[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125136-en":3,"doc-seo-125136-105":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},125136,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Enhancing rock fragmentation assessment in mine blasting through machine learning algorithms: a practical approach","Blasting optimization in open pit mining relies on accurate prediction of rock fragmentation. Rock fragmentation depends on rock mass characteristics, blast geometry, and explosive properties. This study develops machine learning and deep learning models to predict fragmentation percentage, selecting ten candidate parameters and removing weakly correlated ones to reduce complexity. Using 219 datasets with five input features, the work trains random forest regression, support vector regression, XGBoost, and neural network regression, evaluating performance with R-squared, RMSE, MSE, MAPE, and MAE.","Research  \nEnhancing rock fragmentation assessment in mine blasting through machine learning algorithms: a practical approach  \nAngesom Gebretsadik1,2 · Rahul Kumar3 · Yewuhalashet Fissha2,4 · Yemane Kide2 · Natsuo Okada1 · Hajime Ikeda4 · Arvind Kumar Mishra3 · Danial Jahed Armaghani5 · Yoko Ohtomo1 · Youhei Kawamura1  \nReceived: 10 February 2024 / Accepted: 12 April 2024  \n© The Author(s) 2024 OPEN  \nAbstract  \nThe optimization of blasting operations greatly benefits from the prediction of rock fragmentation. The main factors that affect fragmentation are rock mass characteristics, blast geometry, and explosive properties. This paper is a step towards the implementation of machine learning and deep learning algorithms for predicting the extent of fragmentation (in percentage) in open pit mining. While various parameters can affect rock fragmentation, this study considers ten among them (i.e., spacing, drill hole diameter, burden, average bench height, powder factor, number of holes, charge per delay, uniaxial compressive strength, specific drilling, and stemming) to train and test the models. However, due toa weak correlation with rock fragmentation, drill diameter, average bench height, compressive strength, stemming, and charge per delay are eliminated to reduce model complexity. A total of 219 data sets having five input features including the number of holes, spacing, burden, specific drilling, and powder factor are used to develop the models. Machine learning models (random forest regression, support vector regression, and XG boost), as well as a deep learning model (neural network regression), are applied to develop a practical way that can optimize the prediction of fragmentation. This study employs performance measures such as R-squared, RMSE, MSE, MAPE, and MAE. The optimization of the model revealed promising results, indicating that the architecture 5-64-32-16-1 exhibits strong performance. Specifically, the model achieved mean squared error (MSE) values of 41.32 and 28.59 on the training and test datasets, respectively. The R2 value for both training and test is 0.83. RFR is also performing well compared to SVR and XG boost with MSE values of 12.37 and 9.89 on training and testing data, [respectively.in](respectively.in) both sets, the R2 value is 94% . Based on permutation importance and shapely plot values, it is observed that the powder factor has the highest impact, while the burden has the lowest impact on fragmentation.  \nArticle Highlights  \n• ML models, including RFR, SVR, XGBoost, and ANN, are used to optimize fragmentation prediction.  \n* Angesom Gebretsadik, [angstg2007@gmail.com](angstg2007@gmail.com); * Yewuhalashet Fissha, [yowagaye@gmail.com](yowagaye@gmail.com); Rahul Kumar, rahulmty389@  \n[gmail.com](gmail.com); Yemane Kide, [yemanekide1@gmail.com](yemanekide1@gmail.com); Natsuo Okada, [natsu0807chi@gmail.com](natsu0807chi@gmail.com); Hajime Ikeda, [ikeda@gipc.akita-u.ac.jp](ikeda@gipc.akita-u.ac.jp);  \nArvind Kumar Mishra, [arvindmishra@iitism.ac.in](arvindmishra@iitism.ac.in); Danial Jahed Armaghani, [danial.jahedarmaghani@uts.edu.au](danial.jahedarmaghani@uts.edu.au); Yoko Ohtomo, ohtomoy@  \n[eng.hokudai.ac.jp](eng.hokudai.ac.jp); Youhei Kawamura, [kawamura@eng.hokudai.ac.jp |](kawamura@eng.hokudai.ac.jp |1Division of Sustainable Resources)[1](kawamura@eng.hokudai.ac.jp |1Division of Sustainable Resources)[Division of Sustainable Resources](kawamura@eng.hokudai.ac.jp |1Division of Sustainable Resources) Engineering, Graduate School of Engineering, Hokkaido University, Kita-13, Nishi-8, Sapporo 060-8628, Japan. 2Department of Mining Engineering, Aksum University, 7080 Aksum, Ethiopia. 3Department of Mining Engineering, IIT (Indian School of Mines) Dhanbad, Dhanbad 826004, India. 4Department of Geosciences, Geotechnology and Materials Engineering for Resources, Graduate School of International Resource Sciences, Akita University, Akita 010-8502, Japan. 5School of Civil and Environmental Engineer","cbCaia2aCqjww6Vz","https://ap.wps.com/l/cbCaia2aCqjww6Vz","pdf",3804647,1,34,"English","en",105,"# Abstract\n# Article Highlights\n# Keywords\n# Abbreviations\n# 1 Introduction","[{\"question\":\"What inputs and models are used to predict rock fragmentation in open pit mining?\",\"answer\":\"The study uses 219 datasets with five input features (number of holes, spacing, burden, specific drilling, and powder factor). It applies random forest regression, support vector regression, XGBoost, and a neural network regression model.\"},{\"question\":\"Why are some parameters eliminated from the model training?\",\"answer\":\"Parameters showing weak correlation with rock fragmentation are removed to reduce model complexity, specifically drill diameter, average bench height, compressive strength, stemming, and charge per delay.\"},{\"question\":\"Which model and variables show the best predictive performance and importance?\",\"answer\":\"The architecture 5-64-32-16-1 shows strong performance with an R2 of 0.83 on both training and test sets. Permutation importance and Shapley plots indicate powder factor has the highest impact, while burden has the lowest impact.\"}]","Enhancing rock fragmentation assessment in mine blasting through machine learning algorithms: a practical approach | PDF",1785896857,86,{"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},"enhancing-rock-fragmentation-assessment-in-mine-blasting-through-machine-learning-algorithms-a-practical-approach","",{"@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/enhancing-rock-fragmentation-assessment-in-mine-blasting-through-machine-learning-algorithms-a-practical-approach/125136/",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 inputs and models are used to predict rock fragmentation in open pit mining?","Question",{"text":75,"@type":76},"The study uses 219 datasets with five input features (number of holes, spacing, burden, specific drilling, and powder factor). It applies random forest regression, support vector regression, XGBoost, and a neural network regression model.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why are some parameters eliminated from the model training?",{"text":80,"@type":76},"Parameters showing weak correlation with rock fragmentation are removed to reduce model complexity, specifically drill diameter, average bench height, compressive strength, stemming, and charge per delay.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model and variables show the best predictive performance and importance?",{"text":84,"@type":76},"The architecture 5-64-32-16-1 shows strong performance with an R2 of 0.83 on both training and test sets. Permutation importance and Shapley plots indicate powder factor has the highest impact, while burden has the lowest impact.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]