An integrated SRM-multi-gene genetic programming approach for prediction of factor of safety of 3-D soil nailed slopes

Garg, Akhil; Garg, Ankit; Tai, Kang; Sreedeep, S.

doi:10.1016/j.engappai.2013.12.011

Cited by 91 publications

(40 citation statements)

References 36 publications

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“…The population size and number of generations depend upon the involvement of the input data. A study by Garg et al (2014) suggests that minimum error will be achieved for complex data, if the population size and number of generation values are kept at a higher range.…”

Section: Genetic Programmingmentioning

confidence: 98%

“…Following the literature (Garg et al 2014), we have formulated the LS-SVM (See Fig. S-1b) for prediction in the present study as follows (Garg et al 2014),…”

Section: Support Vector Machines Modelmentioning

confidence: 99%

“…Genetic programming (GPTIPS) is a biological inspired technology (multi gene) based on Darwinian Theory of evolution and on Herbert Spencer phrase "survival of the fittest" (Garg et al 2014). In Genetic programming, the fittest population has to be developed from the best performing trees of different sizes randomly by performing tasks based on physical data of mutation and crossover process.…”

Section: Genetic Programmingmentioning

confidence: 99%

“…For example, in the past artificial neural network, response surface methodology, neuro-fuzzy and swam particle optimization have been used widely in the prediction of contaminant removal from water systems with reasonable accuracy (Kotti et al 2013;Dora et al 2013;Aleboyeh et al 2008;Akratos et al 2008;Chu 2003;Saha et al 2010;Elmolla et al 2010;Khajeh and Modarress 2010); presently some researchers have tried to develop applications of genetic programming (GP) and support vector machine (SVM) tools in prediction and optimization of various aspects, such as prediction of carbon monoxide concentrations by using hybrid Partial Least Square Support Vector Machine (LS-SVM) models (Garg et al 2014;Yeganeh et al 2012), and photovoltaic plant output forecasting (Russo et al 2014), vaporization enthalpy of petroleum fractions and pure hydrocarbon modeling (Parhizgar et al 2013) and groundwater level prediction and simulation (Fallah-Mehdipour et al 2013) by using GP techniques.…”

Section: Introductionmentioning

confidence: 99%

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Modeling of Arsenic (III) Removal by Evolutionary Genetic Programming and Least Square Support Vector Machine Models

et al. 2014

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In this study, the co-precipitation method was used to synthesize the cerium oxide tetraethylenepentamine (CTEPA) hybrid material with the variation of the molar concentration of the metal oxide. Physicochemical techniques, like FESEM, XRD, FTIR, HR-TEM and TGA-DSC, were used to characterize the hybrid material. The adsorption experiment was carried out to estimate the optimum condition for adsorption with the variation of adsorbent dose, pH of the solution, time and initial concentration of the adsorbate. These variables are further used to develop an approach for the evaluation of As(III) removal from water by using evolutionary genetic programming techniques (GP) and least square support vector models (LS-SVM). GP model was found to be the best performing model for understanding the nonlinear behavior and prediction of As(III) removal (minimum standard error GP training 0.411, GP testing 0.658). The adsorption process in this study followed second order kinetics. The experimental data were best fitted to linearly transformed Langmuir isotherm with an R 2 (correlation coefficient) value of >0.99 and having a maximum adsorption capacity of 124.8 mg/g at 25±2°C. The type of adsorption process was derived from the DubininRadushkevich isotherm model. A comparison between the model and actual experimental values gave a high correlation coefficient value (R 2 training (GP) 0.988, R 2 testing (GP) 0.977).

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Section: Genetic Programmingmentioning

confidence: 98%

“…Following the literature (Garg et al 2014), we have formulated the LS-SVM (See Fig. S-1b) for prediction in the present study as follows (Garg et al 2014),…”

Section: Support Vector Machines Modelmentioning

confidence: 99%

Section: Genetic Programmingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling of Arsenic (III) Removal by Evolutionary Genetic Programming and Least Square Support Vector Machine Models

et al. 2014

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“…The values of population size and number of generations fairly depend on the complexity of the data. Based on previous applications of the algorithm by Garg et al [34][35][36][37][38][39], the population size and number of generations should be fairly large for data of higher complexity, so as to find the models with minimum error. Maximum number of genes and maximum depth of the gene influences the size and the number of models to be searched in the global space.…”

Section: Multi-gene Genetic Programmingmentioning

confidence: 99%

Process characterisation of 3D-printed FDM components using improved evolutionary computational approach

Vijayaraghavan

Garg

Lam

et al. 2014

Int J Adv Manuf Technol

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Fused deposition modelling (FDM) is an additive manufacturing technique deployed to fabricate the functional components leading to shorter product development times with less human intervention. Typical characteristics such as surface roughness, mechanical strength and dimensional accuracy are found to influence the wear strength of FDM fabricated components. It would be useful to determine an explicit numerical model to describe the correlation between various output process parameters and input parameters. In this paper, we have proposed an improved approach of multi-gene genetic programming (Im-MGGP) to formulate the functional relationship between wear strength and input process variables of the FDM process. It was found that the improved approach performs better than MGGP, SVR and ANN models and is able to generalise wear strength of the FDM prototype satisfactorily. Further, sensitivity and parametric analysis is conducted to study the influence of each input variable on the wear strength of the FDM fabricated components. It was found that the input parameter, air gap, has the maximum influence on the wear strength of the FDM fabricated component.

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Optimization of helical soil nailing behaviors by response surface methodology and hybrid Coot optimization

Goyal

Shrivastava

2023

Num Anal Meth Geomechanics

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Soil nailing is one of the effective methods of slope stabilization, and its stability mainly depends on the properties of both the soil and soil nails. Optimizing soil nailing parameters is an effective task to avoid installation issues. This work uses four input parameters: different shaft diameters, surcharge pressure, helical pitch, and inclination angle. The design plan for the experimentation is conducted using Box Behnken design (BBD) of response surface methodology (RSM), performed in design expert software. In addition, RSM is used to determine the optimal design combinations. A total of 25 experimental runs are taking place, and the significance of the developed quadratic model is determined using the analysis of variance (ANOVA) test. Hybrid deep belief network-based Coot optimization (DBN-CO) is also used to optimize the pull-out capacity and the safety factor during installation for more precised prediction. When compared to others the proposed hybrid DBN-CO predictive model represents the better performance with regression R = 0.99. Also least MSE & RMSE and highest R & R 2 values obtained by hybrid DBN-CO. The obtained experimental outcomes from RSM are 1.46 of safety factor, and 7.55 kN pull-out capacity. The predicted results from the proposed hybrid DBN-CO for the safety factor are 1.44 and pull-out capacity is 7.77 kN. As a result, the optimal results of DBN-CO are 3% greater than RSM, DBN, and artificial neural network (ANN). Based on prediction approaches the proposed hybrid DBN-CO results are in perfect agreement with experimental values and are additional superior to the RSM, DBN, and ANN.

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An integrated SRM-multi-gene genetic programming approach for prediction of factor of safety of 3-D soil nailed slopes

Cited by 91 publications

References 36 publications

Modeling of Arsenic (III) Removal by Evolutionary Genetic Programming and Least Square Support Vector Machine Models

Modeling of Arsenic (III) Removal by Evolutionary Genetic Programming and Least Square Support Vector Machine Models

Process characterisation of 3D-printed FDM components using improved evolutionary computational approach

Optimization of helical soil nailing behaviors by response surface methodology and hybrid Coot optimization

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