1. Automatic identification of dense damage-sensitive features in civil infrastructure using sparse sensor networks
- Author
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Said Quqa, Pier Paolo Diotallevi, Luca Landi, Quqa S., Landi L., and Diotallevi P.P.
- Subjects
Non-stationary ,Computer science ,Real-time computing ,0211 other engineering and technologies ,020101 civil engineering ,02 engineering and technology ,Curvature ,0201 civil engineering ,Acceleration ,Damage quantification ,021105 building & construction ,Vehicular traffic ,Modal identification ,Civil and Structural Engineering ,Influence line ,Structural health monitoring ,Emergency management ,business.industry ,Building and Construction ,Identification (information) ,Modal ,Control and Systems Engineering ,business ,Wireless sensor network - Abstract
Widespread monitoring of bridges is yet rarely employed at a territorial level due to the high costs of monitoring systems. However, the aging of civil infrastructures, combined with the growing traffic demand, poses the need for a simple and automatic tool that helps emergency management. In this paper, an integrated algorithm for the identification of dynamic and dense quasi-static structural features exploiting moving vehicles is proposed. Filtering raw acceleration structural responses, triggered by passing vehicles, enables the identification of modal parameters and curvature influence lines. The procedure can be implemented efficiently as its main computational core consists of convolutions. The definition of a curvature-based damage index leads to accurate localization and quantification of structural anomalies using few sensors. The proposed procedure is tested on three viaducts of the Italian A24 motorway. Moreover, a numerical model is studied to evaluate the potentialities of the strategy for damage localization.
- Published
- 2021