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Typical methods for forecasting karst collapse in China

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Title:
Typical methods for forecasting karst collapse in China
Alternate Title:
NCKRI Symposium 2: Proceedings of the Thirteenth Multidisciplinary Conference on Sinkholes and the Engineering and Environmental Impacts of Karst
Creator:
Meng, Yan
Dai, Jianling
Jia, Long
Lei, Mingtang
Ji, Feng
Publisher:
University of South Florida
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English

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Conference Proceeding
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The aim of this paper is to describe improvements in the accuracy of forecasting karst collapse by summarizing the methods and analyzing their advantages and disadvantages. The forecasting methods were classified as geophysical surveys, monitoring of triggering factors, and strain measurements using optical fibers. Geophysical surveys can directly identify soil cavities, but the precision and depth of exploration are limited by equipment parameters and geological conditions. For example, ground penetrating radar can discover a soil cavity when the overburden layer is less than 15 m thick, and frequent scanning can determine changes in the soil cavity and predict sinkhole collapse when combined with a balance arch model. Monitoring of triggering factors is widely used to forecast karst collapse when the opening is caused by pumping, as the dynamic groundwater conditions can be acquired in real-time. However, the prediction criteria can be very difficult to obtain. In this paper we recommend a method based on the relationship between the times when anomalous monitoring data appear and the time a sinkhole opens. Using optical fibers to forecast karst collapse is the most advanced technology currently available in China. The location and time of sinkhole opening can be forecast by this method in theory, but some key issues have yet to be resolved. These issues include the strain correlation between the optical fiber and the soil, the effect of temperature on the optical fiber strain and the method of laying optical fibers in the soil. Finally, some proposals are suggested in the hope that they will generate public discussion, reducing the damage caused by karst collapse. -- Authors
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University of South Florida Library
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University of South Florida
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K26-04816 ( USFLDC DOI )
k26.4816 ( USFLDC Handle )
11825 ( karstportal - original NodeID )

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Description
The aim of this paper
is to describe improvements in the accuracy of forecasting
karst collapse by summarizing the methods and analyzing their
advantages and disadvantages. The forecasting methods were
classified as geophysical surveys, monitoring of triggering
factors, and strain measurements using optical fibers.
Geophysical surveys can directly identify soil cavities, but
the precision and depth of exploration are limited by equipment
parameters and geological conditions. For example, ground
penetrating radar can discover a soil cavity when the
overburden layer is less than 15 m thick, and frequent scanning
can determine changes in the soil cavity and predict sinkhole
collapse when combined with a balance arch model. Monitoring of
triggering factors is widely used to forecast karst collapse
when the opening is caused by pumping, as the dynamic
groundwater conditions can be acquired in real-time. However,
the prediction criteria can be very difficult to obtain. In
this paper we recommend a method based on the relationship
between the times when anomalous monitoring data appear and the
time a sinkhole opens. Using optical fibers to forecast karst
collapse is the most advanced technology currently available in
China. The location and time of sinkhole opening can be
forecast by this method in theory, but some key issues have yet
to be resolved. These issues include the strain correlation
between the optical fiber and the soil, the effect of
temperature on the optical fiber strain and the method of
laying optical fibers in the soil. Finally, some proposals are
suggested in the hope that they will generate public
discussion, reducing the damage caused by karst collapse. --
Authors



