{"paper_id":"3443545a-511d-494d-9ba3-5663d4e0fadf","body_text":"Data-Driven SHM of a Timber Lamella Dome Using CISMN | 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 Data-Driven SHM of a Timber Lamella Dome Using CISMN Mohsen Mokhtari Kashavar, Yaser Shahbazi, Aynaz Sadeghi, Siamak Pedrammehr This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7711888/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract This study develops and validates a data-driven framework for structural health monitoring of a timber lamella dome, with the objective of predicting three-directional displacements from measured actions. A computational pipeline generates a parametric geometric model in a Formex-based script, translates it to a finite-element model in a parametric environment, and runs code-compliant seismic load cases to produce training data. A multi-objective search explores wide ranges of Load(X) and Load(Y), recording elastic energy in three axes and the corresponding displacements, yielding a diverse dataset of load–response pairs. A Chaos-Integrated Synaptic-Memory Network (CISMN) is trained to map five predictors, namely Load(X)-load in X direction, Load(Y)-load in Y direction, and Elastic energy in z, x, and y, directions to the three displacement components. The learning setup uses robust loss, regularization, and early stopping with an adaptive schedule, and evaluation uses an 80/20 train–validation split in original physical units. Results show high fidelity: overall coefficient of determination near 0.99, near-identity behavior for in-plane components, and small, interpretable deviations for the vertical component confined to the largest responses. Training and validation histories track closely, and residual diagnostics indicate low bias with stable dispersion. The trained model runs quickly and outputs displacements directly in engineering units, enabling threshold-based alarms, trend tracking, and digital-twin updates. The workflow is reproducible and extensible to additional covariates or uncertainty quantification, supporting practical, real-time SHM of lamella domes. Structural Health Monitoring (SHM) lamella dome timber dome ML in SHM Computational SHM Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Jan, 2026 Reviews received at journal 25 Nov, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviews received at journal 20 Oct, 2025 Reviewers agreed at journal 19 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers invited by journal 15 Oct, 2025 Editor assigned by journal 29 Sep, 2025 Submission checks completed at journal 29 Sep, 2025 First submitted to journal 25 Sep, 2025 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. 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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-7711888\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":534713775,\"identity\":\"e037b7fa-fe39-4afe-afbe-321c235b7546\",\"order_by\":0,\"name\":\"Mohsen Mokhtari Kashavar\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Tabriz Islamic Art University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Mohsen\",\"middleName\":\"Mokhtari\",\"lastName\":\"Kashavar\",\"suffix\":\"\"},{\"id\":534713776,\"identity\":\"7d366303-bf26-46a4-b923-de89a4467a9e\",\"order_by\":1,\"name\":\"Yaser 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CISMN\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":true,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":true,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"discover-civil-engineering\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [Discover Civil Engineering](https://www.springer.com/journal/44290)\",\"snPcode\":\"44290\",\"submissionUrl\":\"https://submission.nature.com/new-submission/44290\",\"title\":\"Discover Civil 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A computational pipeline generates a parametric geometric model in a Formex-based script, translates it to a finite-element model in a parametric environment, and runs code-compliant seismic load cases to produce training data. A multi-objective search explores wide ranges of Load(X) and Load(Y), recording elastic energy in three axes and the corresponding displacements, yielding a diverse dataset of load\\u0026ndash;response pairs. A Chaos-Integrated Synaptic-Memory Network (CISMN) is trained to map five predictors, namely Load(X)-load in X direction, Load(Y)-load in Y direction, and Elastic energy in z, x, and y, directions to the three displacement components. The learning setup uses robust loss, regularization, and early stopping with an adaptive schedule, and evaluation uses an 80/20 train\\u0026ndash;validation split in original physical units. Results show high fidelity: overall coefficient of determination near 0.99, near-identity behavior for in-plane components, and small, interpretable deviations for the vertical component confined to the largest responses. Training and validation histories track closely, and residual diagnostics indicate low bias with stable dispersion. The trained model runs quickly and outputs displacements directly in engineering units, enabling threshold-based alarms, trend tracking, and digital-twin updates. The workflow is reproducible and extensible to additional covariates or uncertainty quantification, supporting practical, real-time SHM of lamella domes.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Data-Driven SHM of a Timber Lamella Dome Using CISMN\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-10-29 04:12:02\",\"doi\":\"10.21203/rs.3.rs-7711888/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2026-01-23T10:16:25+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-11-25T17:49:15+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"69523460048078284868737069508575104354\",\"date\":\"2025-11-25T14:57:13+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-10-20T11:52:00+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"160393247007935965496212927593614515921\",\"date\":\"2025-10-19T07:38:12+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"195078768507927904303628773276816166416\",\"date\":\"2025-10-15T04:38:57+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-10-15T04:00:32+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-09-29T07:44:46+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-09-29T07:43:56+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Discover Civil Engineering\",\"date\":\"2025-09-25T10:30:02+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"discover-civil-engineering\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [Discover Civil Engineering](https://www.springer.com/journal/44290)\",\"snPcode\":\"44290\",\"submissionUrl\":\"https://submission.nature.com/new-submission/44290\",\"title\":\"Discover Civil Engineering\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Discover Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"438b121f-1c40-4e76-991c-a3644b179eb4\",\"owner\":[],\"postedDate\":\"October 29th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-03-13T07:55:25+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-10-29 04:12:02\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7711888\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7711888\",\"identity\":\"rs-7711888\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}