Riyadh Noaman

Bio: Riyadh Noaman is an academic researcher. The author has contributed to research in topics: Compressive strength & Cement. The author has an hindex of 4, co-authored 5 publications receiving 72 citations.

Microstructure characterizations, thermal properties, yield stress, plastic viscosity and compression strength of cement paste modified with nanosilica

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.

Reduction of computational error by optimizing SVR kernel coefficients to simulate concrete compressive strength through the use of a human learning optimization algorithm

Abstract: This research presents a new model for finding optimal conditions in the concrete technology area. To do that, results of a series of laboratory investigations on concrete samples were considered and used to design several artificial intelligence (AI) models. The data samples include 8 parameters i.e., silica fume replacement ratio, fly ash replacement ratio, fine aggregate, water content, high rate water reducing agent, coarse aggregate, total cementitious material, and age of samples, were used to predict and optimize the compressive strength of concrete samples. For optimization purposes, this study used a human learning optimization (HLO) algorithm to find the optimal results as well as optimizing the kernel coefficients of the support vector regression (SVR) models. Initially, to form the core of this research, various models were constructed and proposed to design the required relationship between the data using SVR. Since different SVR kernels have their own coefficients, using optimization theory, the probability of error in the models was reduced and the models were identified and executed with the highest accuracy. Finally, the polynomial model was selected as the model with the lowest computational error and the highest accuracy for evaluating the compressive strength of the concrete samples. The accuracy of the proposed SVR model for training and testing data was obtained as the coefficient of determination (R2) = 0.9694 and R2 = 0.9470, respectively. This function was considered as a relation, to be developed by the HLO algorithm to find optimal options under different conditions. The results for 14 samples, which are the most important examples of this research, showed that the optimal states are obtained with a high level of accuracy. This confirms the proper use/develop of the SVR-HLO algorithm in designing the predictive model as well as finding optimal conditions in the concrete technology area.

Soft computing techniques: Systematic multiscale models to predict the compressive strength of HVFA concrete based on mix proportions and curing times

Abstract: Advances in technology and environmental issues allow the building industry to use ever more high-performance engineered materials. In this study, the hardness of concrete mixtures with high-volume fly ash (HVFA) has been evaluated and modeled for the LEED (Leadership for Energy and Environmental Design). High-performance building materials may have greater strength, ductility, external factor resistance, more environmentally sustainable construction, and lower cost than conventional building materials. To overcome the mentioned matter, this study aims to establish systematic multiscale models to predict the compressive strength of concrete mixes containing a high volume of fly ash (HVFA) and to be used by the construction industry with no theoretical restrictions. For that purpose, a wide experimental data (a total of 450 tested HVFA concrete mixes) from different academic research studies have been statically analyzed and modeled. For that purpose, Linear, Nonlinear Regressions, Multi-logistic Regression, M5P-tree, and Artificial Neural Network (ANN) technical approaches were used for the qualifications. In the modeling process, most relevant parameters affecting the strength of concrete, i.e. fly ash (class C and F) incorporation ratio (0–80% of cement's mass), water-to-binder ratio (0.27–0.58), and gravel, sand, cement contents and curing ages (3–365 days). According to the correlation coefficient (R) and the root mean square error, the compressive strength of HVFA concrete can be well predicted in terms of w/b, fly ash, cement, sand, and gravel densities, and curing time using various simulation techniques. Among the used approaches and based on the training data set, the model made based on the ANN, M5P-tree, and Non-linear regression models seem to be the most reliable models. The results of this study suggest that the M5Ptree-based model is performing better than other applied models using training and testing datasets. The maximum and minimum percentage of error between the actual test results and the outcome of the prediction using MLR, LR, M5P-tree, and ANN were 0.03–43%, 0.03–54%, 0.04–33%, and 0.03–41% respectively. Based on the outcomes from the models and statistical assessments such as coefficient of determination (R2), mean absolute error (MAE) and the root mean square error (RMSE), the models M5P-tree, ANN, and MLR respectively were predicted the compressive strength of the HVFA concrete very well with a high value of R2 and low values of MAE and RMSE based on the comparison with experimental data. The sensitivity investigation concludes that the curing time is the most dominating parameter for the prediction of the compressive strength of HVFA concrete with this data set.

