期刊
SENSORS
卷 22, 期 21, 页码 -出版社
MDPI
DOI: 10.3390/s22218483
关键词
structural health monitoring; smartphone application; damage identification; machine learning; structural dynamics
资金
- COFAC-Cooperativa de Formacao e Animacao Cultural, C.R.L.
- Fundacao para a Ciencia e a Tecnologia (MCTES) through national funds (PIDDAC) under the R&D Unit Civil Engineering Research and Innovation for Sustainability (CERIS) [UIDB/04625/2020]
This paper presents a smartphone application called App4SHM, which uses the phone's internal accelerometer to measure accelerations and employs a machine learning algorithm to detect structural damage. It shows reliable precision and accurate damage detection, making it a low-cost solution for long-term SHM and post-disaster assessment.
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.
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