New Gene Expression Programming models for normalized shear modulus and damping ratio of sands

Keshavarz, Amin; Mehramiri, Mohammad

doi:10.1016/j.engappai.2015.07.022

Cited by 36 publications

(16 citation statements)

References 25 publications

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“…Chromosomes are expressed as expression tree and evaluated by fit function, if the desired solution or the arrival of generations, the evolution is stopped and the best solution is provided [83,84]. If the conditions are not stopped, elitist will be done and the remaining solutions will be assigned to the selective process, which will be repeated for several generations and proceeded the generation to a superior quality of the population [85,86]. In the planning of gene expression, various operators, such as mutation and combination, are used.…”

Section: Gene Expression Programming (Gep)mentioning

confidence: 99%

Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP) approaches for predicting TBM penetration rate

Afradi

Ebrahimabadi

2020

SN Appl. Sci.

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Section: Gene Expression Programming (Gep)mentioning

confidence: 99%

Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP) approaches for predicting TBM penetration rate

Afradi

Ebrahimabadi

2020

SN Appl. Sci.

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“…Johari et al [39] used GEP in predicting effective stress parameter in unsaturated soils. Keshavarz and Mehramiri [40] utilized GEP to model the normalized shear modulus and damping ratio of sands. This method has also been used to predict the maximum lateral displacement of retaining wall [41] and soil-water characteristic curve [42].…”

Section: Due To Ability Of Finding Complex Relationships In Multivarimentioning

confidence: 99%

Gene expression programming models for liquefaction-induced lateral spreading

Keshavarz

Tofighi

2018

Scientia Iranica

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Lateral spreading is one of the most significant destructive and catastrophic phenomena associated with liquefaction caused by earthquake and can impose very serious damages to structures and engineering facilities. The aim of this study is to evaluate liquefaction induced lateral spreading and finding new relations using gene expression programming (GEP) that is a new and developed generation of genetic algorithms approaches. Since there are complicated, nonlinear and higher order relationships between many factors affecting the lateral spreading, GEP is assumed to be capable of finding complex and accurate relationships between these factors. This study includes three main stages: (i) compilation of available database (484 data), (ii) dividing data into training and testing categories, and (iii) building new models and propose new relationships to predict ground displacement in free face, gentle slope and general ground conditions. The results of modeling each of the different ground conditions are presented in the form of mathematical equations. At the end, the final GEP models for 3 different cases of ground conditions are compared with multiple linear regression (MLR) and other published models. The statistical parameters indicate the higher accuracy of the GEP models over other relations.

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“…The predictive ability of genetic programming and gene expression programming (GP and GEP, respectively) models has attracted interest from researchers investigating structural engineering problems [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. For example, Mansouri et al [ 24 ] applied the GEP approach to improving the shear strength estimation of exterior RC beam–column connections.…”

Section: Introductionmentioning

confidence: 99%

A Prediction Model for the Calculation of Effective Stiffness Ratios of Reinforced Concrete Columns

et al. 2021

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Nonlinear dynamic analyses of reinforced concrete (RC) frame buildings require the use of effective stiffness of members to capture the effect of cracked section stiffness. In the design codes and practices, the effective stiffness of RC sections is given as an empirical fraction of the gross stiffness. However, a more precise estimation of the effective stiffness is important as it affects the distribution of forces and various demands and response parameters in nonlinear dynamic analyses. In this study, an evolutionary computation method called gene expression programming (GEP) was used to predict the effective stiffness ratios of RC columns. Constitutive relationships were obtained by correlating the effective stiffness ratio with the four mechanical and geometrical parameters. The model was developed using a database of 226 samples of nonlinear dynamic analysis results collected from another study by the author. Subsequent parametric and sensitivity analyses were performed and the trends of the results were confirmed. The results indicate that the GEP model provides precise estimations of the effective stiffness ratios of the RC frames.

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New Gene Expression Programming models for normalized shear modulus and damping ratio of sands

Cited by 36 publications

References 25 publications

Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP) approaches for predicting TBM penetration rate

Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP) approaches for predicting TBM penetration rate

Gene expression programming models for liquefaction-induced lateral spreading

A Prediction Model for the Calculation of Effective Stiffness Ratios of Reinforced Concrete Columns

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