[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124809-en":3,"doc-seo-124809-105":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},124809,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Enhancing Prediction of Blast-Induced Ground Vibrations through Machine Learning - Master’s thesis in Computer Science","This Master’s thesis investigates the use of Machine Learning to predict blast-induced ground vibrations in mining, aiming to exceed the precision of an industry-standard empirical regression model. A Deep Neural Network model is developed to incorporate a wider range of influencing variables than the baseline approach. Model performance is assessed with three statistical criteria: coefficient of correlation (R2), mean square error (MSE), and mean absolute error (MAE). Results show the DNN achieves markedly higher predictive accuracy, with R2 of 0.94, MSE of 0.94, and MAE of 0.60, substantially improving upon the industry-standard values.","Faculty of Science and Technology Department of Computer Science  \nEnhancing Prediction of Blast-Induced Ground Vibrations through Machine Learning  \nGuro Jansrud Master‘s thesis in Computer Science-INF-3981  \nThis thesis document was typeset using the UiT Thesis LaTEX Template.© 2024 – [http://github.com/egraff/uit-thesis](http://github.com/egraff/uit-thesis)  \n“I particularly hope that you will conclude the merit of the ideas I present  \noutweigh my defects as a writer.”  \n–Philip A. Fisher  \n“To start something is good, but to finish it is a miracle.”  \n–Richard Strawbridge  \nAbstract  \nThis Master’s thesis investigates the application of Machine Learning (ML) in predicting blast-induced ground vibrations in mining, with the aim of surpassing the precision of the current industry-standard model that utilizes an empirical, regression-based method. The study applied a Deep Neural Network (DNN) model, selected for its capability to consider a broader range of variables than the industry-standard model, leading to significantly enhanced predictive capabilities. The evaluation of these models was conducted using three statistical criteria: coefficient of correlation (R2), mean square error (MSE), and mean absolute error (MAE) .  \nThe key finding is the DNN model’s superior performance, achieving an R2 of 0.94, an MSE of 0.94, and an MAE of 0.60, which represent a significant improvement and reduction over the industry-standard model’s predictive results. Specifically, there is an 84% improvement in the R2 value, an 87% decrease in MSE, and a 71% decrease in MAE compared to the industry-standard model’s R2 of 0.51, MSE of 7.41, and MAE of 2.04 . This marked enhancement in predictive accuracy illustrates the model’s ability to analyze multiple variables concurrently and highlights the potential of AI and ML to improve environmental safety and operational efficiency in the mining industry.  \nAcknowledgment  \nI want to thank my primary supervisor Anne Håkansson for all the help during this autumn, with her insights, structured approach, and invaluable advice. My gratitude also extends to my co-supervisor, Aya Saad, whose expertise and perspectives have significantly enriched this thesis. Their combined feedback and support have been instrumental for shaping this thesis.  \nThank you to all my fellow students at UiT, and to my friends and family for always supporting me, and believing in me, which has been a constant source of strength.  \nLastly, my deepest appreciation is reserved for my partner, for being my biggest supporter. During my toughest times, that must be have been the biggest of challenges. Your presence and support have been a guiding light throughout this journey, and I am immensely grateful for everything you’ve done. This achievement is as much yours as it is mine.  \nContents  \nAbstract iii  \nAcknowledgment v  \nList of Figures xi  \nList of Tables xiii  \n1 Introduction 1  \n1.1 Background And Motivation .................. 3  \n1.2 Problem Definition ....................... 4  \n1.3 Goal .............................. 4  \n1.4 Methodology .......................... 5  \n1.5 Contribution .......................... 6  \n1.6 Stakeholders .......................... 6  \n1.7 Limitations ........................... 7  \n1.8 Outline ............................. 9  \n2 Background 13  \n2.1 Blasting Operations ...................... 13  \n2.1.1 Environmental Consequences ............. 14  \n2.1.2 Controllable And Uncontrollable Blasting Variables . 14  \n2.1.3 Designing A Blast Event ................ 15  \n2.2 Blast-Induced Ground Vibration ................ 17  \n2.2.1 Peak Particle Velocity ................. 17  \n2.2.2 Initial Predictor Of Peak Particle Velocity ....... 18  \n2.2.3 Measurement And Representation .......... 19  \n2.2.4 Multidimensional Characterization .......... 19  \n2.2.5 Regulatory And Industry Standards .......... 20  \n2.3 Industry Standard Prediction Method ............. 20  \n2.3.1 Linear Equation .................... 21  ","cbCaidcY9BywwUY4","https://ap.wps.com/l/cbCaidcY9BywwUY4","pdf",5077172,1,154,"English","en",105,"# Abstract\n# Acknowledgment\n# List of Figures\n# List of Tables\n# Introduction\n## Background And Motivation\n## Problem Definition\n## Goal\n## Methodology\n## Contribution\n## Stakeholders\n## Limitations\n## Outline\n# Background\n## Blasting Operations\n## Blast-Induced Ground Vibration\n## Industry Standard Prediction Method\n# Artificial Neural Network\n## Machine Learning Models\n## Deep Neural Network\n## Related Work\n# Methods And Methodologies\n## Methodological Framework","[{\"question\":\"What is the main objective of the thesis?\",\"answer\":\"To predict blast-induced ground vibrations in mining using Machine Learning while improving accuracy over an existing industry-standard regression approach.\"},{\"question\":\"Which model is proposed and why?\",\"answer\":\"A Deep Neural Network is used because it can account for a broader set of variables than the industry-standard empirical regression model.\"},{\"question\":\"How are the predictions evaluated?\",\"answer\":\"The models are evaluated using R2, MSE, and MAE to measure correlation and prediction errors.\"}]","Enhancing Prediction of Blast-Induced Ground Vibrations through Machine Learning - 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