Probabilistic Constrained Physics-Informed Neural Networks for Blasting Vibration: From Mechanism Unveiling to Risk-Based Decision Support

Probabilistic Constrained Physics-Informed Neural Networks for Blasting Vibration: From Mechanism Unveiling to Risk-Based Decision Support | 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 Probabilistic Constrained Physics-Informed Neural Networks for Blasting Vibration: From Mechanism Unveiling to Risk-Based Decision Support Hao Liang, Zengjie Dai, Hongwei Li, Gui Wang, Jihua Qin, Yakun Chu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8417294/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 Blast-induced ground vibrations constitute a critical hazard in open-pit mining, threatening adjacent structures and environments. Current prediction methodologies face limitations regarding data dependency, rock mass heterogeneity characterization, and the absence of robust uncertainty quantification. To address these challenges, this paper proposes a Probabilistic Constrained Physics-Informed Neural Network (PC-PINN). Embedded within a Bayesian deep learning framework, the model integrates the cylindrically symmetric damped wave equation via a dual-branch architecture to simultaneously perform vibration prediction and uncertainty quantification. An adaptive weighting strategy is employed to balance physics-based and data-driven loss components. Validated using 161 field datasets from the Kemerburgaz quarry, Turkey, the model incorporates seven critical parameters identified via feature selection, effectively synergizing physical mechanisms with data adaptability. Furthermore, SHAP analysis elucidates the coupled influence of key parameters to guide design adjustments. Ablation studies confirm the architecture's efficacy, achieving an \((R^2)\) of 0.92 and significantly outperforming classical machine learning algorithms in error minimization. Engineering case applications demonstrate high predictive accuracy ( \((R^2=0.91)\) ), supporting safety control in complex rock masses. Notably, the integrated parameter optimization framework reduces engineering risk probability by over 5%, providing a quantitative decision-making basis for hazard prevention and cost optimization. Probabilistic Constrained Physics-Informed Neural Networks Blasting vibration prediction Bayesian deep learning Uncertainty quantification Interpretability analysis Parameter optimization 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-8417294","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":564897240,"identity":"d73cb838-41fc-42e2-880b-6366757e2c82","order_by":0,"name":"Hao 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Current prediction methodologies face limitations regarding data dependency, rock mass heterogeneity characterization, and the absence of robust uncertainty quantification. To address these challenges, this paper proposes a Probabilistic Constrained Physics-Informed Neural Network (PC-PINN). Embedded within a Bayesian deep learning framework, the model integrates the cylindrically symmetric damped wave equation via a dual-branch architecture to simultaneously perform vibration prediction and uncertainty quantification. An adaptive weighting strategy is employed to balance physics-based and data-driven loss components. Validated using 161 field datasets from the Kemerburgaz quarry, Turkey, the model incorporates seven critical parameters identified via feature selection, effectively synergizing physical mechanisms with data adaptability. Furthermore, SHAP analysis elucidates the coupled influence of key parameters to guide design adjustments. Ablation studies confirm the architecture's efficacy, achieving an \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((R^2)\\)\u003c/span\u003e\u003c/span\u003e of 0.92 and significantly outperforming classical machine learning algorithms in error minimization. Engineering case applications demonstrate high predictive accuracy (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((R^2=0.91)\\)\u003c/span\u003e\u003c/span\u003e), supporting safety control in complex rock masses. Notably, the integrated parameter optimization framework reduces engineering risk probability by over 5%, providing a quantitative decision-making basis for hazard prevention and cost optimization.\u003c/p\u003e","manuscriptTitle":"Probabilistic Constrained Physics-Informed Neural Networks for Blasting Vibration: From Mechanism Unveiling to Risk-Based Decision Support","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-24 06:27:19","doi":"10.21203/rs.3.rs-8417294/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":"2283af47-3bbb-47d0-9490-45b0cb3a7f1f","owner":[],"postedDate":"December 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-27T18:53:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-24 06:27:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8417294","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8417294","identity":"rs-8417294","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}