Structural Health Monitoring (SHM) is an increasingly crucial issue in civil engineering. Many structures like bridges and viaducts nowadays are older and show signs of structural deterioration that can potentially affect structural safety. One of the most popular monitoring techniques is dynamic monitoring under environmental vibrations using Operational Modal Analysis (OMA) techniques. The application of OMA techniques usually requires the recording of accelerometer signals of consistent time duration. Consequently, a large storage space, expensive cabled solutions and lots of data traffic are required for such purpose. In the last years, cheap wi-fi accelerometers begun to be installed on bridges providing short length accelerograms. This paper explores the feasibility and the quality of dynamic identification of composite steel-concrete bridge decks based on short-duration accelerometer recordings (slightly longer than 100 seconds) recorded at specific times of day. Furthermore, the outcome of using a small number of sensors on the results of dynamic identification is studied. The article highlights the potential and critical issues of dynamic monitoring based on short-term accelerometer recordings and a limited number of sensors. The results obtained show that short duration of accelerometer recordings cannot be used with a traditional OMA approach but may allow for dynamic identification with acceptable results if properly treated. The second critical aspect is related to the limited number of sensors, which, for the purposes of a model-driven monitoring approach, makes the model updating of the digital twin particularly complex.

Dynamic Monitoring of Composite Steel–Concrete Viaducts Using Short Accelerometric Records: Potential and Limitations / Ferrara, M., Bertagnoli, G., Imperiale, A., Masera, D., Lupoi, A.. - In: PROCEDIA STRUCTURAL INTEGRITY. - ISSN 2452-3216. - ELETTRONICO. - 84:(2026), pp. 1369-1376. (Third FABRE Conference - Existing bridges, viaducts and tunnels: research, innovation and applications Roma 16-19 February 2026) [10.1016/j.prostr.2026.06.175].

Dynamic Monitoring of Composite Steel–Concrete Viaducts Using Short Accelerometric Records: Potential and Limitations.

Mario Ferrara;Gabriele Bertagnoli;Alessandro Imperiale;Davide Masera;
2026

Abstract

Structural Health Monitoring (SHM) is an increasingly crucial issue in civil engineering. Many structures like bridges and viaducts nowadays are older and show signs of structural deterioration that can potentially affect structural safety. One of the most popular monitoring techniques is dynamic monitoring under environmental vibrations using Operational Modal Analysis (OMA) techniques. The application of OMA techniques usually requires the recording of accelerometer signals of consistent time duration. Consequently, a large storage space, expensive cabled solutions and lots of data traffic are required for such purpose. In the last years, cheap wi-fi accelerometers begun to be installed on bridges providing short length accelerograms. This paper explores the feasibility and the quality of dynamic identification of composite steel-concrete bridge decks based on short-duration accelerometer recordings (slightly longer than 100 seconds) recorded at specific times of day. Furthermore, the outcome of using a small number of sensors on the results of dynamic identification is studied. The article highlights the potential and critical issues of dynamic monitoring based on short-term accelerometer recordings and a limited number of sensors. The results obtained show that short duration of accelerometer recordings cannot be used with a traditional OMA approach but may allow for dynamic identification with acceptable results if properly treated. The second critical aspect is related to the limited number of sensors, which, for the purposes of a model-driven monitoring approach, makes the model updating of the digital twin particularly complex.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/3015331
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