Pengfei Liu
Peishuai Chen1
Xin Feng
Cong Zhang- 1Technology Center, CCCC Second Harbor Engineering Co., Ltd., Wuhan, China
- 2School of Civil Engineering, Central South University Forestry and Technology, Changsha, China
Editorial on the Research Topic
Evolution mechanism and prevention technology of karst geological engineering disasters
Introduction
Soluble rocks such as limestone and dolomite can form karst features, including caves, sinkholes, and solution grooves through prolonged groundwater erosion (Parise et al., 2015). Karst landscapes are widespread globally. Construction in karst areas often faces sudden and severe ground collapse risks, endangering project safety, structural integrity, and personnel well-being. Therefore, the detection, assessment, and treatment of karst formations are crucial in engineering.
Karst detection
Geotechnical investigation in karst regions necessarily includes karst detection. Current methods are diverse, primarily comprising geological mapping, engineering drilling, and geophysical exploration (Li and Xiao, 2006; Goldscheider et al., 2011; Kaufmann, 2014). Among these, geophysical techniques impose relatively fewer constraints and are more widely applied. Wang et al. detailed the principles and data processing methods of four geophysical exploration approaches for karst detection: multi-electrode resistivity, cross-hole electromagnetic wave computed tomography, microtremor survey, and ground-penetrating radar. They compared the applicability of each method through case studies. A novel one-shaped layout 3D electrical exploration model has been proposed for detecting karst groundwater channels, improving exploration efficiency by 85.9% over conventional methods while maintaining high accuracy (Wang et al.).
Geological assessment in karst areas
Karst collapse, a common hazard in such regions, is characterized by strong concealment, abrupt occurrence, and high destructiveness, capable of causing sudden surface subsidence and damage to structures. Thus, assessing and predicting karst collapse is essential.
Machine learning has been a transformative tool in addressing various science and technology problems (Qu et al., 2021; Qu et al., 2023). For example, it has been used for predicting potential hazards in tunnel engineering (Zhang et al.; Qu et al., 2025; Yuan et al., 2026). To address the need for high-precision karst collapse assessment, Wang et al. proposed a prediction model based on an optimized sparrow search algorithm combined with an extreme learning machine. The model uses Singer chaotic mapping to improve the sparrow search algorithm, enhancing population diversity and global search capability to avoid local optima. The optimized ISSA automatically adjusts the initial weights and thresholds of the ELM, while five-fold cross-validation determines the optimal hidden layer structure, forming an adaptive intelligent prediction framework.
Karst treatment
Karst features, often concealed, complex, and challenging to treat, can easily lead to engineering accidents if improperly managed during construction. Consequently, karst treatment remains a key research focus.
Grouting is a common technique in karst treatment. To overcome the limitations of traditional cement grouts in complex environments with high water flow and pressure, Liang et al. developed a high-performance modified clay-cement grouting material. Laboratory tests verified its performance, and field application in a water conservancy project demonstrated its suitability and effectiveness.
Addressing water inrush in karst formations and seepage at diaphragm wall joints, Jiang et al. proposed a treatment method combining geophysical exploration with grouting. For karst water inrush, electrical methods identify the flow channels, which are then sealed with clay-cement paste grouting. For diaphragm wall joint seepage, sonar detects leakage points, treated with cement-sodium silicate double-fluid grouting. Xu et al. developed a catastrophe theory-based grouting reinforcement control standard for underwater karst shield tunnels by analyzing instability mechanisms and modeling reinforcement ranges. The mechanism of dynamic grouting with self-expanding slurry for water plugging in karst tunnels was examined by Zhan et al. Tang et al. revealed that karst water erosion causes grouting curtain leakage mainly due to severe degradation of cement-clay composites, which are less durable than pure cement grouts. The pressure filtration effect in karst areas significantly enhances the initial consolidation strength of grouted curtains, necessitating a revised life prediction model that incorporates this effect for accurate service life forecasting, as validated through experiments and field application (Yan et al.). Yao et al. demonstrated that integrating high-density resistivity and frequency-division electrical resistivity tomography with geological methods provides an accurate, practical, and broadly applicable approach for diagnosing leakage risks in karst reservoir dams.
Construction cases in karst formations
Shield tunneling through karst strata poses risks such as shield machine sinking, head pitching, ground settlement or collapse, and excessive post-construction settlement (Cheng et al., 2017), which can cause serious hazards, significant economic loss, and even casualties (Xie et al., 2025; Ou et al., 2025a; Ou et al., 2025b).
