Ground Penetrating Radar Use on Tunnel Disaster Warning

2011 ◽  
Vol 80-81 ◽  
pp. 1320-1323 ◽  
Author(s):  
Cheng Zhong Yang ◽  
Ben Qing Hu
Keyword(s):  
Ground Penetrating Radar ◽  
Radar System ◽  
Hebei Province ◽  
Radar Images ◽  
Testing Method ◽  
Geological Conditions ◽  
Geological Prediction ◽  
Ground Penetrating

The geological conditions of Tunnel are of complexity, variability and uncertainty,So it is a necessary guidance to accurately predict the specific type of adverse geological,the location and the size in front of the tunnel.This article take the Separate tunnel in Hebei province as the background, describes the basic working principles of the Ground Penetrating Radar System and the testing method in the tunnel geological prediction,uses the Ground Penetrating Radar needle to predict adverse geological disasters.and make specific explanation for the targeted radar images. The characteristics of Ground Penetrating Radar are that it does not affect the construction,it is nondestructive, fast and convenient, easy to operate.

Seismic reflection and ground‐penetrating radar imaging of a shallow aquifer

Geophysics ◽  
1998 ◽  
Vol 63 (4) ◽  
pp. 1310-1317 ◽  
Author(s):  
Steven J. Cardimona ◽  
William P. Clement ◽  
Katharine Kadinsky‐Cade
Keyword(s):  
Water Table ◽  
Ground Penetrating Radar ◽  
Low Frequency ◽  
Shallow Aquifer ◽  
Cone Penetrometer ◽  
Gradual Change ◽  
Cone Penetration ◽  
Geotechnical Data ◽  
Radar Images ◽  
Ground Penetrating

In 1995 and 1996, researchers associated with the US Air Force’s Phillips and Armstrong Laboratories took part in an extensive geophysical site characterization of the Groundwater Remediation Field Laboratory located at Dover Air Force Base, Dover, Delaware. This field experiment offered an opportunity to compare shallow‐reflection profiling using seismic compressional sources and low‐frequency ground‐penetrating radar to image a shallow, unconfined aquifer. The main target within the aquifer was the sand‐clay interface defining the top of the underlying aquitard at 10 to 14 m depth. Although the water table in a well near the site was 8 m deep, cone penetration geotechnical data taken across the field do not reveal a distinct water table. Instead, cone penetration tests show a gradual change in electrical properties that we interpret as a thick zone of partial saturation. Comparing the seismic and radar data and using the geotechnical data as ground truth, we have associated the deepest coherent event in both reflection data sets with the sand‐clay aquitard boundary. Cone penetrometer data show the presence of a thin lens of clays and silts at about 4 m depth in the north part of the field. This shallow clay is not imaged clearly in the low‐frequency radar profiles. However, the seismic data do image the clay lens. Cone penetrometer data detail a clear change in the soil classification related to the underlying clay aquitard at the same position where the nonintrusive geophysical measurements show a change in image character. Corresponding features in the seismic and radar images are similar along profiles from common survey lines, and results of joint interpretation are consistent with information from geotechnical data across the site.

scholarly journals ROAD PAVEMENT DENSITY ANALYSIS USING A NEW NON-DESTRUCTIVE GROUND PENETRATING RADAR SYSTEM

2010 ◽  
Vol 21 ◽  
pp. 399-417
Author(s):  
Mardeni Bin Roslee ◽  
Raja Syamsul Azmir Raja Abdullah ◽  
Helmi Zulhaidi bin Mohd Shafr
Keyword(s):  
Ground Penetrating Radar ◽  
Radar System ◽  
Road Pavement ◽  
Density Analysis ◽  
Ground Penetrating ◽  
Non Destructive

Compact programmable ground-penetrating radar system for roadway and bridge deck characterization

2011 ◽  
Author(s):  
Dan Busuioc ◽  
Tian Xia ◽  
Anbu Venkatachalam ◽  
Dryver Huston ◽  
Ralf Birken ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Bridge Deck ◽  
Radar System ◽  
Ground Penetrating

scholarly journals Object Detection in Ground-Penetrating Radar Images Using a Deep Convolutional Neural Network and Image Set Preparation by Migration

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Kazuya Ishitsuka ◽  
Shinichiro Iso ◽  
Kyosuke Onishi ◽  
Toshifumi Matsuoka
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Ground Penetrating Radar ◽  
Deep Convolutional Neural Network ◽  
Training Data ◽  
Neural Network Approach ◽  
Radar Images ◽  
Image Set ◽  
Ground Penetrating ◽  
Better Than

Ground-penetrating radar allows the acquisition of many images for investigation of the pavement interior and shallow geological structures. Accordingly, an efficient methodology of detecting objects, such as pipes, reinforcing steel bars, and internal voids, in ground-penetrating radar images is an emerging technology. In this paper, we propose using a deep convolutional neural network to detect characteristic hyperbolic signatures from embedded objects. As a first step, we developed a migration-based method to collect many training data and created 53510 categorized images. We then examined the accuracy of the deep convolutional neural network in detecting the signatures. The accuracy of the classification was 0.945 (94.5%)–0.979 (97.9%) when using several thousands of training images and was much better than the accuracy of the conventional neural network approach. Our results demonstrate the effectiveness of the deep convolutional neural network in detecting characteristic events in ground-penetrating radar images.

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