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