[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124340-en":3,"doc-seo-124340-105":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":20,"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},124340,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Exploring Machine Learning Techniques for Predicting Open Stope Stability in Underground Mining - Evaluating Accuracy and Applicability - Doctor of Philosophy Thesis","Underground mining operations face significant safety risks from constrained spaces, limited ventilation, heavy machinery, explosives, and drilling, as well as geological hazards including rockfalls, collapses, and seismic events. Collapsed and caving openings are especially dangerous because they can trap workers and destabilize surrounding rock masses. This thesis evaluates machine learning approaches for predicting open stope stability by using classification and feature-importance analysis to identify critical factors, support preventive decisions, and improve safety and production efficiency.","Exploring Machine Learning Techniques for Predicting Open Stope Stability in Underground Mining: Evaluating  \nAccuracy and Applicability  \nby  \nAlicja Szmigiel  \nA thesis submitted in partial fulfillment of the requirements for the degree of  \nDoctor of Philosophy  \nin  \nMining Engineering  \nDepartment of Civil and Environmental Engineering University of Alberta  \n©Alicja Szmigiel, 2024  \nABSTRACT  \nUnderground mining operations are inherently dangerous due to a variety offactors present in a mining environment. Firstly, the confined spaces and limited ventilation, the use of heavy machinery, explosives, and drilling equipment poses significant risks to the safety of workers. Moreover, underground mines are susceptible to geological hazards such as rockfalls, collapses, and seismic events.  \nCollapsed and caving openings in underground mining are particularly hazardous due to the potential for catastrophic events. When openings collapse or cave in, they can trap workers underground, leading to injuries, fatalities, and the disruption of rescue operations. Furthermore, collapses can destabilize the surrounding rock mass, leading to further collapses.  \nObserving and assessing the stability of underground openings is important for several reasons. Firstly, it ensures the safety of workers by identifying potential hazards before accidents occur. By monitoring the stability of openings, mining companies can implement preventative measures such as reinforcement and support systems. Additionally, assessing the stability of underground openings allows for informed decision-making regarding mining operations, ensuring the sustainability and efficiency of production while minimizing risks to personnel and equipment.  \nMachine learning methods offer promising solutions to the stability assessment problem in underground mining. Through various techniques such as classification and feature importance analysis, machine learning algorithms can effectively predict and evaluate the stability of underground openings. Classification models can classify openings as stable, unstable, or caved based on input features such as geological characteristics and historical stability data. Feature  \nimportance analysis helps identify critical factors influencing stability, enabling targeted interventions.  \nThis research study presents a comprehensive investigation of various machine learning models applications, aimed to predict the stability of underground mining openings, particularly stopes. Open stopes are integral to underground mining operations, where they serve as excavated voids created during the extraction of mineral resources from underground deposits.  \nChapter 1 of this thesis presents the groundwork by providing a comprehensive overview of the research topic, outlining its primary objectives, the methodology employed, and the structure of the thesis.  \nChapter 2 of this study provides a comprehensive engineering background and overview of open stopes mining operations. The chapter begins with an explanation of the terminology associated with mining methods. Moreover, the chapter elaborates on the most popular methods used for rock mass classification.  \nIn Chapter 3, an extensive literature review of contemporary methods for assessing the stability of open stopes is presented. This review presents a diverse range of approaches, including empirical methods, statistical analyses, and applications of machine learning techniques, which have been proposed by various researchers to address the challenge of evaluating stope stability.  \nChapters 4, 5, and 6 present the results of various machine learning models, that were developed to predict the stability of open stopes. Chapter 4 utilizes a Potvin database, where stability number N’ and shape factor HR of each historical case were used, and each case had a stability assessment assigned. Random Forest (RF) and Logistic Regression models were employed, evaluated, and compared to achieve the most ","cbCaikstBfjGD9wU","https://ap.wps.com/l/cbCaikstBfjGD9wU","pdf",3596465,1,206,"English","en",105,"# Abstract\n# Preface\n# Chapter 1 - Research Overview\n# Chapter 2 - Engineering Background\n## Mining method terminology and rock mass classification\n# Chapter 3 - Literature Review\n# Chapters 4-6 - Machine Learning Models and Results\n## Potvin database: Random Forest and Logistic Regression\n## Potvin database: Artificial Neural Network development and feature importance\n## Larger literature database: model comparison and key feature analysis","[{\"question\":\"Why is assessing open stope stability critical in underground mining?\",\"answer\":\"It identifies hazards before accidents, enabling reinforcement and support measures. It also supports informed decisions that reduce risk to personnel and equipment while maintaining efficient, sustainable production.\"},{\"question\":\"What machine learning approaches are used to predict stope stability in this thesis?\",\"answer\":\"The study applies classification models to label openings as stable, unstable, or caved. It also uses feature importance analysis to determine which parameters most influence stability.\"},{\"question\":\"How are the models evaluated and compared across the thesis chapters?\",\"answer\":\"Chapter 4 compares Random Forest and Logistic Regression models using the Potvin database. Chapter 5 develops an Artificial Neural Network with parameters forming the stability number N’, then performs feature importance analysis. Chapter 6 analyzes a larger literature database and compares multiple machine learning models while identifying the most important features for each.\"}]","Exploring Machine Learning Techniques for Predicting Open Stope Stability in Underground Mining - Evaluating Accuracy and Applicability - Doctor of Philosophy Thesis | PDF",1785821713,519,{"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},"exploring-machine-learning-techniques-for-predicting-open-stope-stability-in-underground-mining-evaluating-accuracy-and-applicability-doctor-of-philosophy-thesis","",{"@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/exploring-machine-learning-techniques-for-predicting-open-stope-stability-in-underground-mining-evaluating-accuracy-and-applicability-doctor-of-philosophy-thesis/124340/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is assessing open stope stability critical in underground mining?","Question",{"text":75,"@type":76},"It identifies hazards before accidents, enabling reinforcement and support measures. It also supports informed decisions that reduce risk to personnel and equipment while maintaining efficient, sustainable production.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning approaches are used to predict stope stability in this thesis?",{"text":80,"@type":76},"The study applies classification models to label openings as stable, unstable, or caved. It also uses feature importance analysis to determine which parameters most influence stability.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the models evaluated and compared across the thesis chapters?",{"text":84,"@type":76},"Chapter 4 compares Random Forest and Logistic Regression models using the Potvin database. Chapter 5 develops an Artificial Neural Network with parameters forming the stability number N’, then performs feature importance analysis. 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