Performance evaluation of the neural networks for moisture detection using GPR
Source abstract
Abstract
AbstractGround penetrating radar (GPR) is a highly researched area; however, despite this, there is a lack of knowledge about the well-known problem of moisture distorting the results of GPR surveys. This research analyses the results of a GPR survey on a Case Study Bridge structure in order to analyse this effect, specifically when checking for the positioning of rebar. The expected distortions of the GPR results due to the presence of moisture were indeed present, as further evidenced by subsequent destructive testing and velocity analysis. Furthermore, neural networks were also utilised to detect moisture ingress from the GPR raw data.Keywords:: GPR and data processingbridge structuresnon-destructivemoisture ingressneural networksback-propagation learning algorithm
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Geophysical Methods and Applications · Seismic Waves and Analysis · Ultrasonics and Acoustic Wave Propagation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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