[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124472-en":3,"doc-seo-124472-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124472,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Predictive Modelling of bridge bearing displacements with Physics-Enhanced Machine Learning (PEML) - environmental effects filtering","Bridge Structural Health Monitoring (SHM) faces difficulty in anomaly identification because environmental and operational variability, such as temperature changes and traffic loads, can mask damage-related signatures. This study builds a predictive model that isolates normal structural responses to enable detection of damage-induced anomalies. Using displacement and temperature sensors, it predicts longitudinal displacements at bridge bearings, treating temperature as the main independent variable and combining it with time to capture nonlinear daily and seasonal cycles. Regression-based methods, including Gaussian Process Regression (GPR), are enhanced through a Physics-Enhanced Machine Learning (PEML) grey-box framework that improves accuracy and interpretability. Validated on real highway viaduct data, the grey-box model remains robust with limited datasets, supporting more reliable SHM and improved bridge safety.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nPredictive Modelling of bridge bearing displacements with PhysicsEnhanced Machine Learning (PEML) environmental effects filtering  \nOriginal  \nPredictive Modelling of bridge bearing displacements with PhysicsEnhanced Machine Learning (PEML) environmental effects filtering / Cianci, Enrico; Civera, Marco; De Biagi, Valerio; Chiaia, Bernardino. -In: CE/PAPERS. -ISSN 2509- 7075. -8:(2025), pp. 156-163. ( EUROSTRUCT 2025 European Association on Quality Control of Bridges and Structures Dublin (IE) 2–5 September 2025) [10 . 1002/cepa.3387] .  \nAvailability:  \nThis version is available at: 11583/3005857 since: 2025-12-14T14:14:02Z  \nPublisher:  \nErnst & Sohn  \nPublished  \nDOI:10.1002/cepa.3387  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \n(Article begins on next page)  \n21 February 2026  \nProceedings  \nin civil engineering  \nORIGINAL ARTICLE  \nPredictive Modelling of bridge bearing displacements with Physics-Enhanced Machine Learning (PEML) environmental effects filtering  \nEnrico Cianci 1 | Marco Civera 1 | Valerio De Biagi 1 | Bernardino Chiaia1  \nCorrespondence  \nDr. Marco Civera Politecnico di Torino Department of Structural, Geotechnical and Building Engineering (DISEG)  \nCorso Duca Degli Abruzzi, 24 10129 Turin  \nEmail: marco.civera@polito. it  \n1 Politecnico di Torino, Department of Structural, Geotechnical and Building Engineering (DISEG) Turin, Italy  \nAbstract  \nIn bridge Structural Health Monitoring (SHM), identifying anomalies is challenging due to environmental and operational variability (EOV), such as temperature changes, traffic loads, and else. This study develops a predictive model to isolate normal structural responses, enabling the detection of damage-induced anomalies. Using displacement and temperature sensors, the model evaluates longitudinal displacements at the bridge bearings. Temperature is the primary independent variable, combined with time, to capture daily and seasonal cycles characterised by nonlinear behaviour. Regression-based Machine Learning algorithms, such as Gaussian Process Regression (GPR), are employed to predict the expected displacements. A Physics-Enhanced Machine Learning (PEML) approach, or grey-box model, integrating physical knowledge with data-driven insights is adopted, improving accuracy and interpretability. Tested on real-world data from a highway viaduct, the greybox model demonstrates superior performance and robustness, even with limited datasets. This confirms the potential of PEML-based approaches for damage assessment with data from static monitoring, paving the way for more reliable SHM systems and enhanced bridge safety.  \nKeywords  \nBridge bearings, displacement transducers, thermal effects, EOVs, SHM, Static Monitoring, Machine Learning, Physics-Enhanced Machine Learning, Grey-Box model  \n1 Introduction can significantly influence structural responses, potentially masking the effects of damage [3] [4] . This presents a  \nBridge bearings play a crucial role in accommodating translational and rotational movements due to factors such as thermal changes and seismic activity. However, if not properly maintained, these components can suffer from structural degradation and lead to localised malfunctioning or even catastrophic failure. While traditional visual inspections have long been the standard for assessing bearings, they often fall short in capturing time-dependent and complex behaviour due to the long time intervals between inspections. This is especially significant for early damage detection when macroscopic effects are barely visible. To overcome this limitation, Structural Health Monitoring (SHM) provides data-driven insights that complement traditional visual assessments [1] . A common goal of SHM is to establish baselines representing the \"normal\" behaviour of the target structure in ","cbCaiaoGlAq18HRU","https://ap.wps.com/l/cbCaiaoGlAq18HRU","pdf",1290089,1,9,"English","en",105,"# Introduction\n## Bridge bearings and SHM challenges\n## Environmental variability and damage masking\n## Temperature-driven quasi-static methods\n## Physics-Enhanced Machine Learning (PEML) and grey-box models","[{\"question\":\"Why is anomaly detection in bridge SHM challenging?\",\"answer\":\"Because environmental and operational variability—such as temperature changes and traffic loads—can obscure damage-related effects.\"},{\"question\":\"What inputs does the predictive model use to estimate bearing displacements?\",\"answer\":\"It uses displacement and temperature sensors, with temperature as the primary independent variable and time to capture daily and seasonal nonlinear cycles.\"},{\"question\":\"How does Physics-Enhanced Machine Learning (PEML) improve the modelling approach?\",\"answer\":\"The PEML grey-box framework integrates physical knowledge with data-driven learning, improving accuracy and interpretability and remaining robust even with limited datasets.\"}]","Predictive Modelling of bridge bearing displacements with Physics-Enhanced Machine Learning (PEML) - 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