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13TH SINKHOLE CONFERENCE NCKRI SYMPOSIUM 2 since the 1960s to solve karst geology questions including karst collapse. Direct current (DC) resistivity techniques have been used in cave detection because of its low costs, (Vincenz, 1968; Smith, 1986; Panno et al, 1994; Batayneh geophysical tool for identifying and locating subsurface karst features, such as cavities, conduits and fractures (Arzi, 1975; Blizkovsky, 1979; Butler, 1984) and the methods are effective for forecasting karst collapse, but the precision, continuity and depth of the exploration are limited Monitoring for trigger factors based on the groundwater pressure has become more common and can be an effective method for forecasting karst collapse (Lei et al, 2002; Li et al, 2005; Meng et al, 2006). The greatest laboratory studies of the seepage deformation test have indicated that errors often appear due to discrepancies the soil, even within a single layer. Thus, the smallest experimental marginal hydraulic gradient usually is adopted as a threshold for engineering safety, but this leads to a very low forecasting accuracy. latest method for predict karst collapse. Model tests show that there is a very good relationship between the techniques need to be implemented to understand the Abstract The aim of this paper is to describe improvements in the accuracy of forecasting karst collapse by summarizing the methods and analyzing their advantages and as geophysical surveys, monitoring of triggering but the precision and depth of exploration are limited by equipment parameters and geological conditions. For example, ground penetrating radar can discover a soil cavity when the overburden layer is less than 15 m thick, and frequent scanning can determine changes in the soil cavity and predict sinkhole collapse when combined with a balance arch model. Monitoring of triggering factors is widely used to forecast karst collapse when the opening is caused by pumping, as the dynamic groundwater conditions can be acquired a method based on the relationship between the times when anomalous monitoring data appear and the time collapse is the most advanced technology currently available in China. The location and time of sinkhole opening can be forecast by this method in theory, but some key issues have yet to be resolved. These issues and the soil, the effect of temperature on the optical the soil. Finally, some proposals are suggested in the hope that they will generate public discussion, reducing the damage caused by karst collapse. Introduction In most studies of sinkholes, forecasting has been mainly based on geophysical surveys, monitoring of triggering factors, and strain measurements of the soil TYPICAL METHODS FOR FORECASTING KARST COLLAPSE IN CHINA Yan Meng, Jianling Dai, Long Jia, Mingtang Lei Institute of Karst Geology, Guilin, 541004, P.R. China, sinkhole@163.com, daijianling@karst.ac.cn Feng Ji State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610000, P.R. China, 54589998@qq.com 239

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NCKRI SYMPOSIUM 2 13TH SINKHOLE CONFERENCE 2 2 develop further when h>(b 2 Frequent scanning to monitor changes in the arch is very important, but can have high costs when the survey area soil cavities because of equipment limitations and the complicated geological conditions. Monitoring of Triggering Factors Following a geological survey and risk assessment, boreholes may be constructed in high risk locations to monitor changes in the groundwater through the karst Determining the forecasting threshold is a key part of the method, and laboratory testing is the most common way. First, some undisturbed soil samples are obtained, and these in the lab. If the hydraulic gradient in the laboratory tests is the soil, and a karst collapse will open at the ground surface. mechanical properties of soil even in the same layer. In the paper, the principles underlying each method forecasting results. Finally, the merits and disadvantages of each method are analyzed and some suggestions are provided for improving the forecasting precision and reducing the harm from karst collapse. Ground Penetrating Radar (GPR) Surveys The simplest and most popular method for forecasting geophysical surveys. This technique can directly identify is very important to predict sinkhole openings at the ground surface. If changes in the soil cavity arch from the on an idealized balance arch as shown in Figure 1, where: v is the natural vertical stress; is the natural horizontal stress; h is the height of the arch; b is the half v (Protodyakonov, 1962). The coordinates x and y describe the location of A on the arch LOM in the Figure 2. The 240 Figure 1. Ground penetrating radar image of a highway in Guilin, China and the idealized model of its balance arch.

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13TH SINKHOLE CONFERENCE NCKRI SYMPOSIUM 2 241 Figure 3. Monitoring of groundwater pressure. Figure 2. The idealized GPR modeling of its balance arch (Protodyakonov, 1962). v is the natural vertical stress; is the natural horizontal stress; LOM is the arch; h is the height of the arch; b is the half span; f is the Protodyakonov (1962) coefficient; x and y are the coordinates of A on the arch LOM; Rv is the horizontal thrust at the top of arch; P is the thrust at the arch springing and T is horizontal component, N is vertical component.

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NCKRI SYMPOSIUM 2 13TH SINKHOLE CONFERENCE time of the sinkholes opening have a linear correlation. The equation describing the relationship between the time of the maximum anomaly appearing and the time the equation describing the relationship between the time of the minimum anomaly appearing and the time of the relationship between the time of the most anomalous To improve the forecasting accuracy, a new method based on the residual analysis of groundwater pressure was developed (Table1). The monitoring data is anomalous based on relationships between the time that anomalous monitoring data were recorded and the time of karst collapse (Table 2). The data analysis shows that the times of the maximum, minimum and most anomalous values appearing and the 242 Figure 4. Relationship between timing of maximum anomalies and collapses. ID The monitoring time Monitoring data(kpa) Simulating data(kpa) residual errors 1 21.061 2 21.078 21.078 4 5 21.111 .... .... .... .... .... Table 1. Monitoring simulation data and residual errors.

