scholarly journals Experimental investigation and predictive modeling of compressive strength of pozzolanic geopolymer concrete using gene expression programming

2020 ◽  
Author(s):  
Amir Ali Shahmansouri ◽  
Habib Akbarzadeh Bengar ◽  
Saeed Ghanbari
Keyword(s):  
Gene Expression ◽  
Experimental Investigation ◽  
Compressive Strength ◽  
Gene Expression Programming ◽  
Solution Concentration ◽  
Partial Replacement ◽  
Geopolymer Concrete ◽  
Naoh Solution ◽  
High Prediction ◽  
Influential Parameters

With regard to the adverse environmental impacts of cement production, the use of geopolymer concrete (GPC) can be considered as a more environmentally friendly approach for concreting. This study deals with an experimental investigation on the effects of partial replacement of the GGBS (replaced with 5, 10, 15, 20, 25, and 30%) used in GPC with natural zeolite (NZ) and silica fume (SF) simultaneously with different concentration (4, 6 and 8 M) of sodium hydroxide (NaOH) together with sodium silicate (water glass) solution on the compressive strength. Results indicate that increasing concentration of NaOH yields decreases the compressive strength of the concrete. In contrast, adding NZ and SF into concrete results in increasing the compressive strength. In addition, gene expression programming (GEP) was employed to develop mathematical models for predicting the compressive strength of GPC based on GGBS. Using the experimental results, an extensive and reliable database of compressive strength of GGBS-based GPC was obtained. The database comprises the compressive strength results of 351 specimens produced from 117 different mixtures. The five most influential parameters i.e., age of specimens, NaOH solution concentration, NZ, SF and GGBS content of GPC, were considered as the input parameters for modeling. The results reflected that the proposed models are accurate and possess a high prediction capability. The findings of this study can enhance the re-use of GGBS for the development of GPC leading to environmental protection and monetary benefits.

scholarly journals Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete

Materials ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1106 ◽  
Author(s):  
Mohsin Ali Ali Khan ◽  
Adeel Zafar ◽  
Arslan Akbar ◽  
Muhammad Faisal Javed ◽  
Amir Mosavi
Keyword(s):  
Gene Expression ◽  
Compressive Strength ◽  
Empirical Equation ◽  
Gene Expression Programming ◽  
Curing Temperature ◽  
Fine Aggregate ◽  
Geopolymer Concrete ◽  
Regression Equations ◽  
Soft Computing Techniques ◽  
Water Ratio

For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, the soft computing techniques known as gene expression programming (GEP) are executed to deliver an empirical equation to estimate the compressive strength fc′ of GPC made by employing FA. To build a model, a consistent, extensive and reliable data base is compiled through a detailed review of the published research. The compiled data set is comprised of 298 fc′ experimental results. The utmost dominant parameters are counted as explanatory variables, in other words, the extra water added as percent FA (%EW), the percentage of plasticizer (%P), the initial curing temperature (T), the age of the specimen (A), the curing duration (t), the fine aggregate to total aggregate ratio (F/AG), the percentage of total aggregate by volume ( %AG), the percent SiO2 solids to water ratio (% S/W) in sodium silicate (Na2SiO3) solution, the NaOH solution molarity (M), the activator or alkali to FA ratio (AL/FA), the sodium oxide (Na2O) to water ratio (N/W) for preparing Na2SiO3 solution, and the Na2SiO3 to NaOH ratio (Ns/No). A GEP empirical equation is proposed to estimate the fc′ of GPC made with FA. The accuracy, generalization, and prediction capability of the proposed model was evaluated by performing parametric analysis, applying statistical checks, and then compared with non-linear and linear regression equations.

scholarly journals Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest

2021 ◽  
Vol 2021 ◽  
pp. 1-17 ◽  
Author(s):  
Mohsin Ali Khan ◽  
Shazim Ali Memon ◽  
Furqan Farooq ◽  
Muhammad Faisal Javed ◽  
Fahid Aslam ◽  
...  
Keyword(s):  
Gene Expression ◽  
Compressive Strength ◽  
Fly Ash ◽  
Random Forest ◽  
Gene Expression Programming ◽  
Curing Temperature ◽  
Fine Aggregate ◽  
Geopolymer Concrete ◽  
Soft Computing Techniques ◽  
Water Ratio

Fly ash (FA) is a residual from thermal industries that has been effectively utilized in the production of FA-based geopolymer concrete (FGPC). To avoid time-consuming and costly experimental procedures, soft computing techniques, namely, random forest regression (RFR) and gene expression programming (GEP), are used in this study to develop an empirical model for the prediction of compressive strength of FGPC. A widespread, reliable, and consistent database of compressive strength of FGPC is set up via a comprehensive literature review. The database consists of 298 compressive strength data points. The influential parameters that are considered as input variables for modelling are curing temperature T , curing time t , age of the specimen A , the molarity of NaOH solution M , percent SiO2 solids to water ratio %   S / W in sodium silicate (Na2SiO3) solution, percent volume of total aggregate (   %   A G ), fine aggregate to the total aggregate ratio F / A G , sodium oxide (Na2O) to water ratio N / W in Na2SiO3 solution, alkali or activator to the FA ratio A L / F A , Na2SiO3 to NaOH ratio N s / N o , percent plasticizer ( %   P ), and extra water added as percent FA E W % . RFR is an ensemble algorithm and gives outburst performance as compared to GEP. However, GEP proposed an empirical expression that can be used to estimate the compressive strength of FGPC. The accuracy and performance of both models are evaluated via statistical error checks, and external validation is considered. The proposed GEP equation is used for sensitivity analysis and parametric study and then compared with nonlinear and linear regression expressions.

