The predictive maintenance of large-scale civil infrastructure is emerging as a strategic frontier in the effort to ensure long-term resilience, reduce lifecycle costs, and prioritize safety. This contribution explores an integrated framework for the predictive maintenance of suspension bridges, coupling deterioration modelling with parametric cost-effectiveness analyses and long-term planning optimization. The degradation processes due to ageing are first reviewed and technical expressions to implement in simpler models are identified. A simple case study that highlights the problems and allows to compare different maintenance strategies serves as an example of application of the main steps of a predictive maintenance framework.
Predictive Maintenance of Cable Supported Bridges: Integrating Damage Evolution, Cost Analysis, and Data-Driven Decision Support / De Biagi, V., Casasso, L., Domaneschi, M., Chiaia, B.. - In: REPORT. - ISSN 2221-3783. - 2:(2026), pp. 1409-1416. (IABSE Symposium Copenhagen 2026: Bridging Advanced Technologies - Structural Innovation Copenhagen (Denmark) 21-24 April 2026) [10.2749/copenhagen.2026.1409].
Predictive Maintenance of Cable Supported Bridges: Integrating Damage Evolution, Cost Analysis, and Data-Driven Decision Support
De Biagi V.;Casasso L.;Domaneschi M.;Chiaia B.
2026
Abstract
The predictive maintenance of large-scale civil infrastructure is emerging as a strategic frontier in the effort to ensure long-term resilience, reduce lifecycle costs, and prioritize safety. This contribution explores an integrated framework for the predictive maintenance of suspension bridges, coupling deterioration modelling with parametric cost-effectiveness analyses and long-term planning optimization. The degradation processes due to ageing are first reviewed and technical expressions to implement in simpler models are identified. A simple case study that highlights the problems and allows to compare different maintenance strategies serves as an example of application of the main steps of a predictive maintenance framework.| File | Dimensione | Formato | |
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https://hdl.handle.net/11583/3016275
