Prediction of strain energy-based liquefaction resistance of sand–silt mixtures: An evolutionary approach

Baziar, Mohammad Hassan; Jafarian, Yaser; Shahnazari, Habib; Movahed, Vahid; Tutunchian, Mohammad Amin

doi:10.1016/j.cageo.2011.04.008

Cited by 50 publications

(26 citation statements)

References 44 publications

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“…The energy-based database was developed by taking in new test results [8,9,12,28], and these tests were undrained with k p = 0. Whether before or post consolidation, relative density is almost the same for saturated sand under undrained conditions.…”

Section: Energy-based Analysis and Database Buildingmentioning

confidence: 99%

“…Dissipated energy density (W) represents the energy that soil particles consume in the process of reorganization under seismic loading per unit volume in soil. Capacity energy (W liq ) is denoted as the W that achieves liquefaction and has been identified as a representative indicator in the evaluation liquefaction potential [8][9][10][11][12]. When the test soil mechanical parameters are provided, the W liq can be predicted by employing different neural network (NN) models [11,[13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Liquefaction Potential in Saturated Sand under Different Drainage Boundary Conditions—An Energy Approach

Yao

Wang

Liu

et al. 2019

JMSE

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Drainage conditions are supposed to have significant influence on sand liquefaction behavior. An infiltration device was utilized in cyclic triaxial tests to reproduce different drainage conditions by altering dry density of the within silt. Permeability coefficient ratio (kp) was utilized for quantifying the drainage boundary effect. Cyclic triaxial tests were conducted on saturated Fujian standard sand samples. Test results were used to evaluate the liquefaction potential by using the energy approach. It can be concluded that, if kp increases slightly bigger than zero, excess pore water pressure (EPWP) will respond more fiercely, and the dissipated energy that triggers sand liquefaction will be less. By considering kp, an energy-based database was built by taking kp into consideration and different neural network (NN) models were constructed to predict liquefaction potential by energy approaches accurately under different drainage boundary conditions. It was suggested that the neuro-fuzzy (NF)-based NN model has more satisfactory performance.

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Section: Energy-based Analysis and Database Buildingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Liquefaction Potential in Saturated Sand under Different Drainage Boundary Conditions—An Energy Approach

Yao

Wang

Liu

et al. 2019

JMSE

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“…Moreover, they require the structure of the network to be identified in advance . Recently, Baziar et al (2011) utilized an evolutionary approach based on GP for estimation of capacity energy of liquefiable soils.…”

Section: Soft Computing-based Modelsmentioning

confidence: 99%

Energy-based numerical models for assessment of soil liquefaction

Alavi

Gandomi

2012

Geoscience Frontiers

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This study presents promising variants of genetic programming (GP), namely linear genetic programming (LGP) and multi expression programming (MEP) to evaluate the liquefaction resistance of sandy soils. Generalized LGP and MEP-based relationships were developed between the strain energy density required to trigger liquefaction (capacity energy) and the factors affecting the liquefaction characteristics of sands. The correlations were established based on well established and widely dispersed experimental results obtained from the literature. To verify the applicability of the derived models, they were employed to estimate the capacity energy values of parts of the test results that were not included in the analysis. The external validation of the models was verified using statistical criteria recommended by researchers. Sensitivity and parametric analyses were performed for further verification of the correlations. The results indicate that the proposed correlations are effectively capable of capturing the liquefaction resistance of a number of sandy soils. The developed correlations provide a significantly better prediction performance than the models found in the literature. Furthermore, the best LGP and MEP models perform superior than the optimal traditional GP model. The verification phases confirm the efficiency of the derived correlations for their general application to the assessment of the strain energy at the onset of liquefaction. ª 2011, China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V. All rights reserved.

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“…This software is written on MATLAB. Several applications of this software are reported in literature [29][30][31][32]. This software tool has a inherited capacity to avoid bloat problem by imposing restrictions on maximum value of parameters such as the number of genes, depth of trees and genes, number of nodes per tree, etc.…”

Section: B Genetic Programming (Gp)mentioning

confidence: 99%

Empirical analysis of model selection criteria for genetic programming in modeling of time series system

Garg

Sriram

Tai

2013

2013 IEEE Conference on Computational Intelligence for Financial Engineering &Amp; Economics (CIFEr)

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Genetic programming (GP) and its variants have been extensively applied for modeling of the stock markets. To improve the generalization ability of the model, GP have been hybridized with its own variants (gene expression programming (GEP), multi expression programming (MEP)) or with the other methods such as neural networks and boosting. The generalization ability of the GP model can also be improved by an appropriate choice of model selection criterion. In the past, several model selection criteria have been applied. In addition, data transformations have significant impact on the performance of the GP models. The literature reveals that few researchers have paid attention to model selection criterion and data transformation while modeling stock markets using GP. The objective of this paper is to identify the most appropriate model selection criterion and transformation that gives better generalized GP models. Therefore, the present work will conduct an empirical analysis to study the effect of three model selection criteria across two data transformations on the performance of GP while modeling the stock indexed in the New York Stock Exchange (NYSE). It was found that FPE criteria have shown a better fit for the GP model on both data transformations as compared to other model selection criteria.

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Prediction of strain energy-based liquefaction resistance of sand–silt mixtures: An evolutionary approach

Cited by 50 publications

References 44 publications

Evaluation of Liquefaction Potential in Saturated Sand under Different Drainage Boundary Conditions—An Energy Approach

Evaluation of Liquefaction Potential in Saturated Sand under Different Drainage Boundary Conditions—An Energy Approach

Energy-based numerical models for assessment of soil liquefaction

Empirical analysis of model selection criteria for genetic programming in modeling of time series system

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