The Probe Technology and Counter Measures of Goaf of Colliery Area in Tongluoshan Tunnel

2012 ◽  
Vol 204-208 ◽  
pp. 1419-1422
Author(s):  
Xin Bin Zhang
Keyword(s):  
Ground Penetrating Radar ◽  
Wall Rock ◽  
Underground Water ◽  
Construction Safety ◽  
Light Rail ◽  
Counter Measures ◽  
Rail Line ◽  
Geological Prediction ◽  
Ground Penetrating

During the construction of Tongluoshan Tunnel of the 6th light rail line in Chongqing, collapse of goaf of colliery area occurred which affects the construction and permanent safety heavily. Advanced geological prediction were done with ground penetrating radar to probe the goaf’s location, scale and characters of underground water and filling articles, then countermeasures such as advanced pipe roof supporting, goaf stowing, wall rock and filling articles’ voids grouting was applied；after that，supporting plan was adjusted to ensure construction safety with the data from monitoring whose result proves the availability of above prediction technology and countermeasures to similar goaf.

Simulation and Detection Leakage of Underground Water Pipeline by Ground Penetrating Radar

2019 ◽  
Vol 48 (3) ◽  
pp. 20190181 ◽  
Author(s):  
Yupeng Shen ◽  
Yuanrong Lin ◽  
Ping Li ◽  
Yujie Fu ◽  
Yaqiong Wang
Keyword(s):  
Ground Penetrating Radar ◽  
Underground Water ◽  
Water Pipeline ◽  
Ground Penetrating

scholarly journals Detection of underground water distribution piping system and leakages using ground penetrating radar (GPR)

2017 ◽  
Author(s):  
Tengku Sarah Tengku Amran ◽  
Mohamad Pauzi Ismail ◽  
Mohamad Ridzuan Ahmad ◽  
Mohamad Syafiq Mohd Amin ◽  
Suhairy Sani ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Water Distribution ◽  
Underground Water ◽  
Piping System ◽  
Ground Penetrating

Ground Penetrating Radar (GPR) and its Application in Tunnel Engineering

2014 ◽  
Vol 501-504 ◽  
pp. 1783-1786
Author(s):  
Chun Jin Lin ◽  
Shu Cai Li
Keyword(s):  
Case Studies ◽  
Phase Difference ◽  
Ground Penetrating Radar ◽  
Hubei Province ◽  
Tunnel Engineering ◽  
Main Criterion ◽  
Reflection Phase ◽  
Geological Prediction ◽  
Ground Penetrating ◽  
Reflection Characteristics

A Ground Penetrating Radar (GPR) method was conducted for advanced geological prediction in Wuchiba Tunnel field, Hubei Province, China. A 100 MHz antennae is applied. Reflection characteristics of water are studied. Theoretic and case studies indicate that the reflection characteristics of water or mud are: The reflection energy (amplitude) is high; the reflection frequency is low and the reflection phase is inverse (the phase difference is 180°). These characteristics are the main criterion of water and is instructive for further GPR advanced geological predictions in similar geological fields.

Exploration of Goaf of Colliery Area with Engineering Materials in a Highway Tunnel

2013 ◽  
Vol 676 ◽  
pp. 65-69
Author(s):  
Shi Liang Wang ◽  
Li Yun Yi
Keyword(s):  
Mining Area ◽  
Light Rail ◽  
Dynamic Adjustment ◽  
Coal Mining Area ◽  
Highway Tunnel ◽  
Chongqing City ◽  
Rail Line ◽  
Grouting Reinforcement ◽  
Ground Penetrating ◽  
Filling Characteristics

The safety of Tongluoshan Tunnel construction and permanent structure , in the 6th light rail line in Chongqing City, is seriously interfered with coal mining area collapsed. Some characters of the goaf, such as the position, scale, groundwater and filling characteristics, had been probe by Ground Penetrating Radar. And then some measures had been taken, such as pipe roof ahead of support, sealers goaf filling pore grouting reinforcement. After disposal in accordance with the monitoring Data of the dynamic adjustment of the support program and to ensure construction safety, and proved the validity of the aforementioned prediction and disposal techniques similar goaf treatment.

