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Rheological, Chemical and Mechanical Properties of Cementitious Materials with Nanoclays and Diutan Gum
Cement has three sequential states in most applications: fluid, setting and hardened. This thesis focuses on the effect of nanoclays and diutan gum on rheological, chemical and mechanical properties corresponding to the three states.
Water transport properties are critically important in many applications, such as oil well cementing and 3D concrete printing. The effect of nanoclays and diutan gum on water transport properties of cement pastes were investigated. Bleeding, water retention under suction pressure, and evaporation under air flow were measured. The nanoclay was found to reduce bleeding but had no effect on water retention or evaporation. The diutan gum was found to reduce bleeding, improve water retention, and decrease evaporation loss. The rheological properties of the pastes and their interstitial solution were also characterized to resolve the mechanisms underlying the water transport behaviors. Good correlation between the measured rheological parameters and water transport properties was found.
In addition to water retention, the static yield stress build-up plays a major role in the successful oil well cementing and 3D concrete printing. Linear models are commonly used to describe the early structural build-up of cement-based materials. However, some studies have shown that there exists a faster non-linear phase before the linear phase. A simple non-linear thixotropy model is presented to describe the structural build-up process. It was quantified using static yield stress and storage modulus, which are measured through the stress growth protocol and small amplitude oscillatory shear (SAOS) tests, respectively. The effect of pre-shear, rest condition and nanoclay and diutan gum on the build-up behavior are studied. The results showed distinctly different trends between static yield stress and storage modulus. This may be attributed to the two different structures of fresh cement pastes, i.e. floc structures and C-S-H structures, measured by the stress growth protocol and SAOS test, respectively.
Phase characterization of cement paste was performed through synchrotron x-ray diffraction technique. This allowed for real-time, in-situ measurements of x-ray diffraction patterns to be obtained, and subsequently the continuous formation and decomposition of select phases over time (up to 8 hours). Phases of interest included alite, ferrite, portlandite, ettringite, monosulfate, and jaffeite (crystalline form of calcium silicate hydrate). The effects of elevated temperatures at elevated pressure, as well as the effect of nanomaterial addition were investigated. Rate of conversion of ettringite to monosulfate increased with increasing temperature, and monosulfate became unstable when temperatures reached 85ºC. The synchrotron x-ray diffraction setup appeared to have captured the seeding effect of nano-sized attapulgite clays at 0.5% addition by mass of cement, where acceleration in the rate of formation of portlandite and jaffeite was observed.
Finally, the investigated system was upscaled from cement paste to cement mortar incorporating the fly ash and the slag. The effect of the nanoclays on the mechanical properties was evaluated in comparison with the carbon nanotube. Compressive strength and tensile strength were evaluated. Results indicated that although the nanoclays are utilized primarily as a rheological modifier, they can also enhance mechanical properties
Exploiting Image Local And Nonlocal Consistency For Mixed Gaussian-Impulse Noise Removal
Most existing image denoising algorithms can only deal with a single type of
noise, which violates the fact that the noisy observed images in practice are
often suffered from more than one type of noise during the process of
acquisition and transmission. In this paper, we propose a new variational
algorithm for mixed Gaussian-impulse noise removal by exploiting image local
consistency and nonlocal consistency simultaneously. Specifically, the local
consistency is measured by a hyper-Laplace prior, enforcing the local
smoothness of images, while the nonlocal consistency is measured by
three-dimensional sparsity of similar blocks, enforcing the nonlocal
self-similarity of natural images. Moreover, a Split-Bregman based technique is
developed to solve the above optimization problem efficiently. Extensive
experiments for mixed Gaussian plus impulse noise show that significant
performance improvements over the current state-of-the-art schemes have been
achieved, which substantiates the effectiveness of the proposed algorithm.Comment: 6 pages, 4 figures, 3 tables, to be published at IEEE Int. Conf. on
Multimedia & Expo (ICME) 201
Image Restoration Using Joint Statistical Modeling in Space-Transform Domain
This paper presents a novel strategy for high-fidelity image restoration by
characterizing both local smoothness and nonlocal self-similarity of natural
images in a unified statistical manner. The main contributions are three-folds.