scholarly journals Compressive Strength of Fly ash-based Geopolymer Concrete with a Variable of Sodium Hydroxide (NaOH) Solution Molarity

2018 ◽  
Vol 147 ◽  
pp. 01004 ◽  
Author(s):  
Herwani ◽  
Ivindra Pane ◽  
Iswandi Imran ◽  
Bambang Budiono
Keyword(s):  
Compressive Strength ◽  
Fly Ash ◽  
Sodium Hydroxide ◽  
Raw Materials ◽  
Sodium Hydroxide Solution ◽  
Solution Concentration ◽  
Geopolymer Concrete ◽  
Hydroxide Solution ◽  
Naoh Solution ◽  
New Material

Geopolymer concrete is a new material made by activating the raw materials which contain many elements of silica and alumina. Compressive strength of geopolymer concrete produced was influenced by the concentration of the activator solution. This paper presents an experimental investigation into fly ash-based geopolymer concrete. Research objective was to investigate the effects of alkaline activator solution (AAS) molarity on compressive strength of geopolymer concrete. Variable of the test were a solution to sodium hydroxide was chosen as the activator solution. Concentration of sodium hydroxide solution used was 10 M, 12 M and 14 M with ambient curing. The specimen is made of concrete cylinder with diameter 10 cm and height 20 cm as many as 9 pieces each variable. Compressive strength tests is performed when the concrete is 7, 14, and 28 days old. Results of the test are indicated that the increasing of sodium hydroxide (NaOH) solution concentration leads to improve the compressive strength of geopolymer concrete. The optimal compressive strength of geopolymer concrete was achieved at a concentration of sodium hydroxide solution (NaOH) of 12 M. Geopolymer concretes compressive strength only achieves around 50-60% of the planned.

scholarly journals Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete

2021 ◽  
Author(s):  
Mohsin Ali Khan ◽  
Adeel Zafar ◽  
Arslan Akbar ◽  
Muhammad Faisal Javed ◽  
Amir Mosavi
Keyword(s):  
Gene Expression ◽  
Compressive Strength ◽  
Empirical Equation ◽  
Gene Expression Programming ◽  
Curing Temperature ◽  
Fine Aggregate ◽  
Geopolymer Concrete ◽  
Regression Equations ◽  
Soft Computing Techniques ◽  
Water Ratio

Abstract: For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, the soft computing techniques known as gene expression programming (GEP) are executed to deliver an empirical equation to estimate the compressive strength f_c^' of GPC made by employing FA. To build a model, a consistent, extensive and reliable data base is compiled through a detailed review of the published research. The compiled data set is comprised of 298 f_c^' experimental results. The utmost dominant parameters are counted as explanatory variables, in other words, the extra water added as percent FA (%E_W), the percentage of plasticizer (%P), the initial curing temperature (T), the age of the specimen (A), the curing duration (t), the fine aggregate to total aggregate ratio (F⁄A_G ), the percentage of total aggregate by volume ( 〖%A〗_G), the percent SiO2 solids to water ratio (% S/W) in sodium silicate (Na2SiO3) solution, the NaOH solution molarity (M), the activator or alkali to FA ratio (A_L⁄F_A ), the sodium oxide (Na2O) to water ratio (N⁄W) for preparing Na2SiO3 solution, and the Na2SiO3 to NaOH ratio (N_s⁄N_o ). A GEP empirical equation is proposed to estimate the f_c^' of GPC made with FA. The accuracy, generalization, and prediction capability of the proposed model was evaluated by performing parametric analysis, applying statistical checks, and then compared with non-linear and linear regression equations.

scholarly journals Resistance to Sulfuric Acid Corrosion of Geopolymer Concrete Based on Different Binding Materials and Alkali Concentrations

Materials ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7109
Author(s):  
Wei Yang ◽  
Pinghua Zhu ◽  
Hui Liu ◽  
Xinjie Wang ◽  
Wei Ge ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Corrosion Resistance ◽  
Sulfuric Acid ◽  
Fly Ash ◽  
Acid Corrosion ◽  
Geopolymer Concrete ◽  
High Calcium ◽  
High Calcium Fly Ash ◽  
Naoh Solution ◽  
Sulfuric Acid Corrosion

