Title of Paper:Improved characterization of underground structure defects from two-stage Bayesian inversion using crosshole GPR data

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Date of Publication:2018-11-01

Journal:Automation in Construction

Included Journals:SCIE

Volume:95

Page Number:233-244

ISSN No.:0926-5805

Key Words:Underground structure; Crosshole ground-penetrating radar (GPR); Bayesian waveform inversion; Markov chain Monte Carlo (MCMC); Discrete cosine transform (DCT); Area of interest (AOI)

Abstract:Crosshole ground-penetrating radar (GPR) is a widely used measurement technique to help inspect the structural integrity of man-made underground structures. In a previous paper, we have introduced a Bayesian framework for inversion of crosshole GPR experiments to help back out defects in concrete underground structures. Here, we evaluate the practical usefulness of our inversion framework by application to waveform data from a real-world GPR survey of a diaphragm wall panel with two embedded structure defects. We also use this case study to further refine our methodology by introducing the elements of a two-stage inversion method to help delineate the exact location and shape of small structure defects. Herein, a low-resolution inversion composed of relatively few inversion coefficients (stage-1) is used to determine roughly the presence of structure defects, followed by a second inversion (stage-2) with much enhanced spatial resolution in those areas classified with anomalous or suspicious permittivity values. This two-stage inversion approach uses more wisely CPU-resources by focusing primarily on those areas of the concrete structure that have been classified as anomalies. We investigate the benefits of this two-stage inversion scheme using a synthetic and real-world case study involving waveform data of a diaphragm wall panel measured with crosshole GPR. Our results demonstrate that the proposed two-stage inversion method recovers successfully the location and shape of structure defects, at a computational cost that is considerably lower than the original inversion framework.