{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/341922"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/341922","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Structural assessment of bridges using monitoring data","abstract":"Despite decades of research, there has been relatively little industry uptake of structural health monitoring (SHM) technologies and model updating techniques, i.e., the integration of structural monitoring data with structural analysis modelling, in bridge operation and maintenance (O\\&M) activities. Many bridge practitioners question whether these technologies and techniques can provide valuable and reliable information on bridge safety and performance. In the first part of this PhD thesis, a series of industry interviews are used to investigate the reasons behind this limited industry adoption and the disconnects between research and practice regarding bridge monitoring and model updating. It has been found that while most studies in the bridge SHM community have been focused on damage detection, many bridge practitioners are ultimately more interested in bridge capacity assessment, and in particular, the ``margin of capacity'' of their bridge assets (i.e., how much additional live load can be safely placed on a bridge). In addition, bridge assessment remains a key asset management challenge in that there has been a consistent mismatch between the calculated load rating and the actual capacity of bridges. These findings form the basis of the second part of this PhD thesis, which investigates how strain monitoring data may be utilised to improve bridge assessment by (i) better understanding and quantifying commonly made assumptions about structural behaviour to enable more realistic bridge modelling and analysis, and (ii) evaluating in-service structural utilisation to better understand the ``margin of capacity'', which is defined in this PhD study as how much additional live load can be safely placed on a bridge without violating the design performance criteria. First, four common assumptions about structural behaviour in bridge assessment are examined. These are the amount of prestress loss (for prestressed concrete bridges), load distribution characteristics, support boundary conditions and the stiffness contribution of secondary structural elements. Novel or refined methods are developed to enable the evaluation of these structural behaviour characteristics under normal operational conditions. In particular, the methods developed utilises normalised structural response profiles to minimise the effects of uncertainties in estimating material stiffness and load magnitudes. Using these methods, continuous data collection for more automated and reliable evaluation of in-service structural behaviour is made possible. Subsequently, to improve the understanding of ``margin of capacity'', a new and systematic methodology for evaluating and visualising in-service structural utilisation of bridges, based on monitoring data, is presented. Three definitions of structural utilisation and three types of visualisation are proposed to inform the ``margin of capacity''. It has been found that the actual utilisation, based on monitoring data, can be significantly lower than the design expectation. Finally, a comprehensive analysis of data-related uncertainties is presented in order to evaluate the uncertainty levels of the output parameters of interest (e.g., structural properties, load effects) in the aforementioned studies. This study also includes a sensitivity analysis to identify the sources of data-related uncertainty that are most significant. Building on the outcomes of this thesis, it is envisaged that the industry challenge of ``How much additional live load can be placed on a bridge before violating its safety, serviceability or durability criteria?'' could be addressed, thereby facilitating more efficient utilisation and more targeted maintenance of bridge assets and hence realising the practical value of bridge monitoring and model updating.","abstract_html":"Despite decades of research, there has been relatively little industry uptake of structural health monitoring (SHM) technologies and model updating techniques, i.e., the integration of structural monitoring data with structural analysis modelling, in bridge operation and maintenance (O\\&M) activities. Many bridge practitioners question whether these technologies and techniques can provide valuable and reliable information on bridge safety and performance. In the first part of this PhD thesis, a series of industry interviews are used to investigate the reasons behind this limited industry adoption and the disconnects between research and practice regarding bridge monitoring and model updating. It has been found that while most studies in the bridge SHM community have been focused on damage detection, many bridge practitioners are ultimately more interested in bridge capacity assessment, and in particular, the ``margin of capacity'' of their bridge assets (i.e., how much additional live load can be safely placed on a bridge). In addition, bridge assessment remains a key asset management challenge in that there has been a consistent mismatch between the calculated load rating and the actual capacity of bridges. These findings form the basis of the second part of this PhD thesis, which investigates how strain monitoring data may be utilised to improve bridge assessment by (i) better understanding and quantifying commonly made assumptions about structural behaviour to enable more realistic bridge modelling and analysis, and (ii) evaluating in-service structural utilisation to better understand the ``margin of capacity'', which is defined in this