We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Analysis of the Impact of Sustained Load and Temperature on the Performance of the Electromechanical Impedance Technique through Multilevel Machine Learning and FBG Sensors.
- Authors
Perera, Ricardo; Torres, Lluis; Díaz, Francisco J.; Barris, Cristina; Baena, Marta
- Abstract
The electro-mechanical impedance (EMI) technique has been applied successfully to detect minor damage in engineering structures including reinforced concrete (RC). However, in the presence of temperature variations, it can cause false alarms in structural health monitoring (SHM) applications. This paper has developed an innovative approach that integrates the EMI methodology with multilevel hierarchical machine learning techniques and the use of fiber Bragg grating (FBG) temperature and strain sensors to evaluate the mechanical performance of RC beams strengthened with near surface mounted (NSM)-fiber reinforced polymer (FRP) under sustained load and varied temperatures. This problem is a real challenge since the bond behavior at the concrete–FRP interface plays a key role in the performance of this type of structure, and additionally, its failure occurs in a brittle and sudden way. The method was validated in a specimen tested over a period of 1.5 years under different conditions of sustained load and temperature. The analysis of the experimental results in an especially complex problem with the proposed approach demonstrated its effectiveness as an SHM method in a combined EMI–FBG framework.
- Subjects
IMPACT loads; MACHINE learning; STRUCTURAL health monitoring; FIBER Bragg gratings; STRAIN sensors; FIBER-reinforced concrete; CONCRETE beams
- Publication
Sensors (14248220), 2021, Vol 21, Issue 17, p5755
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s21175755