Vipulanandan models to predict the electrical resistivity, rheological properties and compressive stress-strain behavior of oil well cement modified with silica nanoparticles

Mohammed, Ahmed Salih

doi:10.1016/j.ejpe.2018.07.001

Cited by 64 publications

(14 citation statements)

References 26 publications

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“…To propose a NLR model, Eq 3 could be considered as a general form [ 99 , 100 ]. The interrelation between different variables in Eqs 1 and 2 can be represented in Eq 3 to predict the compression strength of FA-GPC mixtures.…”

Section: Modelingmentioning

confidence: 99%

Systematic multiscale models to predict the compressive strength of fly ash-based geopolymer concrete at various mixture proportions and curing regimes

Ahmed

Mohammed

et al. 2021

PLoS ONE

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Geopolymer concrete is an inorganic concrete that uses industrial or agro by-product ashes as the main binder instead of ordinary Portland cement; this leads to the geopolymer concrete being an eco-efficient and environmentally friendly construction material. A variety of ashes used as the binder in geopolymer concrete such as fly ash, ground granulated blast furnace slag, rice husk ash, metakaolin ash, and Palm oil fuel ash, fly ash was commonly consumed to prepare geopolymer concrete composites. The most important mechanical property for all types of concrete composites, including geopolymer concrete, is the compressive strength. However, in the structural design and construction field, the compressive strength of the concrete at 28 days is essential. Therefore, achieving an authoritative model for predicting the compressive strength of geopolymer concrete is necessary regarding saving time, energy, and cost-effectiveness. It gives guidance regarding scheduling the construction process and removal of formworks. In this study, Linear (LR), Non-Linear (NLR), and Multi-logistic (MLR) regression models were used to develop the predictive models for estimating the compressive strength of fly ash-based geopolymer concrete (FA-GPC). In this regard, a comprehensive dataset consists of 510 samples were collected in several academic research studies and analyzed to develop the models. In the modeling process, for the first time, twelve effective variable parameters on the compressive strength of the FA-GPC, including SiO2/Al2O3 (Si/Al) of fly ash binder, alkaline liquid to binder ratio (l/b), fly ash (FA) content, fine aggregate (F) content, coarse aggregate (C) content, sodium hydroxide (SH)content, sodium silicate (SS) content, (SS/SH), molarity (M), curing temperature (T), curing duration inside ovens (CD) and specimen ages (A) were considered as the modeling input parameters. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the Coefficient of determination (R2) were used to evaluate the efficiency of the developed models. The results indicated that the NLR model performed better for predicting the compressive strength of FA-GPC mixtures compared to the other models. Moreover, the sensitivity analysis demonstrated that the curing temperature, alkaline liquid to binder ratio, and sodium silicate content are the most affecting parameter for estimating the compressive strength of the FA-GPC.

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Section: Modelingmentioning

confidence: 99%

Systematic multiscale models to predict the compressive strength of fly ash-based geopolymer concrete at various mixture proportions and curing regimes

Ahmed

Mohammed

et al. 2021

PLoS ONE

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“…The use of nanosilica in cement resulting from the Stober method (a variant of the sol-gel method) improves the properties of hardened cement. Due to the extremely small size, high sphericity and relatively high quality, nanoparticles are preferred because their abrasive action is negligible with a lower impact of kinetic energy [1,9]. When nanosilica is used, the mechanism of its action is multifaceted.…”

Section: Nanosilica In Cement Slurriesmentioning

confidence: 99%

“…Drilling deeper and deeper creates more and more difficult conditions for the cement slurry and increases the requirements [5,6]. Cement slurries must be able to cope with them, therefore it implies the need to use advanced, innovative measures that will improve the functional parameters of cement slurry and cement stone [7][8][9][10]. Such activities allow for obtaining appropriate zone insulation.…”

Section: Introductionmentioning

confidence: 99%

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Influence of the Addition of Silica Nanoparticles on the Compressive Strength of Cement Slurries under Elevated Temperature Condition