PhD study as how much additional live load can be safely placed on a bridge without violating the design performance criteria. First, four common assumptions about structural behaviour in bridge assessment are examined. These are the amount of prestress loss (for prestressed concrete bridges), load distribution characteristics, support boundary conditions and the stiffness contribution of secondary structural elements. Novel or refined methods are developed to enable the evaluation of these structural behaviour characteristics under normal operational conditions. In particular, the methods developed utilises normalised structural response profiles to minimise the effects of uncertainties in estimating material stiffness and load magnitudes. Using these methods, continuous data collection for more automated and reliable evaluation of in-service structural behaviour is made possible. Subsequently, to improve the understanding of ``margin of capacity'', a new and systematic methodology for evaluating and visualising in-service structural utilisation of bridges, based on monitoring data, is presented. Three definitions of structural utilisation and three types of visualisation are proposed to inform the ``margin of capacity''. It has been found that the actual utilisation, based on monitoring data, can be significantly lower than the design expectation. Finally, a comprehensive analysis of data-related uncertainties is presented in order to evaluate the uncertainty levels of the output parameters of interest (e.g., structural properties, load effects) in the aforementioned studies. This study also includes a sensitivity analysis to identify the sources of data-related uncertainty that are most significant. Building on the outcomes of this thesis, it is envisaged that the industry challenge of ``How much additional live load can be placed on a bridge before violating its safety, serviceability or durability criteria?'' could be addressed, thereby facilitating more efficient utilisation and more targeted maintenance of bridge assets and hence realising the practical value of bridge monitoring and model updating.","abstract_has_math":false,"creators":["Ye, Cong"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Middleton, Campbell"],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-02-05","date_published":"2022-02-05","updated_at":"2026-07-22T22:24:24Z","subjects":["civil engineering","structural engineering","bridge engineering","structural health monitoring","fibre optic sensing","model updating","bridge assessment","structural behaviour","structural utilisation","uncertainty analysis","prestressed concrete bridge","railway bridge","infrastructure management","industry interview"],"languages":["eng"],"rights":[],"rights_urls":["https://www.rioxx.net/licenses/all-rights-reserved/"],"identifier_entries":[{"key":"dc:creator.authoridentifier","label":"Author Identifier","values":["0000000191061578"],"render_values":[{"text":"0000-0001-9106-1578","href":"https://orcid.org/0000-0001-9106-1578","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.89342","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Middleton, Campbell"]},{"key":"dc:creator","label":"Author","values":["Ye, Cong"]},{"key":"dc:creator.authoridentifier","label":"Author Identifier","values":["0000000191061578"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2022-02-05"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/341922"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["civil engineering","structural engineering","bridge engineering","structural health monitoring","fibre optic sensing","model updating","bridge assessment","structural behaviour","structural utilisation","uncertainty analysis","prestressed concrete bridge","railway bridge","infrastructure management","industry interview"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://www.rioxx.net/licenses/all-rights-reserved/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["10.17863/CAM.89342"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/f368c357-26c3-4bb9-b9ec-f0bdc2d7f100/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Despite decades of research, there has been relatively little industry uptake of structural health monitoring (SHM) technologies and model updating techniques, i.e., the integration of structural monitoring data with structural analysis modelling, in bridge operation and maintenance (O\\&M) activities. 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These findings form the basis of the second part of this PhD thesis, which investigates how strain monitoring data may be utilised to improve bridge assessment by (i) better understanding and quantifying commonly made assumptions about structural behaviour to enable more realistic bridge modelling and analysis, and (ii) evaluating in-service structural utilisation to better understand the ``margin of capacity'', which is defined in this PhD study as how much additional live load can be safely placed on a bridge without violating the design performance criteria. First, four common assumptions about structural behaviour in bridge assessment are examined. These are the amount of prestress loss (for prestressed concrete bridges), load distribution characteristics, support boundary conditions and the stiffness contribution of secondary structural elements. Novel or refined methods are developed to enable the evaluation of these structural behaviour characteristics under normal operational conditions. In particular, the methods developed utilises normalised structural response profiles to minimise the effects of uncertainties in estimating material stiffness and load magnitudes. Using these methods, continuous data collection