RETRACTED ARTICLE: Predicting the effects of nanoparticles on compressive strength of ash-based geopolymers by gene expression programming

2012 ◽  
Vol 23 (6) ◽  
pp. 1677-1685 ◽  
Author(s):  
Ali Nazari ◽  
Shadi Riahi
Keyword(s):  
Gene Expression ◽  
Compressive Strength ◽  
Gene Expression Programming ◽  
Retracted Article

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 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.

Uniaxial compressive strength prediction through a new technique based on gene expression programming

2017 ◽  
Vol 30 (11) ◽  
pp. 3523-3532 ◽  
Author(s):  
Danial Jahed Armaghani ◽  
Vali Safari ◽  
Ahmad Fahimifar ◽  
Mohd For Mohd Amin ◽  
Masoud Monjezi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Compressive Strength ◽  
Uniaxial Compressive Strength ◽  
Gene Expression Programming ◽  
New Technique ◽  
Strength Prediction ◽  
A New Technique

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.

RETRACTED: Prediction of Compressive Strength of Geopolymers with Seeded Fly Ash and Rice Husk–Bark Ash by Gene Expression Programming

2011 ◽  
Vol 21 (8) ◽  
pp. 1202-1226 ◽  
Author(s):  
Ali Nazari ◽  
Shadi Riahi ◽  
Gholamreza Khalaj ◽  
Hamid Bohlooli ◽  
Mohammad Mehdi Kaykha
Keyword(s):  
Gene Expression ◽  
Compressive Strength ◽  
Fly Ash ◽  
Rice Husk ◽  
Gene Expression Programming ◽  
Rice Husk Bark Ash

scholarly journals COMPRESSIVE STRENGTH PREDICTION OF LIGHTWEIGHT SHORT COLUMNS AT ELEVATED TEMPERATURE USING GENE EXPRESSION PROGRAMING AND ARTIFICIAL NEURAL NETWORK

2020 ◽  
Vol 26 (2) ◽  
pp. 189-199
Author(s):  
Ahmed Ashteyat ◽  
Yasmeen T. Obaidat ◽  
Yasmin Z. Murad ◽  
Rami Haddad
Keyword(s):  
Gene Expression ◽  
Compressive Strength ◽  
Elevated Temperature ◽  
Gene Expression Programming ◽  
Data Set ◽  
Short Column ◽  
Short Columns ◽  
Ann Models ◽  
Artificial Neural ◽  
Concrete Elements

The experimental behavior of reinforced concrete elements exposed to fire is limited in the literature. Although there are few experimental programs that investigate the behavior of lightweight short columns, there is still a lack of formulation that can accurately predict their ultimate load at elevated temperature. Thus, new equations are proposed in this study to predict the compressive strength of the lightweight short column using Gene Expression Programming (GEP) and Artificial neural networks (ANN). A total of 83 data set is used to establish GEP and ANN models where 70% of the data are used for training and 30% of the data are used for validation and testing. The predicting variables are temperature, concrete compressive strength, steel yield strength, and spacing between stirrups. The developed models are compared with the ACI equation for short columns. The results have shown that the GEP and ANN models have a strong potential to predict the compressive strength of the lightweight short column. The predicted compressive strengths of short lightweight columns using the GEP and ANN models are closer to the experimental results than that obtained using the ACI equations.

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