2021

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Drilling ever deeper, and thus in increasingly difficult conditions, is associated with restrictive requirements that must be met by cement slurries. This implies the need to use advanced, innovative measures that will significantly improve the performance parameters of the cement slurry and cement stone. Due to its unique properties, an admixture of nanosilica improves the properties of the cement stone and allows for appropriate zone insulation. The article presents the results of strength tests of cement stone samples with the addition of silica nanoparticles deposited in an environment of increased temperature of 90 °C. In all three cases of modification with an admixture of nanosilica (type 1, 2 and 3, concentration 0.5%, 1% and 5%), the cement stone shows an improvement in mechanical properties, which is manifested by an increase in compressive strength. The most homogeneous results of strength measurements are for cement slurries with an admixture of type 3 nanosilica (the highest average strength: 132–149% in relation to the base sample). They show the smallest stretch marks and deviations from the average. The highest average increase in strength is for the sample with the addition of 1% nanosilica (on average 124% in relation to the base sample). This amount causes the greatest increase in strength with no significant deterioration of rheological parameters.

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“…There are several methods for modeling the properties of materials, including computational modeling, statistical techniques, and recently developed tools such as regression analyses and Artificial Neural Networks (ANN) [25,26]. Multilinear regression analysis, M5P-tree, and ANN are techniques widely used to solve problems in construction project applications [27][28][29][30][31][32][33][34][35][36][37][38]. The most important characteristics of ANN is the ability to learn directly from examples and the great response to imperfect tasks.…”

Section: Introductionmentioning

confidence: 99%

Soft Computing and Multi-Scal Model Technics to Predict the Early Age Compression Strength of Cement Paste as a Function of Polymer Contents, Water-to-Cement-Ratio, and Curing Ages

Mohammed

Ghafor

Mahmood

et al. 2021

Preprint

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In this study, the effect of two water reducer polymers with smooth and rough surfaces on the compression strength of Ordinary Portland cement (OPC) was investigated. Three different initial ratios between water and cement (w/c) 0.5, 0.6, and 1 were used in this study. The amount of polymer contents varied from 0 to 0.06 % (%wt) for the cement paste with initial w/c of 0.5 and the polymer contents ranged between 0 to 0.16% (%wt) for the cement paste with initial w/c of 0.6 and 1 were investigated. SEM test was conducted to identify the impact of polymers on the behavior of cement paste. The compression strength of OPC cement was increased significantly with increasing the polymer contents. Because of a fiber net (netting) around cement paste particle was developed when the polymers were added to the cement paste which leads to decrease the void between the particles, binding the cement particles, therefore, increased the viscosity and compression strength of the cement rapidly. In this analysis, the hardness of cement paste with polymer contents has been evaluated and modeled using four different model technics. More environmentally sustainable construction, and lower cost than conventional building materials and early age strengths of the cement. To overcome the mentioned matter, this study aims to establish systematic multiscale models to predict the compression strength of cement paste containing polymers and to be used by the construction industry with no theoretical restrictions. For that purpose, a wide data a total of 280 tested cement paste modified with polymers, has been conducted, analyzed, and modeled. Linear, Nonlinear regression, M5P-tree, and Artificial Neural Network (ANN) technical approaches were used for the qualifications. In the modeling process, the most relevant parameters affecting the strength of cement paste, i.e. polymer incorporation ratio (0-0.16% of cement's mass), water-to-cement ratio (0.5-1), and curing ages (1 to 28 days). According to the correlation coefficient (R), mean absolute error and the root means a square error, the compression strength of cement paste can be well predicted in terms of w/c, polymer content, and curing time using four various simulation techniques. Among the used approaches and based on the training data set, the model made based on the Non-linear regression, ANN, and M5P-tree models seem to be the most reliable models. The sensitivity investigation concludes that the polymer content is the most dominating parameter for the prediction of the compression strength of cement paste with this data set.

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Vipulanandan models to predict the electrical resistivity, rheological properties and compressive stress-strain behavior of oil well cement modified with silica nanoparticles

Cited by 64 publications

References 26 publications

Systematic multiscale models to predict the compressive strength of fly ash-based geopolymer concrete at various mixture proportions and curing regimes

Systematic multiscale models to predict the compressive strength of fly ash-based geopolymer concrete at various mixture proportions and curing regimes

Influence of the Addition of Silica Nanoparticles on the Compressive Strength of Cement Slurries under Elevated Temperature Condition

Soft Computing and Multi-Scal Model Technics to Predict the Early Age Compression Strength of Cement Paste as a Function of Polymer Contents, Water-to-Cement-Ratio, and Curing Ages

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