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Rock Strength Estimation Using Several tree-Based ML Techniques

Computer Modeling in Engineering & Sciences 2022年第12期133卷 799-824页

作者： Zida Liu Danial Jahed Armaghani Pouyan Fakharian Diyuan Li Dmitrii Vladimirovich Ulrikh Natalia Nikolaevna Orekhova Khaled Mohamed Khedher School of Resources and Safety Engineering Central South UniversityChangsha410083China Department of Urban Planning Engineering Networks and SystemsInstitute of Architecture and ConstructionSouth Ural State UniversityChelyabinsk454080Russia Faculty of Civil Engineering Semnan UniversitySemnan35131-19111Iran Department of Geology Mine Surveying and Mineral ProcessingNosov Magnitogorsk State Technical UniversityMagnitogorsk455000Russia Department of Civil Engineering College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Department of Civil Engineering High Institute of Technological StudiesMrezgua University CampusNabeul8000Tunisia

The uniaxial compressive strength(UCS)of rock is an essential property of rock material in different relevant applications,such as rock slope,tunnel construction,and foundation.It takes enormous time and effort to obtain the UCS values directly in the laboratory.Accordingly,an indirect determination of UCS through conducting several rock index tests that are easy and fast to carry out is of interest and importance.This study presents powerful boosting trees evaluation framework,i.e.,adaptive boosting machine,extreme gradient boosting machine(XGBoost),and category gradient boosting machine,for estimating the UCS of sandstone.Schmidt hammer rebound number,P-wave velocity,and point load index were chosen as considered factors to forecast UCS values of sandstone samples.Taylor diagrams and five regression metrics,including coefficient of determination(R2),root mean square error,mean absolute error,variance account for,and A-20 index,were used to evaluate and compare the performance of these boosting trees.The results showed that the proposed boosting trees are able to provide a high level of prediction capacity for the prepared database.In particular,itwas worth noting that XGBoost is the best model to predict sandstone strength and it achieved 0.999 training R^(2) and 0.958 testing R^(2).The proposed model had more outstanding capability than neural network with optimization techniques during training and testing phases.The performed variable importance analysis reveals that the point load index has a significant influence on predicting UCS of sandstone.

关键词： Uniaxial compressive strength rock index tests machine learning techniques boosting tree

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Risk Prediction of Aortic Dissection Operation Based on boosting trees

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Computers, Materials & Continua 2021年第11期69卷 2583-2598页

作者： Ling Tan Yun Tan Jiaohua Qin Hao Tang Xuyu Xiang Dongshu Xie Neal N.Xiong The Second Xiangya Hospital of Central South University Changsha410011HunanChina Central South University of Forestry&Technology Changsha410004HunanChina Northeastern State University Tahlequah74464OKUSA

During the COVID-19 pandemic,the treatment of aortic dissection has faced additional challenges.The necessary medical resources are in serious shortage,and the preoperative waiting time has been significantly prolonged due to the requirement to test for COVID-19 infection.In this work,we focus on the risk prediction of aortic dissection surgery under the influence of the COVID-19 pandemic.A general scheme of medical data processing is proposed,which includes five modules,namely problem definition,data preprocessing,data mining,result analysis,and knowledge application.Based on effective data preprocessing,feature analysis and boosting trees,our proposed fusion decision model can obtain 100%accuracy for early postoperative mortality prediction,which outperforms machine learning methods based on a single model such as LightGBM,XGBoost,and CatBoost.The results reveal the critical factors related to the postoperative mortality of aortic dissection,which can provide a theoretical basis for the formulation of clinical operation plans and help to effectively avoid risks in advance.

关键词： Risk prediction aortic dissection COVID-19 postoperative mortality boosting tree

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