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Crack density estimation in rock structures using machine learning techniques 认领 引用
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作者 Junghee Park Hyung-Koo Yoon 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第7期5076-5090,共15页
Crack density is a key quantitative indicator for assessing the fracture state and stability of rock structures.However,direct fieldmeasurement is often impractical due to time,cost,and accessibility constraints,neces... Crack density is a key quantitative indicator for assessing the fracture state and stability of rock structures.However,direct fieldmeasurement is often impractical due to time,cost,and accessibility constraints,necessitating alternative predictive approaches.This study aims to estimate rock crack density using machine learning techniques with input features based on physical properties consistent with the Biot theory.Rock samples from a tunnel site were categorized into fivemineralogical groups–albite,quartz,biotite,calcite,and chlorite-via X-ray diffraction(XRD).To simulate varying fracture states,samples were artificiallyweathered through cycles of chemical treatment with saline water and slake durability testing.ML models were trained to predict crack density using measured physical properties,yielding R2 values from 0.03 to 0.98 depending on the mineral group.To enhance performance under data-sparse conditions,an oversampling algorithm was applied,resulting in improved R2 values exceeding 0.9 across all groups.In addition,feature importance analysis was conducted to identify practical input parameters.Results indicate that compressional and shear wave velocities are among the most influentialpredictors,enabling accurate and efficientcrack density estimation.This study demonstrates the potential for using minimal,measurable parameters in conjunction with ML algorithms to assess rock fracture conditions reliably,offering a practical tool for stability evaluation in construction environments. 展开更多
关键词 Crack density Machine learning Oversampling algorithm Tunnel Weathering
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Employee Attrition Classification Model Based on Stacking Algorithm 认领 引用
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作者 CHEN Yanming LIN Xinyu ZHAN Kunye 《Psychology Research》 2023年第6期279-285,共7页
This paper aims to build an employee attrition classification model based on the Stacking algorithm.Oversampling algorithm is applied to address the issue of data imbalance and the Randomforest feature importance rank... This paper aims to build an employee attrition classification model based on the Stacking algorithm.Oversampling algorithm is applied to address the issue of data imbalance and the Randomforest feature importance ranking method is used to resolve the overfitting problem after data cleaning and preprocessing.Then,different algorithms are used to establish classification models as control experiments,and R-squared indicators are used to compare.Finally,the Stacking algorithm is used to establish the final classification model.This model has practical and significant implications for both human resource management and employee attrition analysis. 展开更多
关键词 employee attrition classification model machine learning ensemble learning oversampling algorithm Randomforest stacking algorithm
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