Blast Toes Volume Estimation for Post-Blast Efficiency: A Comparative Analysis of hybrid ensemble learning, voting, and base AI-algorithms

Blast Toes Volume Estimation for Post-Blast Efficiency: A Comparative Analysis of hybrid ensemble learning, voting, and base AI-algorithms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Blast Toes Volume Estimation for Post-Blast Efficiency: A Comparative Analysis of hybrid ensemble learning, voting, and base AI-algorithms Esma Kahraman, Blessing Olamide Taiwo, Shahab Hosseini, Yewuhalashet Fissha, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4014302/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study compares base, hybrid, and voting modeling techniques to predict blast toe volume size. The investigation integrates independent models, explores synergies in hybrid approaches, and optimizes accuracy through ensemble voting to offer comprehensive knowledge and more reliable forecasts for blast toe volume estimation in various design. 457 blasting was investigated and data was collected at Anguran lead and zinc mine in Iran. Nine model accuracy indices were used to compare the algorithm's prediction accuracy. The study indicates a significant relationship between toe volume size and explosive charge per delay, as demonstrated by multicollinearity, Spearman, and Kendall correlation analyses. The analysis of the model showed that Light Gradient Boosting Machine (LightGBM) achieved the highest accuracy compared to the other 8 conventional models, with correlation coefficients (R2) of 0.9004 and 0.8625 for the training and testing datasets, respectively. The Hybrid 6 model, which combines LightGBM and CART algorithms, achieved the highest R2 scores of 0.9473 in the training phase and 0.9467 in the testing phase. The Voting 8 model, consisting of LightGBM, GBM, DT, ET, RF, CatBoost, CART, AdaBoost, and XGBoost, had the greatest R2 scores of 0.9876 and 0.97265 in both the training and testing stages. The voting models can reliably forecast toe volume resulting from a blast design pattern, thereby providing a novel tool for simulation. Blasting toes volume explosive utilization multicollinearity Ensemble learning voting Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4014302","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276488366,"identity":"d53d5fa5-cd45-4613-94de-1472c5adb780","order_by":0,"name":"Esma Kahraman","email":"","orcid":"","institution":"Cukurova University","correspondingAuthor":false,"prefix":"","firstName":"Esma","middleName":"","lastName":"Kahraman","suffix":""},{"id":276488367,"identity":"d548ee41-4c68-4aef-ab3c-5c47cf0a2ccc","order_by":1,"name":"Blessing Olamide Taiwo","email":"data:image/png;base64,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","orcid":"","institution":"Federal University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Blessing","middleName":"Olamide","lastName":"Taiwo","suffix":""},{"id":276488368,"identity":"7a2629ce-63d1-41dd-b9ae-df1e54612348","order_by":2,"name":"Shahab Hosseini","email":"","orcid":"","institution":"Tarbiat Modares University","correspondingAuthor":false,"prefix":"","firstName":"Shahab","middleName":"","lastName":"Hosseini","suffix":""},{"id":276488369,"identity":"6b4e6bf0-36ca-4c57-9494-91ce45fe05e2","order_by":3,"name":"Yewuhalashet Fissha","email":"","orcid":"","institution":"Akita University","correspondingAuthor":false,"prefix":"","firstName":"Yewuhalashet","middleName":"","lastName":"Fissha","suffix":""},{"id":276488370,"identity":"2bee7b27-850d-4c0c-89ad-e1d4a0a4d167","order_by":4,"name":"Victor Jebutu","email":"","orcid":"","institution":"University of Bolton","correspondingAuthor":false,"prefix":"","firstName":"Victor","middleName":"","lastName":"Jebutu","suffix":""},{"id":276488371,"identity":"624f37f4-5c01-4569-9ac7-65269dc109a7","order_by":5,"name":"Adams Akinlabi","email":"","orcid":"","institution":"Federal University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Adams","middleName":"","lastName":"Akinlabi","suffix":""}],"badges":[],"createdAt":"2024-03-04 20:35:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4014302/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4014302/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53988066,"identity":"0e4c7d7d-c0c9-4a0d-a06a-2be916a92899","added_by":"auto","created_at":"2024-04-03 04:52:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":968994,"visible":true,"origin":"","legend":"","description":"","filename":"FinalManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4014302/v1_covered_dd99e3cd-5fef-4cb1-a8e5-37f1303aa0cd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Blast Toes Volume Estimation for Post-Blast Efficiency: A Comparative Analysis of hybrid ensemble learning, voting, and base AI-algorithms","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Blasting, toes volume, explosive utilization, multicollinearity, Ensemble learning voting","lastPublishedDoi":"10.21203/rs.3.rs-4014302/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4014302/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study compares base, hybrid, and voting modeling techniques to predict blast toe volume size. The investigation integrates independent models, explores synergies in hybrid approaches, and optimizes accuracy through ensemble voting to offer comprehensive knowledge and more reliable forecasts for blast toe volume estimation in various design. 457 blasting was investigated and data was collected at Anguran lead and zinc mine in Iran. Nine model accuracy indices were used to compare the algorithm's prediction accuracy. The study indicates a significant relationship between toe volume size and explosive charge per delay, as demonstrated by multicollinearity, Spearman, and Kendall correlation analyses. The analysis of the model showed that Light Gradient Boosting Machine (LightGBM) achieved the highest accuracy compared to the other 8 conventional models, with correlation coefficients (R2) of 0.9004 and 0.8625 for the training and testing datasets, respectively. The Hybrid 6 model, which combines LightGBM and CART algorithms, achieved the highest R2 scores of 0.9473 in the training phase and 0.9467 in the testing phase. The Voting 8 model, consisting of LightGBM, GBM, DT, ET, RF, CatBoost, CART, AdaBoost, and XGBoost, had the greatest R2 scores of 0.9876 and 0.97265 in both the training and testing stages. The voting models can reliably forecast toe volume resulting from a blast design pattern, thereby providing a novel tool for simulation.\u003c/p\u003e","manuscriptTitle":"Blast Toes Volume Estimation for Post-Blast Efficiency: A Comparative Analysis of hybrid ensemble learning, voting, and base AI-algorithms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-07 04:30:11","doi":"10.21203/rs.3.rs-4014302/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1974d957-13be-4ae1-b196-d6efa64fe990","owner":[],"postedDate":"March 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-03T04:44:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-07 04:30:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4014302","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4014302","identity":"rs-4014302","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}