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

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253006
Author(s):  
Hemn Unis Ahmed ◽  
Ahmed Salih Mohammed ◽  
Azad A. Mohammed ◽  
Rabar H. Faraj
Keyword(s):  
Compressive Strength ◽  
Fly Ash ◽  
Sodium Silicate ◽  
Mean Squared Error ◽  
Curing Temperature ◽  
Coefficient Of Determination ◽  
Fine Aggregate ◽  
Geopolymer Concrete ◽  
Palm Oil Fuel Ash ◽  
Binder Ratio

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.

scholarly journals Study of Proportional Variation of Geopolymer Concrete which Self Compacting Concrete

2019 ◽  
Vol 2 (2) ◽  
pp. 65
Author(s):  
Purwanto P. ◽  
Himawan Indarto
Keyword(s):  
Compressive Strength ◽  
Fly Ash ◽  
Power Plant ◽  
Portland Cement ◽  
Fine Aggregate ◽  
Geopolymer Concrete ◽  
Conventional Concrete ◽  
Basic Characteristics ◽  
Carbon Dioxide Co2 ◽  
Proportional Variation

Portland cement production process which is the conventional concrete constituent materials always has an impact on producing carbon dioxide (CO2) which will damage the environment. To maintain the continuity of development, while maintaining the environment, Portland cement substitution can be made with more environmentally friendly materials, namely fly ash. The substitution of fly ash material in concrete is known as geopolymer concrete. Fly ash is one of the industrial waste materials that can be used as geopolymer material. Fly ash is mineral residue in fine grains produced from coal combustion which is mashed at power plant power plant [15]. Many cement factories have used fly ash as mixture in cement, namely Portland Pozzolan Cement. Because fly ash contains SiO2, Al2O3, P2O3, and Fe2O3 which are quite high, so fly ash is considered capable of replacing cement completely.This study aims to obtain geopolymer concrete which has the best workability so that it is easy to work on (Workable Geopolymer Concrete / Self Compacting Geopolymer Concrete) and obtain the basic characteristics of geopolymer concrete material in the form of good workability and compressive strength. In this study, geopolymer concrete is composed of coarse aggregate, fine aggregate, fly ash type F, and activators in the form of NaOH and Na2SiO3 Be52. In making geopolymer concrete, additional ingredients such as superplastizer are added to increase the workability of geopolymer concrete. From this research, the results of concrete compressive strength above fc' 25 MPa and horizontal slump values reached 60 to 80 centimeters.

scholarly journals Gene-Expression Programming-Based Model for Estimating the Compressive Strength of Cement-Fly Ash Stabilized Soil and Parametric Study

2021 ◽  
Vol 6 (12) ◽  
pp. 181
Author(s):  
Van-Ngoc Pham ◽  
Erwin Oh ◽  
Dominic E. L. Ong
Keyword(s):  
Gene Expression ◽  
Compressive Strength ◽  
Fly Ash ◽  
Calcium Oxide ◽  
Parametric Study ◽  
Soil Stabilization ◽  
Gene Expression Programming ◽  
Superior Performance ◽  
Stabilized Soil ◽  
Research Findings

The study aims to develop a reliable model using gene-expression programming (GEP) technique for estimating the unconfined compressive strength (UCS) of soil stabilization by cement and fly ash. The model considered the effects of several parameters, including the fly ash characteristics such as calcium oxide (CaO) content, CaO/SiO2 ratio, and loss of ignition. The research results show that the proposed model demonstrates superior performance with a high correlation coefficient (R > 0.955) and low errors. Therefore, the model could be confidently applied in practice for a variety of fly ash qualities. Besides, the parametric study was conducted to examine the effect of fly ash characteristics on the strength of soil stabilization. The study indicates that if the fly ash contains a high amount of calcium oxide, the strength of fly ash stabilized soil is significant. In addition, fly ash could be used in combination with cement to increase the strength of the mixture. A fly ash replacement ratio is suggested from 0.19 to 0.35, corresponding to the total binder used from 10% to 30%. The research findings could help engineers in optimizing the fly ash proportion and estimating the UCS of soil stabilization by cement and fly ash.

