Structural health monitoring based on acoustic emissions : validation on a prestressed concrete bridge tested to failure

Tonelli, Daniel and Luchetta, Michele and Rossi, Francesco and Migliorino, Placido and Zonta, Daniele (2020) Structural health monitoring based on acoustic emissions : validation on a prestressed concrete bridge tested to failure. Sensors, 20 (24). 7272. ISSN 1424-8220 (https://doi.org/10.3390/s20247272)

[thumbnail of Tonelli-etal-Sensors-2020-Structural-health-monitoring-based-on-acoustic-emissions]
Preview
Text. Filename: Tonelli_etal_Sensors_2020_Structural_health_monitoring_based_on_acoustic_emissions.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo

Download (11MB)| Preview

Abstract

The increasing number of bridges approaching their design life has prompted researchers and operators to develop innovative structural health monitoring (SHM) techniques. An acoustic emissions (AE) method is a passive SHM approach based on the detection of elastic waves in structural components generated by damages, such as the initiation and propagation of cracks in concrete and the failure of steel wires. In this paper, we discuss the effectiveness of AE techniques by analyzing records acquired during a load test on a full-size prestressed concrete bridge span. The bridge is a 1968 structure currently decommissioned but perfectly representative, by type, age, and deterioration state of similar bridges in operation on the Italian highway network. It underwent a sequence of loading and unloading cycles with a progressively increasing load up to failure. We analyzed the AE signals recorded during the load test and examined how far their features (number of hits, amplitude, signal strength, and peak frequency) allow us to detect, quantify, and classify damages. We conclude that AE can be successfully used in permanent monitoring to provide information on the cracking state and the maximum load withstood. They can also be used as a non-destructive technique to recognize whether a structural member is cracked. Finally, we noticed that AE allow classifying different types of damage, although further experiments are needed to establish and validate a robust classification procedure.