Explainable Machine Learning-Based Ground Motion Characterization: Evaluating the Role of Geotechnical Variabilities on Response Parameters

Explainable Machine Learning-Based Ground Motion Characterization: Evaluating the Role of Geotechnical Variabilities on Response Parameters | 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 Article Explainable Machine Learning-Based Ground Motion Characterization: Evaluating the Role of Geotechnical Variabilities on Response Parameters Ayele Tesema Chala, Mais Mayassah, Richard Ray, Janko Logar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6382260/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 Accounting for geotechnical property variability is crucial in seismic site response analysis. Traditionally, the influence of each geotechnical property on response parameters is assessed independently. However, this approach limits our understanding of the combined effects of multiple properties on ground response parameters. This study presents a novel, explainable machine learning (ML) based approach to assess the influence of multiple geotechnical property variations on response parameters. Four ML models, namely AdaBoost, Extreme Gradient Boosting (XGBoost), Random Forest Regressor (RFR) and Gradient Boosting Machine (GBM), were developed for predictive models. The input factors were shear wave velocity, plasticity index, soil thickness, input motion intensity and unit weight of the soils. The response parameters were peak ground acceleration (PGA) and peak ground displacement (PGD). Multiple statistical performance metrics were computed to evaluate the performance of the models. The results show the superior prediction performance of the GBM model with low error rates and high agreement index ( AI ), Kling-Gupta efficiency ( KGE ) and coefficient of determination ( \(\:{R}^{2})\) . The output of the GBM model was further analyzed using Shapley Additive exPlanation (SHAP) technique to explain and identify the most significant factors contributing to the predictions. Finally, the model was used to develop user-friendly web-based software to facilitate rapid predictions of PGA and PGD. Physical sciences/Engineering/Civil engineering Earth and environmental sciences/Natural hazards Site response soil variability Explainable machine learning SHAP Gradient boosting machine Peak ground acceleration 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-6382260","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":447557430,"identity":"176fc012-de51-48a5-911a-573660f41521","order_by":0,"name":"Ayele Tesema Chala","email":"data:image/png;base64,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","orcid":"","institution":"Széchenyi Istvan University","correspondingAuthor":true,"prefix":"","firstName":"Ayele","middleName":"Tesema","lastName":"Chala","suffix":""},{"id":447557431,"identity":"5508dc6b-3bcd-4bd2-8cd3-321a25537162","order_by":1,"name":"Mais Mayassah","email":"","orcid":"","institution":"Széchenyi Istvan University","correspondingAuthor":false,"prefix":"","firstName":"Mais","middleName":"","lastName":"Mayassah","suffix":""},{"id":447557432,"identity":"7a60c7dd-64a8-470b-aea4-5bad1fa42755","order_by":2,"name":"Richard Ray","email":"","orcid":"","institution":"Széchenyi Istvan University","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Ray","suffix":""},{"id":447557433,"identity":"bf37c46f-15c6-4844-b85d-6cc396e2c351","order_by":3,"name":"Janko Logar","email":"","orcid":"","institution":"University of Ljubljana","correspondingAuthor":false,"prefix":"","firstName":"Janko","middleName":"","lastName":"Logar","suffix":""}],"badges":[],"createdAt":"2025-04-05 13:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6382260/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6382260/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84254810,"identity":"be5c70ba-f631-45dc-934e-3ef5743274e8","added_by":"auto","created_at":"2025-06-09 19:46:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1138556,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptv1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6382260/v1_covered_dee9d972-64ed-46e2-bbb2-68d954e1ab10.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Explainable Machine Learning-Based Ground Motion Characterization: Evaluating the Role of Geotechnical Variabilities on Response Parameters","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":"Site response, soil variability, Explainable machine learning, SHAP, Gradient boosting machine, Peak ground acceleration","lastPublishedDoi":"10.21203/rs.3.rs-6382260/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6382260/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccounting for geotechnical property variability is crucial in seismic site response analysis. Traditionally, the influence of each geotechnical property on response parameters is assessed independently. However, this approach limits our understanding of the combined effects of multiple properties on ground response parameters. This study presents a novel, explainable machine learning (ML) based approach to assess the influence of multiple geotechnical property variations on response parameters. Four ML models, namely AdaBoost, Extreme Gradient Boosting (XGBoost), Random Forest Regressor (RFR) and Gradient Boosting Machine (GBM), were developed for predictive models. The input factors were shear wave velocity, plasticity index, soil thickness, input motion intensity and unit weight of the soils. The response parameters were peak ground acceleration (PGA) and peak ground displacement (PGD). Multiple statistical performance metrics were computed to evaluate the performance of the models. The results show the superior prediction performance of the GBM model with low error rates and high agreement index (\u003cem\u003eAI\u003c/em\u003e), Kling-Gupta efficiency (\u003cem\u003eKGE\u003c/em\u003e) and coefficient of determination (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}^{2})\\)\u003c/span\u003e\u003c/span\u003e. The output of the GBM model was further analyzed using Shapley Additive exPlanation (SHAP) technique to explain and identify the most significant factors contributing to the predictions. Finally, the model was used to develop user-friendly web-based software to facilitate rapid predictions of PGA and PGD.\u003c/p\u003e","manuscriptTitle":"Explainable Machine Learning-Based Ground Motion Characterization: Evaluating the Role of Geotechnical Variabilities on Response Parameters","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-25 09:13:18","doi":"10.21203/rs.3.rs-6382260/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":"bb9d8d5a-9d03-4679-9d11-c3513d3d42b1","owner":[],"postedDate":"April 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47638885,"name":"Physical sciences/Engineering/Civil engineering"},{"id":47638886,"name":"Earth and environmental sciences/Natural hazards"}],"tags":[],"updatedAt":"2025-06-09T19:38:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-25 09:13:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6382260","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6382260","identity":"rs-6382260","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}