Literature DB >> 33352961

Structural Health Monitoring Based on Acoustic Emissions: Validation on a Prestressed Concrete Bridge Tested to Failure.

Daniel Tonelli1, Michele Luchetta1, Francesco Rossi2, Placido Migliorino3, Daniele Zonta1.   

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.

Entities:  

Keywords:  acoustic emissions; crack initiation; crack propagation; damage detection; load test; prestressed concrete bridge; structural health monitoring

Year:  2020        PMID: 33352961      PMCID: PMC7766546          DOI: 10.3390/s20247272

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  1 in total

1.  Load Test Analysis of a Long-Span Prestressed Nano-Concrete Highway Bridge.

Authors:  Liang Yan
Journal:  Int J Anal Chem       Date:  2022-09-30       Impact factor: 1.698

  1 in total

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