for more automated and reliable evaluation of in-service structural behaviour is made possible. Subsequently, to improve the understanding of ``margin of capacity'', a new and systematic methodology for evaluating and visualising in-service structural utilisation of bridges, based on monitoring data, is presented. Three definitions of structural utilisation and three types of visualisation are proposed to inform the ``margin of capacity''. It has been found that the actual utilisation, based on monitoring data, can be significantly lower than the design expectation. Finally, a comprehensive analysis of data-related uncertainties is presented in order to evaluate the uncertainty levels of the output parameters of interest (e.g., structural properties, load effects) in the aforementioned studies. This study also includes a sensitivity analysis to identify the sources of data-related uncertainty that are most significant. Building on the outcomes of this thesis, it is envisaged that the industry challenge of ``How much additional live load can be placed on a bridge before violating its safety, serviceability or durability criteria?'' could be addressed, thereby facilitating more efficient utilisation and more targeted maintenance of bridge assets and hence realising the practical value of bridge monitoring and model updating."]},{"key":"dc:format.checksum.md5","label":"Dc Format Checksum Md5","values":["a37e8ac67ccb1df1ede90cf8db3255c6"]},{"key":"dc:title","label":"Title","values":["Structural assessment of bridges using monitoring data"]}]}],"canonical_facts":{"dc:contributor.advisor":["Middleton, Campbell"],"dc:creator":["Ye, Cong"],"dc:creator.authoridentifier":["0000000191061578"],"dc:date.issued":["2022-02-05"],"dc:description.abstract":["Despite decades of research, there has been relatively little industry uptake of structural health monitoring (SHM) technologies and model updating techniques, i.e., the integration of structural monitoring data with structural analysis modelling, in bridge operation and maintenance (O\\&M) activities. 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These findings form the basis of the second part of this PhD thesis, which investigates how strain monitoring data may be utilised to improve bridge assessment by (i) better understanding and quantifying commonly made assumptions about structural behaviour to enable more realistic bridge modelling and analysis, and (ii) evaluating in-service structural utilisation to better understand the ``margin of capacity'', which is defined in this PhD study as how much additional live load can be safely placed on a bridge without violating the design performance criteria. First, four common assumptions about structural behaviour in bridge assessment are examined. These are the amount of prestress loss (for prestressed concrete bridges), load distribution characteristics, support boundary conditions and the stiffness contribution of secondary structural elements. Novel or refined methods are developed to enable the evaluation of these structural behaviour characteristics under normal operational conditions. In particular, the methods developed utilises normalised structural response profiles to minimise the effects of uncertainties in estimating material stiffness and load magnitudes. Using these methods, continuous data collection for more automated and reliable evaluation of in-service structural behaviour is made possible. Subsequently, to improve the understanding of ``margin of capacity'', a new and systematic methodology for evaluating and visualising in-service structural utilisation of bridges, based on monitoring data, is presented. Three definitions of structural utilisation and three types of visualisation are proposed to inform the ``margin of capacity''. It has been found that the actual utilisation, based on monitoring data, can be significantly lower than the design expectation. Finally, a comprehensive analysis of data-related uncertainties is presented in order to evaluate the uncertainty levels of the output parameters of interest (e.g., structural properties, load effects) in the aforementioned studies. This study also includes a sensitivity analysis to identify the sources of data-related uncertainty that are most significant. Building on the outcomes of this thesis, it is envisaged that the industry challenge of ``How much additional live load can be placed on a bridge before violating its safety, serviceability or durability criteria?'' could be addressed, thereby facilitating more efficient utilisation and more targeted maintenance of bridge assets and hence realising the practical value of bridge monitoring and model updating."],"dc:format.checksum.md5":["a37e8ac67ccb1df1ede90cf8db3255c6"],"dc:identifier.doi":["10.17863/CAM.89342"],"dc:identifier.uri":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/f368c357-26c3-4bb9-b9ec-f0bdc2d7f100/download"],"dc:language":["eng"],"dc:publisher.institution":["University of Cambridge"],"dc:relation.isreferencedby.uri":["https://www.repository.cam.ac.uk/handle/1810/341922"],"dc:rights":["https://www.rioxx.net/licenses/all-rights-reserved/"],"dc:subject":["civil engineering","structural engineering","bridge engineering","structural health monitoring","fibre optic sensing","model updating","bridge assessment","structural behaviour","structural utilisation","uncertainty analysis","prestressed concrete bridge","railway bridge","infrastructure management","industry interview"],"dc:title":["Structural assessment of bridges using monitoring data"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["Doctoral"],"dc:type.qualificationname":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-22T22:24:24Z"}