The aging of materials, combined with the persistence and alteration of both operational loads and atmospheric conditions, cause a decrease of the structural properties of civil structures. This raises questions of considerable importance when it comes into play the safety of infrastructure users, mainly related to bridges and roads. Therefore, monitoring and evaluating the health of such structures becomes of central importance, allowing a more efficient maintenance, aimed at preserving or recovering the required structural properties. This article describes a case study where a structural health monitoring system has been installed on a damaged operating bridge, where the signs of heavy wear were first detected through visual inspections. Therefore, a network of accelerometers has been designed and installed to the purpose of monitoring the evolution of deterioration phenomena and testing some new approaches related to the use of MEMS sensors. In particular, sensors readings were collected in real-time in order to gain useful information about the dynamic behavior of the structure under ambient and traffic loads. Data obtained from the monitoring system were used to support the decision of carrying out maintenance operations aiming at reinforcing the bridge, increasing its structural stiffness. This result was achieved through the post tensioned reinforcement of the bridge, by means of external tendons. The vibration data were collected at different points along the bridge, before and after the maintenance operations, so that both the damaged and undamaged information are now known, suggesting a supervised learning approach for future monitoring of the structure. The modal parameters of the bridge, extracted from the data, have been used to verify the change in structural stiffness, confirming the effectiveness of the adopted intervention in improving the structural property.

Structural health monitoring of a damaged operating bridge: Asupervised learning case study

Cigada A.;Luca F.;
2021-01-01

Abstract

The aging of materials, combined with the persistence and alteration of both operational loads and atmospheric conditions, cause a decrease of the structural properties of civil structures. This raises questions of considerable importance when it comes into play the safety of infrastructure users, mainly related to bridges and roads. Therefore, monitoring and evaluating the health of such structures becomes of central importance, allowing a more efficient maintenance, aimed at preserving or recovering the required structural properties. This article describes a case study where a structural health monitoring system has been installed on a damaged operating bridge, where the signs of heavy wear were first detected through visual inspections. Therefore, a network of accelerometers has been designed and installed to the purpose of monitoring the evolution of deterioration phenomena and testing some new approaches related to the use of MEMS sensors. In particular, sensors readings were collected in real-time in order to gain useful information about the dynamic behavior of the structure under ambient and traffic loads. Data obtained from the monitoring system were used to support the decision of carrying out maintenance operations aiming at reinforcing the bridge, increasing its structural stiffness. This result was achieved through the post tensioned reinforcement of the bridge, by means of external tendons. The vibration data were collected at different points along the bridge, before and after the maintenance operations, so that both the damaged and undamaged information are now known, suggesting a supervised learning approach for future monitoring of the structure. The modal parameters of the bridge, extracted from the data, have been used to verify the change in structural stiffness, confirming the effectiveness of the adopted intervention in improving the structural property.
2021
Conference Proceedings of the Society for Experimental Mechanics Series
978-3-030-47633-5
978-3-030-47634-2
Bridge
MEMS sensors
SHM
Structural dynamics
Supervised learning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1168992
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