[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125553-en":3,"doc-seo-125553-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125553,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Classifying the surrounding rock of tunnel face using machine learning","Accurate classification of tunnel-face surrounding rock is critical for reliable dynamic support during drilling and blasting construction. A machine-learning based automatic classification and dynamic prediction framework is developed from data monitored by a computerized rock drilling trolley. The workflow addresses class imbalance using SMOTE with 500 drilling-parameter samples, selects informative characteristics via random forest importance, and trains an XGBoost model compared with AdaBoost and BP networks. Results indicate 87.5% accuracy under small sample sizes, supporting surrounding rock identification, design interaction, supervision, and quality evaluation for upgrading intelligent tunnel construction.","TYPE Original Research PUBLISHED 17 January 2023 DOI 10.3389/feart.2022.1052117  \nOPEN ACCESS  \nEDITED BY  \nJun Yang,  \nNortheastern University, China  \nREVIEWED BY  \nJixiang Liu,  \nXiamen University, China Wei Lv,  \nWuhan University of Technology, China  \n*CORRESPONDENCE  \nWanqi Wang,  \n[176540570@qq.com](176540570@qq.com)  \nSPECIALTY SECTION  \nThis article was submitted to Geoscience and Society, a section of the journal Frontiers in Earth Science  \nRECEIVED 23 September 2022  \nACCEPTED 31 October 2022  \nPUBLISHED 17 January 2023  \nCITATION  \nSong S, Xu G, Bao L, Xie Y, Lu W, Liu Hand Wang W (2023), Classifying the surrounding rock of tunnel face using machine learning.  \nFront. Earth Sci. 10:1052117 .  \ndoi: 10.3389/feart.2022.1052117  \nCOPYRIGHT  \n© 2023 Song, Xu, Bao, Xie, Lu, Liu and Wang. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nClassifying the surrounding rock of tunnel face using machine learning  \nShubao Song 1, Guangchun Xu 2, Liu Bao 1,2, Yalong Xie 1, Wenlong Lu 1, Hongfeng Liu 1 and Wanqi Wang 1*  \n1China Academy of Railway Sciences Corporation Limited, Beijing, China, 2Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China  \nAccurately classifying the surrounding rock of tunnel face is essential. In this paper, we propose a machine learning-based automatic classification and dynamic prediction method of the surrounding rocks of tunnel face using the data monitored by a computerized rock drilling trolley based on the intelligent mechanized construction process for drilling and blasting tunnels. This method provides auxiliary support for the intelligent decision of dynamic support at the construction site. First, this method solves the imbalance in the classification of the surrounding rock samples by constructing the Synthetic Minority Oversampling Technique (SMOTE) algorithm using 500 samples of drilling parameters covering different levels and lithologies of a tunnel. Second, it filters the importance of the characteristic samples based on the random forest method. Third, it uses the XGBoost algorithm to model the processed data and compare it with AdaBoost and BP neural network models. The results show that the XGBoost model achieves a higher accuracy of 87.5% when the sample size is small. Finally, we validate the application scenarios of the above algorithm/model regarding the key aspects of the tunnel construction process, such as surrounding rock identification, design interaction, construction supervision, and quality evaluation, which facilitates the upgrading of intelligent tunnel construction.  \nKEYWORDS  \ntunnel construction, digital twin, SMOTE, drill, machine learning  \n1 Introduction  \nThe common methods of tunnel construction include drilling and blasting, shield construction, and immersed tube construction, among which over 80% of tunnel construction use the drilling and blasting method (Wang, 2010, 2020) . Rock drilling rigs with hydraulic mechanical arms have been used in tunnels since the 1980s, which marks the beginning of mechanized tunnel construction. In the 21st century, as we entered the age of intelligence (Zhao et al., 2017), new opportunities and challenges for the development of technological innovation in railway tunnel construction has emerged (Yang et al., 2022), which has attracted the attention of the world’s leading tunnel construction countries. In the future, the worldwide competition in railway tunnel  \nFrontiers in Earth Science 01 [frontiersin.org](frontiersin.org)  \nconstruction technology level directly depends on the breadth","cbCaiu586WHscWyd","https://ap.wps.com/l/cbCaiu586WHscWyd","pdf",1452498,1,10,"English","en",105,"# Introduction\n## Current tunnel construction methods and the need for intelligent support\n## Role of mechanized drilling data and limitations of discrete sensing","[{\"question\":\"What data source is used to classify the surrounding rock at the tunnel face?\",\"answer\":\"The method uses data monitored by a computerized rock drilling trolley during the intelligent mechanized drilling-and-blasting construction process.\"},{\"question\":\"How does the approach handle class imbalance in surrounding rock samples?\",\"answer\":\"It constructs SMOTE using 500 samples of drilling parameters covering different levels and lithologies.\"},{\"question\":\"Why is the XGBoost model emphasized in the study?\",\"answer\":\"After feature importance filtering and training, the XGBoost model reaches higher accuracy—87.5%—especially when the sample size is small.\"}]","Classifying the surrounding rock of tunnel face using machine learning | PDF",1785899821,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"classifying-the-surrounding-rock-of-tunnel-face-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/classifying-the-surrounding-rock-of-tunnel-face-using-machine-learning/125553/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What data source is used to classify the surrounding rock at the tunnel face?","Question",{"text":75,"@type":76},"The method uses data monitored by a computerized rock drilling trolley during the intelligent mechanized drilling-and-blasting construction process.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the approach handle class imbalance in surrounding rock samples?",{"text":80,"@type":76},"It constructs SMOTE using 500 samples of drilling parameters covering different levels and lithologies.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is the XGBoost model emphasized in the study?",{"text":84,"@type":76},"After feature importance filtering and training, the XGBoost model reaches higher accuracy—87.5%—especially when the sample size is small.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]