Forecast Geological Prediction for Fault and Water Gushing in Deep-Buried Super Long Tunnel

2012 ◽  
Vol 594-597 ◽  
pp. 1294-1297 ◽  
Author(s):  
Yajie Jia ◽  
Ya Lin Li ◽  
Liang Huo
Keyword(s):  
Ground Penetrating Radar ◽  
Prediction Method ◽  
Economic Loss ◽  
Survey Method ◽  
Good Prediction ◽  
Road Tunnel ◽  
Theoretical Significance ◽  
Geological Prediction ◽  
Ground Penetrating ◽  
Seismic Prediction

A kind of geological disasters such as fault and water gushing often occurs during the informational construction of deep super long tunnel, which brings momentous disaster and economic loss to construction safety, so the precise and immediate advance forecast to the situation of fault and water gushing has an important theoretical significance and practical value. The theory of geological survey method, TSP(tunnel seismic prediction) method and GPR(ground penetrating radar) method are summarized and analyzed, forecast geological prediction process for fault and water gushing in deep-buried super long tunnel is formulated with the combination of the three methods. Then a deep super long road tunnel named Da Xiang Ling is detected by using the process synthetically, which gains good prediction results. It has a certain guiding significance to similar projects.

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.

scholarly journals A Comprehensive Evaluation for the Tunnel Conditions with Ground Penetrating Radar Measurements

2021 ◽  
Vol 13 (21) ◽  
pp. 4250
Author(s):  
Jordi Mahardika Puntu ◽  
Ping-Yu Chang ◽  
Ding-Jiun Lin ◽  
Haiyina Hasbia Amania ◽  
Yonatan Garkebo Doyoro
Keyword(s):  
Ground Penetrating Radar ◽  
Field Data ◽  
Comprehensive Evaluation ◽  
Random Noise ◽  
Wall Rock ◽  
Tunnel Lining ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Silhouette Index ◽  
Ground Penetrating

We aim to develop a comprehensive tunnel lining detection method and clustering technique for semi-automatic rebar identification in order to investigate the ten tunnels along the South-link Line Railway of Taiwan (SLRT). We used the Ground Penetrating Radar (GPR) instrument with a 1000 MHz antenna frequency, which was placed on a versatile antenna holder that is flexible to the tunnel’s condition. We called it a Vehicle-mounted Ground Penetrating Radar (VMGPR) system. We detected the tunnel lining boundary according to the Fresnel Reflection Coefficient (FRC) in both A-scan and B-scan data, then estimated the thinning lining of the tunnels. By applying the Hilbert Transform (HT), we extracted the envelope to see the overview of the energy distribution in our data. Once we obtained the filtered radargram, we used it to estimate the Two-dimensional Forward Modeling (TDFM) simulation parameters. Specifically, we produced the TDFM model with different random noise (0–30%) for the rebar model. The rebar model and the field data were identified with the Hierarchical Agglomerative Clustering (HAC) in machine learning and evaluated using the Silhouette Index (SI). Taken together, these results suggest three boundaries of the tunnel lining i.e., the air–second lining boundary, the second–first lining boundary, and the first–wall rock boundary. Among the tunnels that we scanned, the Fangye 1 tunnel is the only one in category B, with the highest percentage of the thinning lining, i.e., 13.39%, whereas the other tunnels are in category A, with a percentage of the thinning lining of 0–1.71%. Based on the clustered radargram, the TDFM model for rebar identification is consistent with the field data, where k = 2 is the best choice to represent our data set. It is interesting to observe in the clustered radargram that the TDFM model can mimic the field data. The most striking result is that the TDFM model with 30% random noise seems to describe our data well, where the rebar response is rough due to the high noise level on the radargram.

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