Prediction of mode I fracture toughness of rock using linear multiple regression and gene expression programming

Afrasiabian, Bijan; Eftekhari, M.

doi:10.1016/j.jrmge.2022.03.008

Cited by 36 publications

(9 citation statements)

References 59 publications

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“…Typically, genes are split between a "head" (encoded function) and a "tail" (non-encoded function). Variables, functions, and constants all point to the encoded function, whereas only constants point to the non-encoded one [47]. In case where the terminal symbols in the gene's head are insufficient to encode a function, the constants and variables in the gene's tail are sometimes employed as supplemental terminal symbols [48].…”

Section: Overview Of Gepmentioning

confidence: 99%

Exploring the Potential of Machine Learning in Stochastic Reliability Modelling for Reinforced Soil Foundations

Raja,

Abdoun,

El-Sekelly

2024

Buildings

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This study introduces a novel application of gene expression programming (GEP) for the reliability analysis (RA) of reinforced soil foundations (RSFs) based on settlement criteria, addressing a critical gap in sustainable construction practices. Based on the principles of probability and statistics, the soil uncertainties were mapped using the first-order second-moment (FOSM) approach. The historical data generated via a parametric study on a validated finite element numerical model were used to train and validate the GEP models. Among the ten developed GEP frameworks, the best-performing model, abbreviated as GEP-M9 (R2 = 0.961 and RMSE = 0.049), in the testing phase was used to perform the RA of an RSF. This model’s effectiveness in RA was affirmed through a comprehensive evaluation, including parametric sensitivity analysis and validation against two independent case studies. The reliability index (β) and probability of failure (Pf) were determined across various coefficient of variation (COV) configurations, underscoring the model’s potential in civil engineering risk analysis. The newly developed GEP model has shown considerable potential for analyzing civil engineering construction risk, as shown by the experimental results of varying settlement values.

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Section: Overview Of Gepmentioning

confidence: 99%

Exploring the Potential of Machine Learning in Stochastic Reliability Modelling for Reinforced Soil Foundations

Raja,

Abdoun,

El-Sekelly

2024

Buildings

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“…In the fourth step, a system including numerical components and qualitative variables was determined to control the execution of the model. In the last step, the stopping criterion of the model, which could be the achievement of the desired fit or the maximum number of model executions, was included [3,48,49].…”

Section: Gene Expression Programmingmentioning

confidence: 99%

Evaluation of River Water Quality Index Using Remote Sensing and Artificial Intelligence Models

Najafzadeh

Basirian

2023

Remote Sensing

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To restrict the entry of polluting components into water bodies, particularly rivers, it is critical to undertake timely monitoring and make rapid choices. Traditional techniques of assessing water quality are typically costly and time-consuming. With the advent of remote sensing technologies and the availability of high-resolution satellite images in recent years, a significant opportunity for water quality monitoring has arisen. In this study, the water quality index (WQI) for the Hudson River has been estimated using Landsat 8 OLI-TIRS images and four Artificial Intelligence (AI) models, such as M5 Model Tree (MT), Multivariate Adaptive Regression Spline (MARS), Gene Expression Programming (GEP), and Evolutionary Polynomial Regression (EPR). In this way, 13 water quality parameters (WQPs) (i.e., Turbidity, Sulfate, Sodium, Potassium, Hardness, Fluoride, Dissolved Oxygen, Chloride, Arsenic, Alkalinity, pH, Nitrate, and Magnesium) were measured between 14 March 2021 and 16 June 2021 at a site near Poughkeepsie, New York. First, Multiple Linear Regression (MLR) models were created between these WQPs parameters and the spectral indices of Landsat 8 OLI-TIRS images, and then, the most correlated spectral indices were selected as input variables of AI models. With reference to the measured values of WQPs, the WQI was determined according to the Canadian Council of Ministers of the Environment (CCME) guidelines. After that, AI models were developed through the training and testing stages, and then estimated values of WQI were compared to the actual values. The results of the AI models’ performance showed that the MARS model had the best performance among the other AI models for monitoring WQI. The results demonstrated the high effectiveness and power of estimating WQI utilizing a combination of satellite images and artificial intelligence models.

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“…Although some machine learning methods can yield good results and give precise predictions, some methods can be time-consuming and require high-performance computing, making some methods not readily available [4]. Many opt for more straightforward methods, such as gene expression programming (GEP), one of the widely used soft computing methods [35,36]. GEP has gained more attention now as a prediction tool in many civil engineering applications [37].…”

Section: Introductionmentioning

confidence: 99%

“…GEP has gained more attention now as a prediction tool in many civil engineering applications [37]. As an alternative to traditional regression modelling, GEP combines both GA and genetic programming (GP) in which GEP takes advantage of the simplicity of GA-GP and removes their limitations, such as nonlinear configuration in GP, which makes it quite challenging to generate widespread and easy empirical equations [35,38]. In a genotype/phenotype system, GEP uses populations of individuals, and then evaluates them according to the fitness criteria and processes them using one or more genetic operators [39,40].…”

Section: Introductionmentioning

confidence: 99%

“…It is also a more preferred method than other GP algorithms because it has a fixed chromosome structure that makes GEPs more robust [36]. GEP has been successfully implemented to predict the mode I fracture toughness of rock [35], moment redistribution capacity in reinforced concrete beams [37], and compressive strength in geopolymer concrete [38], to name a few. However, GEP applications in reservoir studies, especially in FD evaluation, are still in their infancy.…”

Section: Introductionmentioning

confidence: 99%

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Fracture Density Prediction of Basement Metamorphic Rocks Using Gene Expression Programming

Hasan,

Tóth

2024

Minerals

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Many methods have been developed to detect and predict the fracture properties of fractured rocks. The standard data sources for fracture evaluations are image logs and core samples. However, many wells do not have these data, especially for old wells. Furthermore, operating both methods can be costly, and, sometimes, the data gathered are of bad quality. Therefore, previous research attempted to evaluate fractures indirectly using the widely available conventional well-logs. Sedimentary rocks are widespread and have been studied in the literature. However, fractured reservoirs, like igneous and metamorphic rock bodies, may also be vital since they provide fluid migration pathways and can store some hydrocarbons. Hence, two fractured metamorphic rock bodies are studied in this study to evaluate any difference in fracture responses on well-log properties. Also, a quick and reliable prediction method is studied to predict fracture density (FD) in the case of the unavailability of image logs and core samples. Gene expression programming (GEP) was chosen for this study to predict FD, and ten conventional well-log data were used as input variables. The model produced by GEP was good, with R2 values at least above 0.84 for all studied wells, and the model was then applied to wells without image logs. Both selected metamorphic rocks showed similar results in which the significant parameters to predict FD were the spectral gamma ray, resistivity, and porosity logs. This study also proposed a validation method to ensure that the FD value predictions were consistent using discriminant function analysis. In conclusion, the GEP method is reliable and could be used for FD predictions for basement metamorphic rocks.

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Prediction of mode I fracture toughness of rock using linear multiple regression and gene expression programming

Cited by 36 publications

References 59 publications

Exploring the Potential of Machine Learning in Stochastic Reliability Modelling for Reinforced Soil Foundations

Exploring the Potential of Machine Learning in Stochastic Reliability Modelling for Reinforced Soil Foundations

Evaluation of River Water Quality Index Using Remote Sensing and Artificial Intelligence Models

Fracture Density Prediction of Basement Metamorphic Rocks Using Gene Expression Programming

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