Geopolymer binder is expected to be an optimum alternative to Portland cement due to its excellent engineering properties of high strength, acid corrosion resistance, low permeability, good chemical resistance, and excellent fire resistance. To study the sulfuric acid corrosion resistance of geopolymer concrete (GPC) with different binding materials and concentrations of sodium hydroxide solution (NaOH), metakaolin, high-calcium fly ash, and low-calcium fly ash were chosen as binding materials of GPC for the geopolymerization process. A mixture of sodium silicate solution (Na2SiO3) and NaOH solution with different concentrations (8 M and 12 M) was selected as the alkaline activator with a ratio (Na2SiO3/NaOH) of 1.5. GPC specimens were immersed in the sulfuric acid solution with the pH value of 1 for 6 days and then naturally dried for 1 day until 98 days. The macroscopic properties of GPC were characterized by visual appearance, compressive strength, mass loss, and neutralization depth. The materials were characterized by SEM, XRD, and FTIR. The results indicated that at the immersion time of 28 d, the compressive strength of two types of fly ash-based GPC increased to some extent due to the presence of gypsum, but this phenomenon was not observed in metakaolin-based GPC. After 98 d of immersion, the residual strength of fly ash based GPC was still higher, which reached more than 25 MPa, while the metakaolin-based GPC failed. Furthermore, due to the rigid 3D networks of aluminosilicate in fly ash-based GPC, the mass of all GPC decreased slightly during the immersion period, and then tended to be stable in the later period. On the contrary, in metakaolin-based GPC, the incomplete geopolymerization led to the compressive strength being too low to meet the application of practical engineering. In addition, the compressive strength of GPC activated by 12 M NaOH was higher than the GPC activated by 8 M NaOH, which is owing to the formation of gel depended on the concentration of alkali OH ion, low NaOH concentration weakened chemical reaction, and reduced compressive strength. Additionally, according to the testing results of neutralization depth, the neutralization depth of high-calcium fly ash-based GPC activated by 12 M NaOH suffered acid attack for 98 d was only 6.9 mm, which is the minimum value. Therefore, the best performance was observed in GPC prepared with high-calcium fly ash and 12 M NaOH solution, which is attributed to gypsum crystals that block the pores of the specimen and improve the microstructure of GPC, inhibiting further corrosion of sulfuric acid.

scholarly journals Comparison of Conventional Concrete and Replacement of River Sand by Waste Foundry Sand and Cement by Nano-Silica

2020 ◽  
pp. 201-205
Keyword(s):  
Experimental Investigation ◽  
Compressive Strength ◽  
Partial Replacement ◽  
Fine Aggregate ◽  
Nano Silica ◽  
River Sand ◽  
Conventional Concrete ◽  
Foundry Sand ◽  
Concrete Production ◽  
Waste Foundry Sand

This paper presents an experimental investigation on the properties of concrete in which like cement is partially replacing by used nano silica and is partially replacing by used waste foundry sand. Because now a day the world wide consumption of sand as cement and as fine aggregate in concrete production is very high. Nano silica and waste foundry sand are major by product of casting industry and create land pollution. The cement will be replaced with nano silica and the river sand will be replaced with waste foundry sand (0%, 5%, 10%, 15%, 20%). This experimental investigation was done and found out that with the increase in the nano silica and waste foundry sand ratio. Compression test has been done to find out the compressive strength of concrete at the age of 7, 14, 21, and 28. Test result indicates in increasing compressive strength of plain concrete by inclusion of nano silica as a partial replacement of cement and waste foundry sand as a partial replacement of fine aggregate.

scholarly journals Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete

Materials ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 983 ◽  
Author(s):  
Dong Dao ◽  
Hai-Bang Ly ◽  
Son Trinh ◽  
Tien-Thinh Le ◽  
Binh Pham
Keyword(s):  
Artificial Intelligence ◽  
Compressive Strength ◽  
Fly Ash ◽  
Sodium Hydroxide ◽  
Sodium Silicate ◽  
Steel Slag ◽  
Sodium Silicate Solution ◽  
Silicate Solution ◽  
Partial Replacement ◽  
Geopolymer Concrete

Geopolymer concrete (GPC) has been used as a partial replacement of Portland cement concrete (PCC) in various construction applications. In this paper, two artificial intelligence approaches, namely adaptive neuro fuzzy inference (ANFIS) and artificial neural network (ANN), were used to predict the compressive strength of GPC, where coarse and fine waste steel slag were used as aggregates. The prepared mixtures contained fly ash, sodium hydroxide in solid state, sodium silicate solution, coarse and fine steel slag aggregates as well as water, in which four variables (fly ash, sodium hydroxide, sodium silicate solution, and water) were used as input parameters for modeling. A total number of 210 samples were prepared with target-specified compressive strength at standard age of 28 days of 25, 35, and 45 MPa. Such values were obtained and used as targets for the two AI prediction tools. Evaluation of the model’s performance was achieved via criteria such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The results showed that both ANN and ANFIS models have strong potential for predicting the compressive strength of GPC but ANFIS (MAE = 1.655 MPa, RMSE = 2.265 MPa, and R2 = 0.879) is better than ANN (MAE = 1.989 MPa, RMSE = 2.423 MPa, and R2 = 0.851). Sensitivity analysis was then carried out, and it was found that reducing one input parameter could only make a small change to the prediction performance.

Sign in / Sign up

Export Citation Format

Share Document