Estimating the efficiency of the sandy soils-cement based grout interactions from Particle size distribution (PSD)
TL;DR: The particle size distribution of the soil fines content and mean particle size (d50) are used in a number of soil property relationships and in the soil classification as mentioned in this paper, which is used to analyze and model partic...
Abstract: The particle size distribution of the soil fines content and mean particle size (d50) are used in a number of soil property relationships and in the soil classification. To analyze and model partic...
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01 Dec 2019
TL;DR: In this paper, two types of polycarboxylate (PCE) polymer (DBC-21 and VK-98) were used as additives in the cement-based grout.
Abstract: In this study, two types of polycarboxylate (PCE) polymer (DBC-21 and VK-98) were used as additives in the cement-based grout. The water-cement ratio (w/c) was fixed to 0.6 and 1.0 at temperature of 25° Celsius and 50° Celsius. Experimentalists were conducted to study the chemical composition of the cement-based grout, the mass loss, the rheology behavior, and the compressive strength. Numerical studies were performed to understand the shear strength, rheological properties and compressive strength by taking advantages of numerical models. The results show that the 0.16% PCE polymer additive leads to low cement weight loss at 800° Celsius, drastic increase of apparent and plastic viscosity, and significant improvement of compressive strength. Effects of polymer content, w/c, curing period and the temperature on the rheological properties and compressive strength (CS) of cement-based grout were investigated using a multiple nonlinear regression analysis.
50 citations
TL;DR: In this paper, the effect of nano-silica (NS) as an additive to the Ordinary Portland Cement was evaluated and quantified using non-linear regression (NLR) based model.
46 citations
TL;DR: In this paper, the Vipulanandan fluid loss model was compared to the API model and it predicted both short-term and long-term fluid losses very well based on the root mean square error (RMSE).
42 citations
TL;DR: In this article, the effect of nano-silica (NS) as an additive to Ordinary Portland Cement was evaluated and quantified using a non-linear regression (NLR) based model.
Abstract: In this study, the effect of nano-silica (NS) as an additive to Ordinary Portland Cement was evaluated and quantified. Scanning electron microscopy (SEM), thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy analysis was used to identify the cement and NS contents. Experimental tests and modeling were conducted to quantify and predict the rheological properties of the cement in the liquid phase such as yield stress, maximum shear strength, plastic viscosity, and mechanical behavior such as compressive strength of cement after hardening. The cement modified with NS was tested at water-to-cement ratios (w/c) of 0.35 and 0.45 and temperatures ranging from 25 to 75 °C. X-ray diffraction (XRD) and TGA were used to analyze the cement, nano-silica, and cement modified with nano-silica. The behavior of cement paste in the liquid phase (slurry) and hardened phase modified with different percentages of nano-silica up to 1% (by dry weight of cement) was investigated. The compressive strength of cement paste modified with nano-silica was tested from a young age up to 28 days of curing. Non-linear regression (NLR) based model was used to assess the effect of nano-silica on the rheological properties and compressive strength of cement. Replacing the cement with nano-silica substantially reduced the volume of Ca(OH)2. TGA tests showed that the 1% nano-silica additive leads to low cement weight loss up to 800 °C due to the de-carbonation of CaCO3 in the hydrated compound and due to interacting the NS with the cement. The addition of NS increased the ultimate shear strength (τmax) and the yield stress (τo) by 15% to 53% and 23% to 186%, respectively based on the NS content, w/c, and temperature. An additional 1% of NS the compressive strength increased of the cement hardened by 15.1% to 72% based on the curing period, and w/c. Based on the model parameters and the experimental performance, the nano-silica is the most effective parameter in improving the properties of cement in both liquid and hardened phases.
36 citations
TL;DR: The sensitivity investigation concludes that the curing time is the most dominating parameter for the prediction of the maximum stress (compression strength) of concrete with this dataset.
