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 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 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 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 Application of Gene Expression Programming, Artificial Neural Network and Multilinear Regression in Predicting Hydrochar Physicochemical Properties

2020 ◽  
Author(s):  
Jibril Abdulsalam ◽  
Abiodun Ismail Lawal ◽  
Ramadimetja Lizah Setsepu ◽  
Moshood Onifade ◽  
Samson Bada
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Artificial Neural Network ◽  
Performance Metrics ◽  
Gene Expression Programming ◽  
Mean Bias Error ◽  
Energy Yield ◽  
Multilinear Regression ◽  
Soft Computing Techniques ◽  
Artificial Neural

Abstract Globally, the provision of energy is becoming an absolute necessity. Biomass resources are abundant and have been described as a potential alternative source of energy. However, it is important to assess the fuel characteristics of the various available biomass sources. Soft computing techniques are presented in this study to predict the mass yield (MY), energy yield (EY), and higher heating value (HHV) of hydrothermally carbonized biomass by using Gene Expression Programming (GEP), multiple-input single output-artificial neural network (MISO-ANN), and Multilinear regression (MLR). The three techniques were compared using statistical performance metrics. The coefficient of determination (R2), mean absolute error (MAE), and mean bias error (MBE) were used to evaluate the performance of the models. The MISO-ANN with 5-10-10-1 and 5-15-15-1 network architectures provided the most satisfactory performance of the three proposed models (R2 = 0.976, 0.955, 0.996; MAE = 2.24, 2.11, 0.93; MBE = 0.16, 0.37, 0.12) for MY, EY and HHV respectively. The GEP technique’s ability to predict hydrochar properties based on the input parameters was found to be satisfactory, while MLR provided an unsatisfactory predictive model. Sensitivity analysis was conducted, and the analysis revealed that volatile matter (VM) and temperature (Temp) have more influence on the MY, EY, and HHV.

A new predictive model for compressive strength of HPC using gene expression programming

2012 ◽  
Vol 45 (1) ◽  
pp. 105-114 ◽  
Author(s):  
Seyyed Mohammad Mousavi ◽  
Pejman Aminian ◽  
Amir Hossein Gandomi ◽  
Amir Hossein Alavi ◽  
Hamed Bolandi
Keyword(s):  
Gene Expression ◽  
Compressive Strength ◽  
Predictive Model ◽  
Gene Expression Programming

scholarly journals Studi Perancangan Campuran Beton Menggunakan Abu Batu Sebagai Agregat Halus. (Hal. 108-117)

2019 ◽  
Vol 5 (3) ◽  
pp. 108
Author(s):  
Muhammad Malik Ibrahim ◽  
Priyanto Saelan
Keyword(s):  
Compressive Strength ◽  
Concrete Mixture ◽  
Fine Aggregate ◽  
Natural Sand ◽  
Additional Water ◽  
Compressive Strength Of Concrete ◽  
Concrete Materials ◽  
The Relationship ◽  
Water Ratio ◽  
Research Show

ABSTRAKSalah satu limbah yang dapat digunakan sebagai pengganti bahan pembuat beton adalah abu batu. Abu batu merupakan limbah dari proses pemecahan bongkahan batu. Ditinjau dari ukuran butirannya maka abu batu merupakan agregat halus. Abu batu memiliki penyerapan air yang lebih tinggi daripada pasir alami, maka dari itu untuk mendapatkan kelecakan campuran beton yang sama dengan kelecakan campuran beton menggunakan pasir alami, penggunaan abu batu sebagai agregat halus dalam campuran beton perlu tambahan air. Namun hal ini akan menyebabkan faktor air-semen bertambah. Sehingga hasil kuat tekan akan menurun. Hal ini sesuai dengan hubungan antara kuat tekan beton dengan faktor air-semen. Perekayasaan yang dilakukan adalah dengan menaikkan faktor granular (G) dan menaikkan kuat tekan rencana berdasarlan teori Dreux. Abu batu pada penelitian ini digunakan sebagai substitusi pasir alami dengan proporsi 0%, 20%, 40%, 60%, 80%, dan 100%. Hasil penelititan ini memperlihatkan penggunaan abu batu sebagai agregat halus lebih dari 40% akan sangat drastis menurunkan kuat tekan beton.Kata kunci: perekayasaan, substitusi, campuran beton, abu batu, agregat halus ABSTRACTOne of the wastes that can be used as a substitute for concrete materials is stone ash. Stone ash is a waste from the process of stone crusher. Consider from the size of the grain, stone ash as fine aggregate. Stone ash has a higher water absorption than natural sand, therefore to get the concrete workability that is the same as the concrete workability using natural sand, the use of stone ash as fine aggregate in the concrete mixture needs additional water. But this will cause the cement-water ratio to increase. So that the compressive strength will decrease. This is following the relationship between the compressive strength of concrete and the cement-water ratio. Engineering is done by increasing the granular factor (G) and increasing the compressive strength of the plan based on Dreux theory. Stone ash in this study was used as a substitute for natural sand with a proportion of 0%, 20%, 40%, 60%, 80%, and 100%. The results of this research show that the use of stone ash as fine aggregate of more than 40% will greatly reduce the compressive strength of the concrete.Keywords: engineering, substitute, concrete mixture, stone ash, fine aggregate

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