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. 2022 Nov 4;22(21):8483.
doi: 10.3390/s22218483.

Smartphone Application for Structural Health Monitoring of Bridges

Eloi Figueiredo  1   2 , Ionut Moldovan  1   2 , Pedro Alves  1   3 , Hugo Rebelo  1   2 , Laura Souza  1   4
Affiliations

Smartphone Application for Structural Health Monitoring of Bridges

Eloi Figueiredo et al. Sensors (Basel). .

Abstract

The broad availability and low cost of smartphones have justified their use for structural health monitoring (SHM) of bridges. This paper presents a smartphone application called App4SHM, as a customized SHM process for damage detection. App4SHM interrogates the phone's internal accelerometer to measure accelerations, estimates the natural frequencies, and compares them with a reference data set through a machine learning algorithm properly trained to detect damage in almost real time. The application is tested on data sets from a laboratory beam structure and two twin post-tensioned concrete bridges. The results show that App4SHM retrieves the natural frequencies with reliable precision and performs accurate damage detection, promising to be a low-cost solution for long-term SHM. It can also be used in the context of scheduled bridge inspections or to assess bridges' condition after catastrophic events.

Keywords: damage identification; machine learning; smartphone application; structural dynamics; structural health monitoring.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Measurement axis used in the application.
Figure 2
Figure 2
Architecture of App4SHM.
Figure 3
Figure 3
Backoffice administration of App4SHM.
Figure 4
Figure 4
User interfaces of the application.
Figure 5
Figure 5
Schematic longitudinal representation of the steel beam along with typical cross section.
Figure 6
Figure 6
Two piezoelectric accelerometers fixed on the underside of the steel beam.
Figure 7
Figure 7
Correlation matrix of the computed natural frequencies.
Figure 8
Figure 8
Time series comparison (393B12 vs. 333B40).
Figure 9
Figure 9
Time series comparison (Phone vs. 333B40).
Figure 10
Figure 10
Individual (grey lines) and average (blue lines) power spectral densities (PSDs).
Figure 11
Figure 11
Average power spectral densities.
Figure 12
Figure 12
Standardized natural frequencies. The training database is represented with blue circles. The points corresponding to the three levels of damage are plotted in green if they were classified as ‘undamaged’ and in red if they were classified as ‘damaged’.
Figure 13
Figure 13
Lab beam and added masses used to simulate damage.
Figure 14
Figure 14
Damage index as a function of the added mass. Circular markers represent the five observations made for each added mass. The mean and standard deviation of each dataset are presented as the blue circle with a dot and the vertical blue bars, respectively.
Figure 15
Figure 15
New (left) and old (right) bridges over the Itacaiúnas River.
Figure 16
Figure 16
Configuration of the measurement sections used to perform ambient vibration tests.
Figure 17
Figure 17
App4SHM being tested at the Itacaiúnas bridges.
Figure 18
Figure 18
Damage detection at section 5 of new and old bridges using training data from the new bridge only.
See this image and copyright information in PMC

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