[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81874-en":3,"doc-seo-81874-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},81874,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","SPECTRA: A Physics-Informed Digital Twin for Real-Time Structural Anomaly Inference Under Operational Variability","Structural health monitoring shifts from damage detection to real-time decision support for ageing and safety-critical infrastructure, demanding methods that distinguish genuine structural change from benign environmental and operational variability while remaining interpretable for engineers. This paper introduces SPECTRA, a physics-informed eigencompressed digital-twin framework for real-time structural anomaly inference. It integrates a healthy structural twin, eigen-compressed dynamic representation, residual-augmented spectral features, kernel principal component analysis, and a persistent decision rule, inferring anomalies from physics-space disagreement. Across seven numerical benchmarks, SPECTRA detects abrupt, gradual, nonlinear and damping-related damage without persistent false alarms under operational variability alone, enabling reproducible pre-deployment testing for infrastructure twins.","SPECTRA: A PHYSICS-INFORMED DIGITAL TWIN FOR REAL-TIME STRUCTURAL ANOMALY INFERENCE UNDER OPERATIONAL VARIABILITY  \nA PREPRINT  \narXiv :2607 .03446v 1 [ cs .CE] 3 Jul 2026  \n Anshu Sharma  \nDepartment of Civil & Environmental Engineering, University of Strathclyde, Glasgow, UK [anshu.sharma@strath.ac.uk](anshu.sharma@strath.ac.uk)  \n Basuraj Bhowmik*∗  \nDepartment of Civil & Environmental Engineering, University of Strathclyde, Glasgow, UK [basuraj.bhowmik@strath.ac.uk](basuraj.bhowmik@strath.ac.uk)  \nJuly 7, 2026  \nABSTRACT  \nStructural health monitoring is moving from damage detection alone towards real-time decision support for ageing and safety-critical infrastructure. This shift requires monitoring methods that can separate true structural change from benign environmental and operational variability, while remaining interpretable to engineers. This paper presents SPECTRA, a physics-informed eigencompressed digital twin framework for real-time structural anomaly inference. The framework combines a healthy structural twin, eigen-compressed dynamic representation, full-order twin innovation, residual-augmented spectral features, kernel principal component analysis, and a persistent decision rule. The central idea is that structural anomalies are inferred not from statistical features alone, but from disagreement between the measured response and a physics-informed healthy twin. The method is assessed through seven numerical benchmarks: smooth Duffing-type nonlinear drift, sudden stiffness loss, gradual stiffness degradation, bilinear breathing stiffness, environmental and operational variability-confounded local damage, local damping loss, and an operationalonly negative-control case. The results show that SPECTRA detects abrupt, gradual, nonlinear and damping-related damage mechanisms, while avoiding persistent false alarms under operational variability alone. Across the accepted benchmark suite, the persistent decision rule gives zero predamage persistent false alarms, finite detection delay in damage cases, and zero persistent alarmsin the no-damage negative-control case. The framework provides a reproducible route for testing physics-informed anomaly inference before deployment in infrastructure digital twins.  \nKeywords Structural health monitoring · digital twin · kernel principal component analysis · environmental and operational variability · eigen-compressed modelling · anomaly detection · infrastructure monitoring · SPECTRA  \n1 Introduction  \nStructural health monitoring (SHM) aims to support engineering decisions by using measured structural response to infer whether a structure is behaving as expected or whether a structural change has occurred. Classical SHM methods have often been framed as pattern recognition problems, where features are extracted from vibration, strain or displacement data and then used to identify deviations from a reference condition [1, 2] . This view has been highly influential because it provides a clear route from sensing to diagnosis. However, practical infrastructure monitoring requires more than statistical separation between healthy and abnormal feature patterns. The decision must also be physically meaningful, robust to normal variability and interpretable to engineers responsible for safety-critical assets [3] . This requirement becomes more demanding in real-time monitoring, where the decision variable must be  \n∗ Corresponding author  \nupdated as new measurements arrive and where an isolated statistical change should not be treated, on its own, as evidence of structural damage. A persistent difficulty in SHM is that measured structural response changes for many reasons that are not damage. Temperature, humidity, traffic, wind, boundary condition changes, sensor noise and operational loading can all alter the measured response of a structure [4] . The Z24 bridge study remains a widely cited example showing that environmental effects can produce modal changes that are comparable to, or","cbCainsccS11T0CF","https://ap.wps.com/l/cbCainsccS11T0CF","pdf",6213281,6,1,23,"English","en",105,"# Introduction\n## Structural health monitoring and decision support\n## Challenges from environmental and operational variability\n## Digital twin-based monitoring and physics-informed disagreement\n## SPECTRA overview and framework principle","[{\"question\":\"What problem does SPECTRA address in structural health monitoring?\",\"answer\":\"SPECTRA addresses the need for real-time monitoring that can separate true structural change from environmental and operational variability, without relying on statistical novelty alone.\"},{\"question\":\"How does SPECTRA infer structural anomalies?\",\"answer\":\"SPECTRA infers anomalies using disagreement between measured structural response and a healthy physics-informed digital twin, treating the difference as a twin-innovation signal.\"},{\"question\":\"How was SPECTRA evaluated, and what are the key results?\",\"answer\":\"The framework was assessed through seven numerical benchmarks including nonlinear drift, stiffness changes, damping loss, and variability-confounded local damage cases. Results report zero predamage persistent false alarms under the accepted benchmark suite, finite detection delays in damage scenarios, and zero persistent alarms in the no-damage negative-control case.\"}]","SPECTRA: A Physics-Informed Digital Twin for Real-Time Structural Anomaly Inference Under Operational Variability | PDF",1784176797,58,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"spectra-a-physics-informed-digital-twin-for-real-time-structural-anomaly-inference-under-operational-variability","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/spectra-a-physics-informed-digital-twin-for-real-time-structural-anomaly-inference-under-operational-variability/81874/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-04","2026-07-16",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What problem does SPECTRA address in structural health monitoring?","Question",{"text":77,"@type":78},"SPECTRA addresses the need for real-time monitoring that can separate true structural change from environmental and operational variability, without relying on statistical novelty alone.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does SPECTRA infer structural anomalies?",{"text":82,"@type":78},"SPECTRA infers anomalies using disagreement between measured structural response and a healthy physics-informed digital twin, treating the difference as a twin-innovation signal.",{"name":84,"@type":75,"acceptedAnswer":85},"How was SPECTRA evaluated, and what are the key results?",{"text":86,"@type":78},"The framework was assessed through seven numerical benchmarks including nonlinear drift, stiffness changes, damping loss, and variability-confounded local damage cases. 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