Evolutionary-based approaches for determining the deviatoric stress of calcareous sands

Shahnazari, Habib; Tutunchian, Mohammad Amin; Rezvani, Reza; Valizadeh, Fatemeh

doi:10.1016/j.cageo.2012.07.006

Cited by 28 publications

(4 citation statements)

References 21 publications

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“…However, the performance of the model in this approach can be significantly impacted by the choice of data sets [18]. In most published EPR works, statistical analysis is used on the input and output parameters of randomly selected training and validation sets to identify the best representation [e.g., 16,19,20,[33][34][35]. The goal is to ensure that the statistical characteristics of the data in both subsets (training and testing) match each other closely, ensuring that they represent the same statistical population.…”

Section: Evolutionary Polynomial Regression (Epr) Is a Hybrid Regress...mentioning

confidence: 99%

Permeability of tight carbonate rocks from sensitivity-driven Evolutionary Polynomial Regression

Gomes,

Vrugt

et al. 2024

Preprint

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The permeability (K) of tight carbonate rocks is important to maximize the efficiency of hydrocarbon production and overall reservoir management. While such property is crucial for engineering design, conducting experimental tests to determine K can be both time-consuming and expensive. As such, reliable and high-fidelity models derived with soft computing techniques become useful for estimating K. Using a data set containing samples from 130 data points published in the literature, this work developed a sensitivity-driven evolutionary polynomial regression (EPR) model to predict K. The model computes the permeability, log10K (mD), as a function of three explanatory variables: porosity, ϕ (-), formation factor, F (-), and the characteristic pore throat diameter, dPT (m). One unique feature of our approachis that it considers the physical meaning of the variables that influence the investigated phenomenon during the construction of the model. Verification of the methodology was carried out using split-sampling cross-validation. The developed model showed attributes such as parsimony (lower number of parameters and input variables), good predictive capability (accurate tracking observed log10K), generalization ability (preserving physical meaning), and robustness (consistent performance under cross-validation). Sensitivity analysis revealed that the model can adequately simulate the increase in K with increasing ϕ and dPT, as well as its capacity to capture the non-linear relationship between log10K and F. Comparison of simulated K-values with results of models published in the literature, further validated the ability of our optimum EPR model structure. The proposed model shows potential as a promising method to estimate the permeability of tight carbonate rocks.

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Section: Evolutionary Polynomial Regression (Epr) Is a Hybrid Regress...mentioning

confidence: 99%

Permeability of tight carbonate rocks from sensitivity-driven Evolutionary Polynomial Regression

Gomes,

Vrugt

et al. 2024

Preprint

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“…This strategy was originally used for environmental modeling by its developers [22][23][24]. However, because of its superior prediction abilities due to its nature, it has been used in several applications in geotechnical engineering, such as prophesying and assessing foundation settlement [25], compressibility and permeability characteristics of soil [26], sand liquefaction potential [27,28], and stability analysis [29][30][31]. Other researchers have used EPR in various geotechnical engineering problems [32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

An EPR model for predicting the bearing capacity of single and double-strip foundations near earth slope crests

Ismael,

Sulaiman

2024

PLoS ONE

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It is imperative to understand how foundations behave on earthen slopes to accurately predict their allowable carrying capacity in geotechnical engineering. A comprehensive finite element (FE) simulation with PLAXIS 2D was conducted to assess the effects of various parameters on the bearing capacity (BC) of single- and double-strip foundations placed near the earth’s slope crest. The specified parameters include foundation width (B) and depth (Df/B); setback distance between the slope edge and foundation (b/B); soil internal friction (ϕ) and cohesion (c); slope inclination (β); and spacing between foundations (S/B). In addition, the numerically simulated database was used to develop simple mathematical expressions for predicting the capacities in both cases using evolutionary polynomial regression (EPR). The results revealed that the bearing capacity of single- and double-strip foundations increased with an increase in all studied parameters except slope inclination. For single-strip foundations, the outcomes demonstrated that slope inclination has no impact on BC when it is located 6B from the slope edge. However, under interference conditions, the critical center-to-center spacing between foundations is 3–4B, beyond which they behave as individual foundations. Additionally, EPR provides a robust method of predicting the BC of single- and double-strip foundations within slope crests based on the strong correlation of various statistical criteria between simulated and predicted results from training, validation, and testing. Finally, according to sensitivity analysis, in both single and double-strip foundations resting on an earthen slope crest, b/B, B, and ϕ are the most important input parameters that impact the output results.

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“…Traditionally, the EPR framework uses simple genetic algorithm (GA) and linear least-squares (LS) for model structure identification and parameter estimation, respectively. The model structure search strategy using single-objective genetic algorithm (SOGA) has been widely applied (Ahangar-Asr et al, 2010, 2011a,b, 2012El-Baroudy et al, 2010;Faramarzi et al, 2012;Shahin, 2015;Shahnazari et al, 2013). In these approaches, the objective function relies on statistical metrics, such as the minimization of the sum of squared errors (SSE).…”

Section: Introductionmentioning

confidence: 99%

A hybrid multi-step sensitivity-driven evolutionary polynomial regression enables robust model structure selection

Gomes

Vrugt

2022

Engineering Applications of Artificial Intelligence

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Evolutionary-based approaches for determining the deviatoric stress of calcareous sands

Cited by 28 publications

References 21 publications

Permeability of tight carbonate rocks from sensitivity-driven Evolutionary Polynomial Regression

Permeability of tight carbonate rocks from sensitivity-driven Evolutionary Polynomial Regression

An EPR model for predicting the bearing capacity of single and double-strip foundations near earth slope crests

A hybrid multi-step sensitivity-driven evolutionary polynomial regression enables robust model structure selection

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