Fractal Prediction of Grouting Volume for Treating Karst Caverns along a Shield Tunneling Alignment
Abstract
:1. Introduction
2. Methodology
3. Hydrogeology and Engineering Geology of Guangzhou
4. Verification
4.1. Fractal Characteristics of Karst Caverns
4.2. Method Verification
4.2.1. Ninety-Eight Geological Drilling Holes
4.2.2. Forty-Eight Geological Drilling Holes
4.2.3. Nine Hundred and Thirty-Seven Grouting Holes
5. Conclusions
- (1)
- Since a karst cavern could be filled with groundwater, sand, or clay, this water along with other sources of groundwater may worsen the geological conditions during shield tunneling. Additionally, severe geo-hazards, such as water ingress, surface settlement, and sinkholes could easily be triggered while tunneling in the buried karst of Guangzhou.
- (2)
- Based on the fractal theory, the proposed method can be used to reasonably estimate the required volume of grouts during treatment. The procedure is: (i) to investigate the distribution of karst caverns along the tunnel alignment; (ii) to calculate the fractal parameters of the karst caverns; (iii) to calculate the total volume of karst caverns; and (iv) to predict the required volume of grouts by introducing the proposed karst grouting coefficient.
- (3)
- The two fractal characteristics—self-similarity and scaling invariance—identified in the literature are also in line with those observed in the karst geology in the Ma-Lian section of Guangzhou metro line no. 9, implying that the fractal theory can be effectively applied to investigate the distribution of karst caverns.
- (4)
- Three datasets including: (i) 98 geological drilling holes; (ii) 48 geological drilling holes; and (iii) 937 grouting holes were established via the presented case history. In the buried karst of Guangzhou, the karst grouting coefficient of 0.26 is recommended for predicting the required volume of grouts for treating karst caverns along tunnel alignment.
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Case | Covering Area (m2) | Fractal Parameters | V (m3) | RC (m3) | VR | ||||
---|---|---|---|---|---|---|---|---|---|
h1 | D1 | h2 | D2 | hmax | |||||
A | 20,384 | 0.2 | 0.27 | 1.6 | 0.15 | 9.6 | 20,446.9 | 2292.1 | 0.11 |
B | 9984 | 0.12 | 0.18 | 1.5 | 0.16 | 8.6 | 8742.2 | 0.26 | |
C | 3748 | 0.1 | 0.1 | 7.9 | 13.11 | 11.5 | 2083.7 | 1.1 |
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Cheng, W.-C.; Cui, Q.-L.; Shen, J.S.-L.; Arulrajah, A.; Yuan, D.-J. Fractal Prediction of Grouting Volume for Treating Karst Caverns along a Shield Tunneling Alignment. Appl. Sci. 2017, 7, 652. https://doi.org/10.3390/app7070652
Cheng W-C, Cui Q-L, Shen JS-L, Arulrajah A, Yuan D-J. Fractal Prediction of Grouting Volume for Treating Karst Caverns along a Shield Tunneling Alignment. Applied Sciences. 2017; 7(7):652. https://doi.org/10.3390/app7070652
Chicago/Turabian StyleCheng, Wen-Chieh, Qing-Long Cui, Jack Shui-Long Shen, Arul Arulrajah, and Da-Jun Yuan. 2017. "Fractal Prediction of Grouting Volume for Treating Karst Caverns along a Shield Tunneling Alignment" Applied Sciences 7, no. 7: 652. https://doi.org/10.3390/app7070652