand spread test and laser profiling. The increase in the roughening duration increased the surface roughness. Adequate bond between UHPC and precast concrete was achieved as long as the substrate surface of precast concrete was well roughened and cleaned. The influence of surface moisture condition (i.e. saturated surface dry and ambient dry) of roughened precast concrete on the bond performance with UHPC was not significant. Moreover, an adequate bond between UHPC and precast concrete could be achieved by partly roughening the substrate surface of precast concrete in the tensile stress zone, instead of roughening the entire substrate surface. In conclusion, this dissertation showed that UHPC with desirable material properties could be manufactured by using locally available materials. The UHPC mixtures developed in this study exhibited adequate bond with precast concrete, which was expected to have successful structural performance for the construction of shear keys in precast bridges

Generative Image Dynamics

We present an approach to modeling an image-space prior on scene dynamics. Our prior is learned from a collection of motion trajectories extracted from real video sequences containing natural, oscillating motion such as trees, flowers, candles, and clothes blowing in the wind. Given a single image, our trained model uses a frequency-coordinated diffusion sampling process to predict a per-pixel long-term motion representation in the Fourier domain, which we call a neural stochastic motion texture. This representation can be converted into dense motion trajectories that span an entire video. Along with an image-based rendering module, these trajectories can be used for a number of downstream applications, such as turning still images into seamlessly looping dynamic videos, or allowing users to realistically interact with objects in real pictures.Comment: Project website: http://generative-dynamics.github.i

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AbstractThe flow field for different cover ratios within a three-level conical ring concentrator of a centrally-fuel-rich swirl coal combustion burner has been studied both experimentally and numerically. A particle dynamics anemometer measurement system was employed in the study to measure velocity and particle volume flux after the outlet of third-level ring. And the numerical simulations were used to calculate the flow field in the conical ring region. In each cross-section, after the outlet of third-level ring, concentration ratio for each cover ratio is always larger than 2. With conical ring concentrator in the primary air tube, the coal concentration can be concentrated to a suitable range. In the cross-sections 0.5<x/D<4.0, as cover ratio increases, concentration ratio decreases and resistance coefficient increases

InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images

We present a method for learning to generate unbounded flythrough videos of natural scenes starting from a single view, where this capability is learned from a collection of single photographs, without requiring camera poses or even multiple views of each scene. To achieve this, we propose a novel self-supervised view generation training paradigm, where we sample and rendering virtual camera trajectories, including cyclic ones, allowing our model to learn stable view generation from a collection of single views. At test time, despite never seeing a video during training, our approach can take a single image and generate long camera trajectories comprised of hundreds of new views with realistic and diverse content. We compare our approach with recent state-of-the-art supervised view generation methods that require posed multi-view videos and demonstrate superior performance and synthesis quality.Comment: ECCV 2022 (Oral Presentation

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We address the problem of synthesizing novel views from a monocular video depicting a complex dynamic scene. State-of-the-art methods based on temporally varying Neural Radiance Fields (aka dynamic NeRFs) have shown impressive results on this task. However, for long videos with complex object motions and uncontrolled camera trajectories, these methods can produce blurry or inaccurate renderings, hampering their use in real-world applications. Instead of encoding the entire dynamic scene within the weights of an MLP, we present a new approach that addresses these limitations by adopting a volumetric image-based rendering framework that synthesizes new viewpoints by aggregating features from nearby views in a scene-motion-aware manner. Our system retains the advantages of prior methods in its ability to model complex scenes and view-dependent effects, but also enables synthesizing photo-realistic novel views from long videos featuring complex scene dynamics with unconstrained camera trajectories. We demonstrate significant improvements over state-of-the-art methods on dynamic scene datasets, and also apply our approach to in-the-wild videos with challenging camera and object motion, where prior methods fail to produce high-quality renderings. Our project webpage is at dynibar.github.io.Comment: Project page: dynibar.github.i