Automatic identification of dense damage-sensitive features in civil infrastructure using sparse sensor networks

• Published in: Automation in construction Vol. 128; p. 103740
• Main Authors: Quqa, Said, Landi, Luca, Diotallevi, Pier Paolo
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.08.2021; Elsevier BV
• Subjects: Algorithms; Anomalies; Curvature; Damage detection; Damage localization; Damage quantification; Emergency management; Influence line; Influence lines; Localization; Microprocessors; Modal identification; Monitoring; Non-stationary; Numerical models; Parameter identification; Structural health monitoring; Vehicular traffic; Viaducts; Non-stationary; Modal identification; Structural health monitoring; Vehicular traffic; Damage quantification; Influence line
• ISSN: 0926-5805; 1872-7891
• DOI: 10.1016/j.autcon.2021.103740

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. •An integrated method to identify modal parameters and influence lines is proposed.•Dense structural features of bridges are identified by filtering acceleration data.•Quasi-static features can be directly employed as dense damage indicators.•Accurate damage localization can be achieved efficiently, using few sensors.•Numerical and experimental case studies show the potential of the method proposed.