Machine learning models to estimate the elastic modulus of weathered magmatic rocks

Ceryan, Nurcihan; Özkat, Erkan Caner; Can, Nuray Korkmaz; Ceryan, Şener

doi:10.1007/s12665-021-09738-9

Cited by 20 publications

(5 citation statements)

References 112 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For this purpose, initially, the dataset was randomly separated into 75% training dataset and 25% testing dataset. The training dataset was utilized to establish five ML models, namely Support Vector Machine (SVM) [23], Gaussian Process Regression (GPR) [24], k-Nearest Neighbors (kNN) [25], Random Forrest (RF) [26], and Multilayer Perceptron (MLP) [27]. The test dataset was deployed to analyse the performance of the models developed.…”

Section: Model Developmentmentioning

confidence: 99%

Machine Learning-Driven Microchannel Geometry and Flow Dimensionless Parameters Optimization for Maximizing Heat Transfer

Ozkat,

Markal,

Avci

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Flow boiling in microchannel heat sinks provides superior cooling capacity and high heat transfer rates thanks to their favorable surface area-to-volume ratio and the utilization of the coolant’s latent heat. Despite its advantages, the transition of flow boiling in microchannel heat sinks from the laboratory to commercial use faces significant challenges. One key challenge is the lack of established methods for accurately estimating the heat transfer coefficient. Additionally, there has been notable neglect in addressing the optimization problem for finding the best flow conditions to maximize the heat transfer coefficient. Therefore in this study, five machine learning models (SVM, GPR, kNN, RF, and MLP) were initially developed to estimate the heat transfer coefficient. Performance evaluation showed that the MLP model outperformed the others based on R2, RMSE, and MAPE metrics. Subsequently, an optimization problem was formulated to determine the optimum microchannel dimensions and flow condition via dimensionless numbers that would maximize the heat transfer coefficient. The optimization results indicated microchannel dimensions of 300 µm in width (w) and 60 µm in height (h) as well as dimensionless numbers, namely Boiling number (Bo) of 0.000103, Weber number (We) of 0.1012, Reynolds number (Re) of 65.278, vapor quality (χe) of 0.0104, and Prandtl number (Pr) of 2.3757. In these optimized conditions, the heat transfer coefficient reached 34.497 kW/m2K.

show abstract

Section: Model Developmentmentioning

confidence: 99%

Machine Learning-Driven Microchannel Geometry and Flow Dimensionless Parameters Optimization for Maximizing Heat Transfer

Ozkat,

Markal,

Avci

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Under the framework of SVR, Babanouri and Fattahi [67] proposed a new shear constitutive model of rock discontinuity. Ceryan et al [68] developed an SVR model to predict the elastic modulus of rock materials with different degrees of weathering. Recently, Xu et al [69] used SVR to study multiple geomechanical properties of rock materials.…”

Section: References Shear Strength Model Peak Dilation Anglementioning

confidence: 99%

Hybrid Machine-Learning-Based Prediction Model for the Peak Dilation Angle of Rock Discontinuities

Xie,

Yao,

Yan

et al. 2023

Materials

View full text Add to dashboard Cite

The peak dilation angle is an important mechanical feature of rock discontinuities, which is significant in assessing the mechanical behaviour of rock masses. Previous studies have shown that the efficiency and accuracy of traditional experimental methods and analytical models in determining the shear dilation angle are not completely satisfactory. Machine learning methods are popular due to their efficient prediction of outcomes for multiple influencing factors. In this paper, a novel hybrid machine learning model is proposed for predicting the peak dilation angle. The model incorporates support vector regression (SVR) techniques as the primary prediction tools, augmented with the grid search optimization algorithm to enhance prediction performance and optimize hyperparameters. The proposed model was employed on eighty-nine datasets with six input variables encompassing morphology and mechanical property parameters. Comparative analysis is conducted between the proposed model, the original SVR model, and existing analytical models. The results show that the proposed model surpasses both the original SVR model and analytical models, with a coefficient of determination (R2) of 0.917 and a mean absolute percentage error (MAPE) of 4.5%. Additionally, the study also reveals that normal stress is the most influential mechanical property parameter affecting the peak dilation angle. Consequently, the proposed model was shown to be effective in predicting the peak dilation angle of rock discontinuities.

show abstract

“…This method allows for the creation of nearly all possible uniaxial stress-strain curve scenarios, enabling fully customizable mechanical behaviors. Furthermore, there have been reports in the literature on the application of machine learning algorithms in predicting the elastic modulus and ultimate stress [24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Machine learning accelerated the prediction of mechanical properties of copper modified by TMDs based on molecular dynamics simulation

Wang,

Gao,

Zhao

et al. 2024

Phys. Scr.

View full text Add to dashboard Cite

In this study, we constructed a dataset of elastic modulus and ultimate stress for copper material enhanced by Transition Metal Dichalcogenides (TMDs) through Molecular Dynamics (MD) simulations. Subsequently, leveraging chemical insights, we selected appropriate descriptors and established machine learning prediction models for elastic modulus and ultimate stress, respectively. Finally, the performance of the machine learning models was evaluated using a test set. The results demonstrate excellent performance of the machine learning models in predicting material properties. This work presents a novel approach for efficient material screening, demonstrating the synergy between MD simulations and machine learning in advancing materials research and intelligent material selection platforms.

show abstract

Machine learning models to estimate the elastic modulus of weathered magmatic rocks

Cited by 20 publications

References 112 publications

Machine Learning-Driven Microchannel Geometry and Flow Dimensionless Parameters Optimization for Maximizing Heat Transfer

Machine Learning-Driven Microchannel Geometry and Flow Dimensionless Parameters Optimization for Maximizing Heat Transfer

Hybrid Machine-Learning-Based Prediction Model for the Peak Dilation Angle of Rock Discontinuities

Machine learning accelerated the prediction of mechanical properties of copper modified by TMDs based on molecular dynamics simulation

Contact Info

Product

Resources

About