In this study,twelve machine learning(ML)techniques are used to accurately estimate the safety factor of rock slopes(SFRS).The dataset used for developing these models consists of 344 rock slopes from various open-pit...In this study,twelve machine learning(ML)techniques are used to accurately estimate the safety factor of rock slopes(SFRS).The dataset used for developing these models consists of 344 rock slopes from various open-pit mines around Iran,evenly distributed between the training(80%)and testing(20%)datasets.The models are evaluated for accuracy using Janbu's limit equilibrium method(LEM)and commercial tool GeoStudio methods.Statistical assessment metrics show that the random forest model is the most accurate in estimating the SFRS(MSE=0.0182,R2=0.8319)and shows high agreement with the results from the LEM method.The results from the long-short-term memory(LSTM)model are the least accurate(MSE=0.037,R2=0.6618)of all the models tested.However,only the null space support vector regression(NuSVR)model performs accurately compared to the practice mode by altering the value of one parameter while maintaining the other parameters constant.It is suggested that this model would be the best one to use to calculate the SFRS.A graphical user interface for the proposed models is developed to further assist in the calculation of the SFRS for engineering difficulties.In this study,we attempt to bridge the gap between modern slope stability evaluation techniques and more conventional analysis methods.展开更多
论文提出基于改进统计学独立性的连续属性值划分方法 APA-SI(Attributes Partition Approach based on Statistical Independence)。APA-SI方法基于统计学法则,既考虑到了两个合并区间的变化的自由度的影响,同时还考虑到数据分配时候变...论文提出基于改进统计学独立性的连续属性值划分方法 APA-SI(Attributes Partition Approach based on Statistical Independence)。APA-SI方法基于统计学法则,既考虑到了两个合并区间的变化的自由度的影响,同时还考虑到数据分配时候变化的影响。通过模拟实验,以银行金融行业的信用风险评估应用作为示例评估连续属性值的划分方法 APA-SI的效果。实验结果表明论文的APA-SI划分方法在C4.5决策树算法、朴素贝叶斯算法、SVM算法中可以完成连续属性值的划分,比已经有的EFD、MDLP、Extended Chi2等方法划分精度要好。展开更多
基金supported via funding from Prince Satam bin Abdulaziz University project number (PSAU/2024/R/1445)The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through large Group Research Project (Grant No.RGP.2/357/44).
文摘In this study,twelve machine learning(ML)techniques are used to accurately estimate the safety factor of rock slopes(SFRS).The dataset used for developing these models consists of 344 rock slopes from various open-pit mines around Iran,evenly distributed between the training(80%)and testing(20%)datasets.The models are evaluated for accuracy using Janbu's limit equilibrium method(LEM)and commercial tool GeoStudio methods.Statistical assessment metrics show that the random forest model is the most accurate in estimating the SFRS(MSE=0.0182,R2=0.8319)and shows high agreement with the results from the LEM method.The results from the long-short-term memory(LSTM)model are the least accurate(MSE=0.037,R2=0.6618)of all the models tested.However,only the null space support vector regression(NuSVR)model performs accurately compared to the practice mode by altering the value of one parameter while maintaining the other parameters constant.It is suggested that this model would be the best one to use to calculate the SFRS.A graphical user interface for the proposed models is developed to further assist in the calculation of the SFRS for engineering difficulties.In this study,we attempt to bridge the gap between modern slope stability evaluation techniques and more conventional analysis methods.
文摘论文提出基于改进统计学独立性的连续属性值划分方法 APA-SI(Attributes Partition Approach based on Statistical Independence)。APA-SI方法基于统计学法则,既考虑到了两个合并区间的变化的自由度的影响,同时还考虑到数据分配时候变化的影响。通过模拟实验,以银行金融行业的信用风险评估应用作为示例评估连续属性值的划分方法 APA-SI的效果。实验结果表明论文的APA-SI划分方法在C4.5决策树算法、朴素贝叶斯算法、SVM算法中可以完成连续属性值的划分,比已经有的EFD、MDLP、Extended Chi2等方法划分精度要好。