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13TH SINKHOLE CONFERENCE NCKRI SYMPOSIUM 2 respectively. The feasibility of this linear relationship can be seen by the model test (Figure 5). First, optical will show the changes in the soil, and thus can predict sinkhole opening at the surface. The results show that the position of peak strain in the optical fibers corresponds to the area of disturbance and cavern formation in the soil, and the change in optical fiber strain in different soil layers indicates the vertical boundary of the area of disturbed soil (Figure 6). The time series of optical fiber strain shows the ongoing formation of areas of disturbed domain reflectometer (BOTDR) will become a reliable method for monitoring and predicting sinkhole collapse or subsidence, especially along highways and railways. value appearing and the time of the sinkhole opening 0.998. Consequently, the time of a sinkhole opening may will be very important in the future. Strain measurement using optical fibers the newest technologies. It has been found that there is a linear relationship between the Brillouin frequency shift (BFS, described by V B ( expressed as: (Eq 1) V B ( ) is the BFS including strain and temperature; V B ( 0,T 0 ) is the BFS at the initial temperature, T 0, without strain; and C 1 and C 2 243 Time of sinkhole opening Time of anomal ies Number of anomalies Minimum anomalies (k Time of minimum anomali es Maximum Anomalies (kpa) Time of maximum anomalies 175 107 41 186 17 26 52 84 11 11 72 22 5 Table 2. Relationship between the timing of anomalous values and karst collapses in Guangzhou, China. (,)(0,)() 0120 VTVTCCTT BB

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NCKRI SYMPOSIUM 2 13TH SINKHOLE CONFERENCE forecasting karst collapse can have very high costs, and the precision and depth of the exploration are limited by equipment parameters and geological conditions. Integrating geophysical surveys with other methods to identify and explore sinkholes may provide better results. When monitoring trigger factors using groundwater, it Discussion Sinkholes are one of the main geological hazards in karst concealed nature and sudden appearance. Research from characterizing karst collapse formation and monitoring collapse, such as geophysical surveys, monitoring trigger 244 Figure 5. Model sinkhole tests using optical fibers in Guilin, China. Figure 6. Strain curve from optical fiber during sinkhole opening

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13TH SINKHOLE CONFERENCE NCKRI SYMPOSIUM 2 collapse research in China. Environmental hazard assessment in evaporitic materials from ground penetrating radar: a case study. Environ Meng Y,Yin KL, Lei MT. 2006. Probabilistic analysis on technical problem in monitoring and predicting Protodyakonov MM (1962) Mechanical properties and drillability of rocks. In: Proceedings of the 5th symposium on rock mechanics, University of Minnesota. Smith D. 1986. Application of the poledipole resistivity technique to the detection of solution cavities Ulriksen C. 1982. Application of impulse radar to civil engineering. PhD Thesis, University Lund, Sweden. Vincenz A. 1968. Resistivity investigations of limestone location of the sinkhole opening cannot be determined. of groundwater withdrawal can be controlled by this method. promising new technology. The position and time of sinkhole opening can be forecast by this method in deformation; the effect of temperature on the optical immature, this method is very promising. These observations above are provided in the hope that they will generate more public discussion. Acknowledgements survey (No. 1212011220192), the special foundation of public service of land and resources in 2012 (the Key Technologies of the Sinkhole Monitoring and the National Natural Science Foundation (No. 40572164 References Arzi A. 1975. Microgravimetry for engineering using electrical resistivity methods. Environ Eng Blizkovsky M. 1979. Processing and application in Butler K. 1984. Microgravimetric and gravity gradient techniques for detection of subsurface cavities. 61:1050. study of monitoring sinkhole collapse by Kaspar M, Pecen J. 1975. Detection of caves in a karst formation by means of electromagnetic waves. 245

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