Abstract
This study investigates the feasibility evaluation of smart PZT-embedded sensors for impedance-based damage monitoring in prestressed concrete (PSC) anchorages. Firstly, the concept of impedance-based damage monitoring for the concrete anchorage is concisely introduced. Secondly, a prototype design of PZT-embedded rebar and aggregate (so-called smart rebar–aggregate) is chosen to sensitively acquire impedance responses-induced local structural damage in anchorage members. Thirdly, an axially loaded concrete cylinder embedded with the smart rebar–aggregate is numerically and experimentally analyzed to investigate their performances of impedance monitoring. Additionally, empirical equations are formulated to represent the relationships between measured impedance signatures and applied compressive stresses. Lastly, an experimental test on a full-scale concrete anchorage embedded with smart rebar–aggregates at various locations is performed to evaluate the feasibility of the proposed method. For a sequence of loading cases, the variation in impedance responses is quantified to evaluate the accuracy of smart rebar–aggregate sensors. The empirical equations formulated based on the axially loaded concrete cylinder are implemented to predict compressive stresses at sensor locations in the PSC anchorage.
Highlights
In prestressed concrete (PSC) structures, anchorage zones resist high compressive forces induced by pre-tensioned strands [1,2]
The statistical damage metric RMSD was utilized to quantify the variations in the impedance responses for loading cases
This study investigated the feasibility evaluation of smart PZT-embedded sensors for impedance-based damage monitoring in the PSC anchorage
Summary
In prestressed concrete (PSC) structures, anchorage zones resist high compressive forces induced by pre-tensioned strands [1,2]. Since the bearing plates are commonly embedded in concrete blocks, incipient concrete damage is extremely difficult to detect. As inner cracks propagate to the concrete surface, the deterioration (e.g., strand corrosion or prestress-loss) could be severe under harsh environmental conditions. Periodic visual inspection is commonly practiced for inspecting surface defects of concrete structures (e.g., spalling). Vision-based monitoring has been applied to automatically identify crack or spalling in PSC structures via deep learning techniques [11,12]. The common inaccessibility (e.g., inner cracks in the anchorage zone) makes the visual-based inspection difficult and frequently inconclusive
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