ANN, M5P-tree and nonlinear regression approaches with statistical evaluations to predict the compressive strength of cement-based mortar modified with fly ash

Abstract: This study aims to establish systematic multiscale models to predict the compressive strength of cement mortar containing a high volume of fly ash (FA) and to be used by the construction industry with no theoretical restrictions. For that purpose, a wide experimental data (a total of 450 tested cement mortar modified with FA) from different academic research studies have been statically analyzed and modeled. For that purpose, Linear and Nonlinear regression, M5P-tree, and Artificial Neural Network (ANN) technical approaches were used for the qualifications. In the modeling process, most relevant parameters affecting the strength of cement mortar, i.e. fly ash (class C and F) incorporation ratio (0−70% of cement's mass), water-to-binder ratio (0.235–1.2), and curing ages (1–365 days). According to the correlation coefficient (R), mean absolute error and the root mean a square error, the compressive strength of cement mortar can be well predicted in terms of w/b, fly ash, and curing time using various simulation techniques. The results of this study suggest that the Non-linear regression-based model (NLR) and ANN are performing better than other applied models using training and testing datasets. The sensitivity investigation concludes that the curing time is the most dominating parameter for the prediction of the compressive strength of cement mortar with this data set.

Influence of nano-TiO2, nano-Fe2O3, nanoclay and nano-CaCO3 on the properties of cement/geopolymer concrete

Abstract: In past decades, researchers have tried to improve the durability of concrete by integrating supplementary cementitious materials into concrete. Recent advancements in the field of nano-engineered concrete have reported that nanomaterials significantly improve the mechanical and durability properties of concrete. This paper provides a comprehensive summary of recent developments on the use of nanomaterials as a performance enhancer in cement/geopolymer concrete. Many significant correlations associated with the reinforcement of cementitious matrices using nano-TiO2, nano-Fe2O3, nanoclay/metakaolin, and nano-CaCO3 were studied. Performance aspects such as fresh properties, microstructure, mechanical and durability characteristics, and the influence of various particle sizes have been reviewed. The findings from this review confirm the feasibility of using the nanomaterials in cement concrete, with the required properties of building materials. It is also expected that this review provides better insight into using nanomaterials in concrete for the benefit of academic and fundamental research and promotes its practical application in the construction industry.

Systematic multiscale models to predict the compressive strength of self-compacting concretes modified with nanosilica at different curing ages

Abstract: The evolution of nanotechnology brings materials with novel performance and during last year’s much attempt has been established to include nanoparticles especially nano-silica (NS) into the concrete to improve performance and develop concrete with enhanced characteristics. Generally, NS is incorporated into the self-compacting concrete (SCC) aiming to positively influence the fresh, mechanical, microstructure, and durability properties of the composite. The most important mechanical property for all types of concrete composites is compressive strength. Therefore, developing reliable models for predicting the compressive strength of SCC is crucial regarding saving time, energy, and cost-effectiveness. Moreover, it gives valuable information for scheduling the construction work and provides information about the correct time for removing the formwork. In this study, three different models including the linear relationship model (LR), nonlinear model (NLR), and multi-logistic model (MLR) were proposed to predict the compressive strength of SCC mixtures made with or without NS. In this regard, a comprehensive data set that consists of 450 samples were collected and analyzed to develop the models. In the modeling process, the most important variables affecting the compressive strength such as NS content, cement content, water to binder ratio, curing time from 1 to 180 days, superplasticizer content, fine aggregate content, and coarse aggregate content were considered as input variables. 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 performance of the proposed models. The results indicated that the MLR model performed better for forecasting the compression strength of SCC mixtures modified with NS compared to other models. The SI and OBJ values of the MLR model were 18.8% and 16.7% lower than the NLR model, indicating the superior performance of the MLR model. Moreover, the sensitivity analysis demonstrated that the curing time is the most affecting variable for forecasting the compressive strength of SCC modified with NS.