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.
Highlights
Several studies have been performed regarding the combined effects of sustained load and temperature on the bond response of near-surface mounted (NSM)-CFRP laminates in concrete elements [4,5,6,7], studies devoted to the global response of the strengthened structure under those same conditions acting simultaneously are practically nonexistent
The first three levels correspond to the cracking load plus 15%, 35%, and 100%, respectively, the fourth level is associated with the steel reinforcement yielding, and at the fifth level, because of the high level of yielding reached in the steel reinforcement, any new increment in internal tensile force was practically supported by the fiber reinforced polymer (FRP) reinforcement
A clustering unsupervised machine learning system for structural health monitoring (SHM) based on fiber Bragg grating (FBG) and PZT measurements has been developed
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Machine learning (ML) approaches are an interesting alternative way to detect hidden patterns in monitored data, and to separate the baseline from any anomalies experienced by the structure, either due to mechanical deterioration or change in temperature This kind of technique might identify complex patterns related to different stages of the tested structure. This paper proposes a damage detection approach for NSM-FRP strengthened RC specimens subjected to sustained loading and variable temperature, based on the combined use of the PZT-EMI technique, FBG sensors and data processing based on multilevel clustering analysis. We think that the combined use of FBG and PZT sensors in conjunction with clustering ML results in operational benefits for the monitoring of a problem in which the effects due to mechanical damage and temperature variations are coupled
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