Neural network based inspection of voids and karst conduits in hydro–electric power station tunnels using GPR
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Date
2018
Authors
Gokhan Kilic
Levent Eren
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier B.V.
Open Access Color
Green Open Access
Yes
OpenAIRE Downloads
OpenAIRE Views
Publicly Funded
No
BIP! Indicators
Impulse
Top 10%
Influence
Top 10%
Popularity
Top 10%
Abstract
This paper reports on the fundamental role played by Ground Penetrating Radar (GPR) alongside advanced processing and presentation methods during the tunnel boring project at a Dam and Hydro–Electric Power Station. It identifies from collected GPR data such issues as incomplete grouting and the presence of karst conduits and voids and provides full details of the procedures adopted. In particular the application of collected GPR data to the Neural Network (NN) method is discussed. © 2023 Elsevier B.V. All rights reserved.
Description
Keywords
Gpr, Karst Conduits, Ndt, Neural Network, Tbm, Geological Surveys, Ground Penetrating Radar Systems, Landforms, Ground Penetrating Radar, It Identify, Karst Conduit, Karst Voids, Network-based, Neural Network Method, Neural-networks, Radar Data, Tbm, Tunnel Boring, Nondestructive Examination, Artificial Neural Network, Dam, Ground Penetrating Radar, Hydroelectric Power Plant, Karst, Tunnel, Void, Geological surveys, Ground penetrating radar systems, Landforms, Ground Penetrating Radar, IT Identify, Karst conduit, Karst voids, Network-based, Neural network method, Neural-networks, Radar data, TBM, Tunnel boring, Nondestructive examination, artificial neural network, dam, ground penetrating radar, hydroelectric power plant, karst, tunnel, void
Fields of Science
0211 other engineering and technologies, 02 engineering and technology, 01 natural sciences, 0105 earth and related environmental sciences
Citation
WoS Q
Scopus Q
OpenCitations Citation Count
41
Source
Journal of Applied Geophysics
Volume
151
Issue
Start Page
194
End Page
204
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Citations
CrossRef : 41
Scopus : 48
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Mendeley Readers : 51
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