scholarly journals Random Forest Modeling for Fly Ash-Calcined Clay Geopolymer Composite Strength Detection

2021 ◽  
Vol 5 (10) ◽  
pp. 271
Author(s):  
Priyanka Gupta ◽  
Nakul Gupta ◽  
Kuldeep K. Saxena ◽  
Sudhir Goyal
Keyword(s):  
Tensile Strength ◽  
Compressive Strength ◽  
Fly Ash ◽  
Random Forest ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Curing Temperature ◽  
Coefficient Of Determination ◽  
Split Tensile Strength ◽  
Calcined Clay

Geopolymer is an eco-friendly material used in civil engineering works. For geopolymer concrete (GPC) preparation, waste fly ash (FA) and calcined clay (CC) together were used with percentage variation from 5, 10, and 15. In the mix design for geopolymers, there is no systematic methodology developed. In this study, the random forest regression method was used to forecast compressive strength and split tensile strength. The input content involved were caustic soda with 12 M, 14 M, and 16 M; sodium silicate; coarse aggregate passing 20 mm and 10 mm sieve; crushed stone dust; superplasticizer; curing temperature; curing time; added water; and retention time. The standard age of 28 days was used, and a total of 35 samples with a target-specified compressive strength of 30 MPa were prepared. In all, 20% of total data were trained, and 80% of data testing was performed. Efficacy in terms of mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and MSE (mean squared error) is suggested in the model. The results demonstrated that the RFR model is likely to predict GPC compressive strength (MAE = 1.85 MPa, MSE = 0.05 MPa, RMSE = 2.61 MPa, and R2 = 0.93) and split tensile strength (MAE = 0.20 MPa, MSE = 6.83 MPa, RMSE = 0.24 MPa, and R2 = 0.90) during training.

scholarly journals Compressive Strength of Geopolymer Concrete Composites: Modeling and Comprehensive Systematic Review

2021 ◽  
Author(s):  
Hemn Unis Ahmed ◽  
Azad A. Mohammed ◽  
Ahmed S. Mohammed
Keyword(s):  
Systematic Review ◽  
Compressive Strength ◽  
Fly Ash ◽  
Sodium Hydroxide ◽  
Sodium Silicate ◽  
Alkaline Solution ◽  
Construction Material ◽  
Assessment Tools ◽  
Curing Temperature ◽  
Geopolymer Concrete

Abstract The growing concern about global climate change and its adverse impacts on societies is putting severe pressure on the construction industry as one of the largest producers of greenhouse gases. Given the environmental issues associated with cement production, geopolymer concrete has emerged as a sustainable construction material. Geopolymer concrete is cementless concrete that uses industrial or agro by-product ashes as the main binder instead of ordinary Portland cement; this leads to being an eco-efficient and environmentally friendly construction material. Compressive strength is one of the most important mechanical property for all types of concrete composites including geopolymer concrete, and it is affected by several parameters like an alkaline solution to binder ratio (l/b), fly ash (FA) content, SiO2/Al2O3 (Si/Al) of the FA, fine aggregate (F) and coarse aggregate (C) content, sodium hydroxide (SH) and sodium silicate (SS) content, ratio of sodium silicate to sodium hydroxide (SS/SH), molarity (M), curing temperature (T), curing duration (CD) inside the oven and specimen ages (A). In this regard, a comprehensive systematic review was carried out to show the effect of these different parameters on the compressive strength of the fly ash-based geopolymer concrete (FA-GPC). In addition, multi-scale models such as Artificial Neural Network (ANN), M5P-tree (M5P), Linear Regression (LR), and Multi-logistic Regression (MLR) models were developed to predict the compressive strength of FA-GPC composites. For the first time, in the modeling process, twelve effective parameters including l/b, FA, Si/Al, F, C, SH, SS, SS/SH, M, T, CD, and A were considered the modeling input parameters. Then, the efficiency of the developed models was assessed by various statistical assessment tools like Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the Coefficient of determination (R2). Results show that the curing temperature, sodium silicate content, and ratio of the alkaline solution to the binder content are the most significant independent parameters that influence on the compressive strength of the FA-GPC, and the ANN model has better performance for predicting the compressive strength of FA-GPC in compared to the other developed models.

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.

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