Abstract: In this study, the effect of two water-reducer polymers with smooth and rough surfaces on the workability, and the compression strength of concrete from an early age (1 day) up to 28 days of curing was investigated. The polymer contents used in this study varied from 0 to 0.25% (wt%). The initial ratio between water and cement (
$$ \frac{w}{c} $$
) was 60%, and it slowly reduced to 0.46 by increasing the polymer contents. The compression strength of concrete was increased significantly with increasing the polymer contents by 24–95% depending on the polymer type, polymer content, $$ \frac{w}{c} $$
, and curing age. Because of a fiber net (netting) in the concrete when the polymers were added which leads to a decrease void between the particles, binding the cement particles, therefore, increased rapidly the viscosity for the fresh concrete and the compression strength of the hardened concrete. This study also aims to establish systematic multiscale models to predict the compression strength of concrete containing polymers and to be used by the construction projects with no theoretical restrictions. For that purpose, 88 concrete samples modified with two types of polymer (44 samples for each modification) has been tested, analyzed, and modeled. Linear, nonlinear regression, M5P-tree, and artificial neural network (ANN) approaches were used for the qualifications. In the modeling process, the most relevant parameters affect the strength of concrete, i.e., polymer incorporation ratio (0–0.25% of cement’s mass), water-to-cement ratio (0.46–0.6), and curing ages (1–28 days). Among the used approaches and based on the training data set, the model made based on the nonlinear regression, ANN, and M5P-tree models seem to be the most reliable models. The sensitivity investigation concludes that the curing time is the most dominating parameter for the prediction of the maximum stress (compression strength) of concrete with this dataset.
33 citations
References
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01 Jan 1981
TL;DR: In this paper, the authors present data on soil behaviour, with emphasis on practical and empirical knowledge required by geotechnical engineers for the design and construction of foundations and embankments.
Abstract: This manual presents data on soil behaviour, with emphasis on practical and empirical knowledge, required by geotechnical engineers for the design and construction of foundations and embankments It deals with: index and classification properties of soils; soil classification; clay minerals and soil structure; compaction; water in soils (capillarity, shrinkage, swelling, frost action, permeability, seepage, effective stress); consolidation and consolidation settlements; time rate of consolidation; the Mohr circle, failure theories, and stress paths; shear strength of sands and clays Four appendices deal with the following: application of the "SI" system of units to getechnical engineering; derivation of Laplace's equation; derivation and solution of Terzaghi's one-dimensional consolidation theory; pore pressure parameters (TRRL)
1,682 citations
401 citations
TL;DR: The grain size distribution is commonly used for soil classification; however, there is also potential to use the grain-size distribution as a basis for estimating soil behaviour as mentioned in this paper. But, this method is not suitable for the use of soil classification.
Abstract: The grain-size distribution is commonly used for soil classification; however, there is also potential to use the grain-size distribution as a basis for estimating soil behaviour. For example, much...
191 citations
TL;DR: In this article, acrylamide polymer was used to modify the water-based bentonite mud to reduce the yield point and maximum shear stress produced by the mud during the drilling operation.
127 citations
TL;DR: In this article, the authors evaluated the ability of seven models (i.e., five lognormal models, the Gompertz model, and the Fredlund model) to fit PSD data sets from a wide range of soil textures.
Abstract: An accurate mathematical representation of particle-size distributions (PSDs) is required to estimate soil hydraulic properties or to compare texture measurements from different classification systems. The objective of this study was to evaluate the ability of seven models (i.e., five lognormal models, the Gompertz model, and the Fredlund model) to fit PSD data sets from a wide range of soil textures. Special attention was given to the effect of texture on model performance. Several criteria were used to determine the optimum model with the least number of fitting parameters when other conditions are equal. The Fredlund model with four parameters showed the best performance with the majority of soils studied, even when three criteria that impose a penalty for additional fitting parameters were used. Especially, the relative performance of the Fredlund model in regard to other models increased with increase of clay content. Among all soil classes, the lognormal models with two or three parameters showed better fits for silty clay, silty clay loam, and silt loam soils, and worse fit for sandy clay loam soil.
126 citations