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http://hdl.handle.net/10397/93544
Title: | GPR pattern recognition of shallow subsurface air voids | Authors: | Luo, TXH Lai, WWL |
Issue Date: | May-2020 | Source: | Tunnelling and underground space technology, May 2020, v. 99, 103355 | Abstract: | Countless subsurface voids in urban areas of cities threaten people's lives and property. A workflow for automatically identifying subsurface voids from ground penetrating radar (GPR) data was developed in this study. The workflow consists of 3 stages: locating voids automatically from C-scans, then verifying voids from corresponding B-scans, and finally making judgements based upon the previous 2 sets of results. This study adopted 2 (Lai et al., 2016) approaches: approach 1 quantified the GPR response of air voids using forward modelling, while approach 2 used workflow prototyping and validation with inverse modelling. Forward simulations indicated that different ratios of void size to GPR signal footprint could result in a variety of patterns in B-scans: they can be hyperbolas, cross patterns, bowl shaped patterns and reverberations. With a database of void patterns of both C-scans and B-scans established, in approach 2 the workflow uses a pyramid pattern recognition method – with pixel value or gradient being used for feature identification – to search automatically for air-filled void responses in GPR data. The workflow was tested using 2 laboratory and field experiments and the results were promising. The constraint values proposed by the 2 experiments were validated with another site experiment. Given the huge workload involved in city-scale subsurface health inspections, a standardized workflow can help improve efficiency and effectiveness of subsurface void identification. | Keywords: | Ground penetrating radar Pattern recognition Pyramid method Subsurface air void |
Publisher: | Pergamon Press | Journal: | Tunnelling and underground space technology | ISSN: | 0886-7798 | DOI: | 10.1016/j.tust.2020.103355 | Rights: | © 2020 Elsevier Ltd. All rights reserved. © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ The following publication Luo, T. X., & Lai, W. W. (2020). GPR pattern recognition of shallow subsurface air voids. Tunnelling and Underground Space Technology, 99, 103355 is available at https://doi.org/10.1016/j.tust.2020.103355 |
Appears in Collections: | Journal/Magazine Article |
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Luo_GPR_Pattern_Recognition.pdf | Pre-Published version | 2.14 MB | Adobe PDF | View/Open |
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