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Early Detection of Corrosion in Reinforced Concrete Structures Using Guided Waves Technique

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dc.contributor.advisor Mustapha, Samir
dc.contributor.author Habbaba, Nura
dc.date.accessioned 2023-08-21T09:25:20Z
dc.date.available 2023-08-21T09:25:20Z
dc.date.issued 2023-08-21
dc.date.submitted 2023-08-21
dc.identifier.uri http://hdl.handle.net/10938/24121
dc.description.abstract Reinforced Concrete is one of the most widely used composite materials in the modern construction industry for the building of infrastructure systems due to the availability of raw materials, low material cost, durability, fireproofing, and extended lifespan. Corrosion of reinforced concrete structures has become a significant problem worldwide, particularly in marine environments, resulting in exceptionally high maintenance costs. In the world of concrete failure, the actual cause is rarely visible, where only symptoms are visible due to accumulated internal stresses. These stresses may be generated due to corrosion of embedded reinforcement and may trigger partial or full collapse of the structure. Consequently, to maintain structural integrity and safety, robust non-destructive techniques to identify the initiation of corrosion are required. Fiber Bragg Grating (FBG) sensors and ultrasonic guided waves (UGW) have emerged as significant technologies in the field of structural health monitoring as they are promising methods for corrosion monitoring in reinforced concrete. FBG sensors are optical sensors that may detect corrosion-induced stresses in concrete, whereas UGW monitoring has been proven to be sensitive to a wide range of faults in composite and metallic constructions. In addition, given that they can propagate for long distances, a larger area may be inspected with a handful of sensors. In reinforced concrete buildings, corrosion causes delamination between the reinforcing steel and concrete interface, which in turn affects the wave propagation characteristics. This study presents an overview of recent developments in corrosion monitoring by using FBG sensors and UGW. The advantages and limitations of these approaches are examined, as well as their potential for real-time corrosion monitoring in reinforced concrete members. In this research, we aimed at detecting early corrosion in steel reinforced concrete by relying on the leaking wave. Hence, UGW propagation in steel bars embedded in concrete and the inspection of leaked energy from one bar to another through propagating in the concrete medium was investigated experimentally and numerically. It also aims to provide passive corrosion monitoring using embedded FBG strain sensors. Since natural corrosion is a slow process, an accelerated corrosion setup is used to induce artificial corrosion in a reinforced concrete specimen using the impressed voltage approach. The results obtained showed that the amplitude of the first longitudinal mode L(0,1) propagating is extremely sensitive to the corrosion mechanism, even when monitored away from the source of corrosion as it varies with the progress of corrosion. It is shown that corrosion initiation can be detected successfully and different corrosion phases which are corrosion initiation, corrosion progression, and diameter reduction can 3 be detected from the signal strength and characteristics of L(0,1) as it is sensitive to corrosion defects along steel bars. Further, the leaking wave was sensitive to the diameter reduction phase and propagation of the cracks as the corrosion progressed. Additionally, the integration of FBG sensors provided further insights into the correlation between strain and GW readings, particularly during the diameter reduction phase.
dc.language.iso en
dc.subject Ultrasonic Guided Waves
dc.subject Corrosion
dc.subject Reinforced Concrete
dc.subject Steel Bars
dc.subject Accelerated Corrosion
dc.title Early Detection of Corrosion in Reinforced Concrete Structures Using Guided Waves Technique
dc.type Thesis
dc.contributor.department Department of Civil and Environmental Engineering
dc.contributor.faculty Maroun Semaan Faculty of Engineering and Architecture (MSFEA)
dc.contributor.commembers Lu, Ye
dc.contributor.commembers Salam, Darine
dc.contributor.commembers Dabaghi, Mayssa
dc.contributor.degree ME
dc.contributor.AUBidnumber 202229572


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