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A platform for research: civil engineering, architecture and urbanism
Bridge damage detection using vehicle axle-force information
https://orcid.org/0000-0003-1358-1943
HighlightsA Moving Force Identification (MFI) algorithm is used to detect bridge damage.Two damage indicators are formed based on the outputs of the MFI algorithm.Each provides a range of possible solutions of damage location and severity.Combining both can provide a unique solution of damage location and severity.
AbstractMoving Force Identification (MFI) is the process of back-calculating the applied axle force histories from bridge measurements. This paper investigates the use of an MFI algorithm to detect the presence of bridge damage by monitoring calculated vehicle applied axle forces. Bridge deflections at three points along the bridge are used as the input to the algorithm. It is found that the combination of mean calculated gross vehicle weight and mean calculated axle weight ratio for a population of similar two-axle vehicles can be combined to indicate the location and severity of damage on a bridge. The bridge is modelled as a simply supported beam and deflections at the quarter point, midpoint and three quarter point are used as the inputs to the MFI algorithm. The method is shown to work best when damage is closer to the centre of the bridge.
Bridge damage detection using vehicle axle-force information
https://orcid.org/0000-0003-1358-1943
HighlightsA Moving Force Identification (MFI) algorithm is used to detect bridge damage.Two damage indicators are formed based on the outputs of the MFI algorithm.Each provides a range of possible solutions of damage location and severity.Combining both can provide a unique solution of damage location and severity.
AbstractMoving Force Identification (MFI) is the process of back-calculating the applied axle force histories from bridge measurements. This paper investigates the use of an MFI algorithm to detect the presence of bridge damage by monitoring calculated vehicle applied axle forces. Bridge deflections at three points along the bridge are used as the input to the algorithm. It is found that the combination of mean calculated gross vehicle weight and mean calculated axle weight ratio for a population of similar two-axle vehicles can be combined to indicate the location and severity of damage on a bridge. The bridge is modelled as a simply supported beam and deflections at the quarter point, midpoint and three quarter point are used as the inputs to the MFI algorithm. The method is shown to work best when damage is closer to the centre of the bridge.
Bridge damage detection using vehicle axle-force information
https://orcid.org/0000-0003-1358-1943
HighlightsA Moving Force Identification (MFI) algorithm is used to detect bridge damage.Two damage indicators are formed based on the outputs of the MFI algorithm.Each provides a range of possible solutions of damage location and severity.Combining both can provide a unique solution of damage location and severity.
AbstractMoving Force Identification (MFI) is the process of back-calculating the applied axle force histories from bridge measurements. This paper investigates the use of an MFI algorithm to detect the presence of bridge damage by monitoring calculated vehicle applied axle forces. Bridge deflections at three points along the bridge are used as the input to the algorithm. It is found that the combination of mean calculated gross vehicle weight and mean calculated axle weight ratio for a population of similar two-axle vehicles can be combined to indicate the location and severity of damage on a bridge. The bridge is modelled as a simply supported beam and deflections at the quarter point, midpoint and three quarter point are used as the inputs to the MFI algorithm. The method is shown to work best when damage is closer to the centre of the bridge.
Bridge damage detection using vehicle axle-force information
OBrien, Eugene J. (author) / Fitzgerald, Paul C. (author) / Malekjafarian, Abdollah (author) / Sevillano, Enrique (author)
Engineering Structures ; 153 ; 71-80
2017-10-06
10 pages
Article (Journal)
Electronic Resource
English
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