First, from the perspective of image statistics, a joint statistical modeling
(JSM) in an adaptive hybrid space-transform domain is established, which offers
a powerful mechanism of combining local smoothness and nonlocal self-similarity
simultaneously to ensure a more reliable and robust estimation. Second, a new
form of minimization functional for solving image inverse problem is formulated
using JSM under regularization-based framework. Finally, in order to make JSM
tractable and robust, a new Split-Bregman based algorithm is developed to
efficiently solve the above severely underdetermined inverse problem associated
with theoretical proof of convergence. Extensive experiments on image
inpainting, image deblurring and mixed Gaussian plus salt-and-pepper noise
removal applications verify the effectiveness of the proposed algorithm.Comment: 14 pages, 18 figures, 7 Tables, to be published in IEEE Transactions
on Circuits System and Video Technology (TCSVT). High resolution pdf version
and Code can be found at: http://idm.pku.edu.cn/staff/zhangjian/IRJSM
Improved Total Variation based Image Compressive Sensing Recovery by Nonlocal Regularization
Recently, total variation (TV) based minimization algorithms have achieved
great success in compressive sensing (CS) recovery for natural images due to
its virtue of preserving edges. However, the use of TV is not able to recover
the fine details and textures, and often suffers from undesirable staircase
artifact. To reduce these effects, this letter presents an improved TV based
image CS recovery algorithm by introducing a new nonlocal regularization
constraint into CS optimization problem. The nonlocal regularization is built
on the well known nonlocal means (NLM) filtering and takes advantage of
self-similarity in images, which helps to suppress the staircase effect and
restore the fine details. Furthermore, an efficient augmented Lagrangian based
algorithm is developed to solve the above combined TV and nonlocal
regularization constrained problem. Experimental results demonstrate that the
proposed algorithm achieves significant performance improvements over the
state-of-the-art TV based algorithm in both PSNR and visual perception.Comment: 4 Pages, 1 figures, 3 tables, to be published at IEEE Int. Symposium
of Circuits and Systems (ISCAS) 201
Image Super-Resolution via Dual-Dictionary Learning And Sparse Representation
Learning-based image super-resolution aims to reconstruct high-frequency (HF)
details from the prior model trained by a set of high- and low-resolution image
patches. In this paper, HF to be estimated is considered as a combination of
two components: main high-frequency (MHF) and residual high-frequency (RHF),
and we propose a novel image super-resolution method via dual-dictionary
learning and sparse representation, which consists of the main dictionary
learning and the residual dictionary learning, to recover MHF and RHF
respectively. Extensive experimental results on test images validate that by
employing the proposed two-layer progressive scheme, more image details can be
recovered and much better results can be achieved than the state-of-the-art
algorithms in terms of both PSNR and visual perception.Comment: 4 pages, 4 figures, 1 table, to be published at IEEE Int. Symposium
of Circuits and Systems (ISCAS) 201
High Quality Image Interpolation via Local Autoregressive and Nonlocal 3-D Sparse Regularization
In this paper, we propose a novel image interpolation algorithm, which is
formulated via combining both the local autoregressive (AR) model and the
nonlocal adaptive 3-D sparse model as regularized constraints under the
regularization framework. Estimating the high-resolution image by the local AR
regularization is different from these conventional AR models, which weighted
calculates the interpolation coefficients without considering the rough
structural similarity between the low-resolution (LR) and high-resolution (HR)
images. Then the nonlocal adaptive 3-D sparse model is formulated to regularize
the interpolated HR image, which provides a way to modify these pixels with the
problem of numerical stability caused by AR model. In addition, a new
Split-Bregman based iterative algorithm is developed to solve the above
optimization problem iteratively. Experiment results demonstrate that the
proposed algorithm achieves significant performance improvements over the
traditional algorithms in terms of both objective quality and visual perceptionComment: 4 pages, 5 figures, 2 tables, to be published at IEEE Visual
Communications and Image Processing (VCIP) 201
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