Bridges and viaducts are crucial components of transportation network, and activities as risk assessment and structural maintenance are crucial to avoid economic losses and casualties due to their collapse. External actions, natural hazards, material degradation and aging affect safety and serviceability of bridges. The effort required by national guidelines, as the one issued in 2020 in Italy, pose questions about the resources required for large-scale screening and risk assessment of large bridge portfolios, mostly populated by simply supported concrete girder bridges, both reinforced and prestressed (PSC). The main challenge is to leverage cutting-edge technologies for a cost-effective near-continuous monitoring of structural risk of bridges. In the field of remote-sensing technologies, Interferometry via Synthetic Aperture Radar and Unmanned Aerial Vehicle photogrammetry are two emerging technologies that meet requirements to achieve the goal. Large coverage, adequate resolution and accuracy, and acquisition frequency are them strengthen points at the base of two proposed frameworks. The combination with basic structural knowledge of bridges and environmental data represents a promising mix for portfolio structural risk assessment of existing bridges and structures as demonstrated in the explored case studies. Although these technologies are affected by limitations, their constant use can help to identify bridges requiring a more refined structural risk assessment based on traditional structural health monitoring systems (i.e., sensor-based). Some bridge typologies as PSC box-girder bridges pose additional challenges requiring more effective strategies to monitor prestressing force reduction. Machine learning surrogate models as Artificial Neural Networks coupled with eXplainability approaches are demonstrated to be effective when integrated with traditional sensor-based systems. The experimental campaign on a scaled PSC box bridge is a great example of this combination. The cost-effective monitoring of structural risk of bridges supported by cutting-edge technologies is a true example of the ongoing change in civil engineering that will flourish in the years to come thanks to the technical and scientific progress.

Cost-effective monitoring of structural risk of bridges supported by cutting-edge technologies

Calo', Mirko
2025

Abstract

Bridges and viaducts are crucial components of transportation network, and activities as risk assessment and structural maintenance are crucial to avoid economic losses and casualties due to their collapse. External actions, natural hazards, material degradation and aging affect safety and serviceability of bridges. The effort required by national guidelines, as the one issued in 2020 in Italy, pose questions about the resources required for large-scale screening and risk assessment of large bridge portfolios, mostly populated by simply supported concrete girder bridges, both reinforced and prestressed (PSC). The main challenge is to leverage cutting-edge technologies for a cost-effective near-continuous monitoring of structural risk of bridges. In the field of remote-sensing technologies, Interferometry via Synthetic Aperture Radar and Unmanned Aerial Vehicle photogrammetry are two emerging technologies that meet requirements to achieve the goal. Large coverage, adequate resolution and accuracy, and acquisition frequency are them strengthen points at the base of two proposed frameworks. The combination with basic structural knowledge of bridges and environmental data represents a promising mix for portfolio structural risk assessment of existing bridges and structures as demonstrated in the explored case studies. Although these technologies are affected by limitations, their constant use can help to identify bridges requiring a more refined structural risk assessment based on traditional structural health monitoring systems (i.e., sensor-based). Some bridge typologies as PSC box-girder bridges pose additional challenges requiring more effective strategies to monitor prestressing force reduction. Machine learning surrogate models as Artificial Neural Networks coupled with eXplainability approaches are demonstrated to be effective when integrated with traditional sensor-based systems. The experimental campaign on a scaled PSC box bridge is a great example of this combination. The cost-effective monitoring of structural risk of bridges supported by cutting-edge technologies is a true example of the ongoing change in civil engineering that will flourish in the years to come thanks to the technical and scientific progress.
2025
Inglese
Uva, Giuseppina
Ruggieri, Sergio
Nettis, Andrea
Iacobellis, Vito
Politecnico di Bari
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/196335
Il codice NBN di questa tesi è URN:NBN:IT:POLIBA-196335