Abstract
Owners of pre-stressed concrete structures must realize preventive maintenance in order to maintain structural safety and limit economic losses. Detection voids in tendon ducts, where corrosion could occur, is key in this effort. This paper focuses on the quantification of the performance of the impact echo method (IEM), applied using a new laser interferometer contactless robot, for duct void detection in a reinforced concrete wall. We show first the influence of the wall stiffness on the IEM (resonance) frequency. We use a probabilistic modeling to evaluate the IEM. We illustrate a way for accounting on-site uncertainties of NDT measurements.
Highlights
Preventive replacement of engineering structures results in high economic and environmental costs
The principal aim of the paper is to illustrate the potential of probabilistic modeling; the procedure and data presented can be implemented in a generic way to other Non Destructive Testing (NDT) methods and inspection problems
In the following we show that it is better to define it base on the best compromise between the increase of Probability of Detection (PoD) and the decrease of Probability of False Alarms (PFAs)
Summary
Preventive replacement of engineering structures results in high economic and environmental costs. Much effort is placed on maintaining these structures with efficient management plans. The challenge for the owners consists of guaranteeing the operation and safety of aging structures while ensuring reasonable costs and operational availability. Owners base their maintenance decision schemes mainly on structural integrity assessment and consequence analysis. The major inputs come from information collected by inspections that employ non-destructive or destructive tools. Risk Based Inspection (RBI) [1,2,3,4,5], provides the basis for optimizing the maintenance plans of existing structures while ensuring satisfactory safety and operational availability of the structure throughout its service life. RBI depends both on reliability computations and probabilistic modeling of inspection results
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