Autonomous Machine Learning Algorithm for Stress Monitoring in Concrete Using Elastoacoustical Effect

• Published in: Materials Vol. 14; no. 15; p. 4116
• Main Authors: Lalik, Krzysztof, Kozek, Mateusz, Dominik, Ireneusz
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 23.07.2021; MDPI
• Subjects: Acoustic coupling; Acoustic propagation; Acoustic waves; Acoustics; Algorithms; Carbon; Concrete; Concrete construction; Concrete structures; Coupling; Energy; Equilibrium; Feedback; Feedback loops; Frequency analysis; Frequency measurement; Frequency spectrum; Machine learning; Positive feedback; Propagation; Sensors; Spectrum analysis; Strain gauges; Stress propagation; Velocity; Vibration; Wave propagation
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma14154116

The measurement of stress in concrete structures is a complex issue. This paper presents a new measurement system called a self-acoustic system (SAS), which uses frequency measurements of acoustic waves to determine the condition of concrete structures. The SAS uses a positive feedback loop between ultrasonic heads, which causes excitation to a stable limit cycle. The frequency of this cycle is related to the propagation time of an acoustic wave, which directly depends on stresses in the test object. The coupling mechanism between acoustic wave propagation speed and stress is the elastoacoustic effect described in this paper. Thus, the proposed system enables the coupling between the limit cycle frequency and the stress degree of the concrete structure. This paper presents a machine learning algorithm to analyse the frequency spectrum of the SAS system. The proposed solution is a real-time classifier that enables online analysis of the frequency spectrum from the SAS system. With this approach, an autonomous system for stress condition identification of concrete structures is built and described.