Based on a shield tunneling project in Shenzhen, China, Li et al. proposed a membrane-sleeve valve pipe grouting technique for reinforcing karst strata, building on conventional sleeve valve pipe grouting. They detailed its key construction points and process, with field tests confirming its applicability. A combined short straight-hole and wedge compound cut blasting scheme was proposed and tested in a hard rock tunnel (Tu et al.). Various machine learning techniques have been leveraged to integrate geological records and tunneling parameters for predicting rock grades (Dang et al.). Mechanistic analysis of TBM cutterhead–ground interaction under the mud build-up effect was explored by Wang et al.
Finally, we extend our gratitude to everyone who contributed to this Research Topic. Their contributions include not only theoretical breakthroughs but also the accumulation and sharing of practical engineering experience. Through their dedicated efforts, challenges in karst construction are being progressively overcome, providing valuable references and insights for similar projects.
Author contributions
PL: Writing – original draft, Conceptualization. PC: Writing – review and editing, Investigation. XS: Writing – review and editing. XF: Writing – review and editing. CZ: Writing – original draft.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Conflict of interest
Authors PL, PC, XS, and XF were employed by CCCC Second Harbor Engineering Company Ltd.
The remaining author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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The author(s) declared that generative AI was not used in the creation of this manuscript.
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References
Cheng, W-C., Cui, Q-L., Shen, JS-L, Arulrajah, A, and Yuan, D-J (2017). Fractal prediction of grouting volume for treating karst caverns along shield tunneling alignment. Appl. Sci. 7 (7), 652–665. doi:10.3390/app7070652
Goldscheider, N., Plagnes, V., Guerin, R., Valois, R., and Bosch, F. P. (2011). Contribution of geophysical methods to karst-system exploration: an overview. Hydrogeol. J. 19 (6), 1169–1180. doi:10.1007/s10040-011-0746-x
Kaufmann, O. (2014). Geophysical mapping of solution and collapse sinkholes. J. Appl. Geophys. 111, 271–288. doi:10.1016/j.jappgeo.2014.10.011
Li, D., and Xiao, K. H. (2006). High density electrical resistance exploration in the NO.2 TieFengShan tunnel. J. Eng. Geol. 14 (3), 197–200. doi:10.3969/j.issn.1672-7940.2006.03.008
Ou, X., Zhou, Y., Kong, Y., Qu, T., Xu, S., Liao, W., et al. (2025a). Predicting floor heave risk in road tunnels with machine learning. J. Rock Mech. Geotechnical Eng. 17 (10), 6428–6438. doi:10.1016/j.jrmge.2024.12.034
Ou, X., Tang, C., Qu, T., Xu, S., Zhou, Y., and Tian, J. (2025b). Towards digitalized maintenance of operating tunnels: a text documents-based defect evaluation and visualization. Tunn. Undergr. Space Technol. 157, 106345. doi:10.1016/j.tust.2024.106345
Parise, M., Closson, F.D., Gutierrez, F., and Stevanović, Z. (2015). Anticipating and managing engineering problems in the complex karst environment. Environ. Earth Sci. 74 (12), 7823–7835. doi:10.1007/s12665-015-4647-5
Qu, T., Di, S., Feng, Y. T., Wang, M., and Zhao, T. (2021). Towards data-driven constitutive modelling for granular materials via micromechanics-informed deep learning. Int. J. Plasticity 144, 103046. doi:10.1016/j.ijplas.2021.103046
Qu, T., Zhao, J., Guan, S., and Feng, Y. T. (2023). Data-driven multiscale modelling of granular materials via knowledge transfer and sharing. Int. J. Plasticity 171, 103786. doi:10.1016/j.ijplas.2023.103786
Qu, T., Zhao, J., and Feng, Y. T. (2025). Artificial intelligence for computational granular media. Comput. Geotechnics 185, 107310. doi:10.1016/j.compgeo.2025.107310
Xie, Y., Qu, T., Yang, J., Wang, S., and Zhao, J. (2025). Multiscale insights into tunneling-induced ground responses in coarse-grained soils. Comput. Geotechnics 185, 107319. doi:10.1016/j.compgeo.2025.107319
Keywords: construction project, karst exploration, karst geological engineering disasters, karst region, karst treatment
Citation: Liu P, Chen P, Song X, Feng X and Zhang C (2026) Editorial: Evolution mechanism and prevention technology of karst geological engineering disasters. Front. Earth Sci. 13:1768763. doi: 10.3389/feart.2025.1768763
Received: 16 December 2025; Accepted: 16 December 2025;
Published: 06 January 2026.
Edited and reviewed by:
Gordon Woo, Risk Management Solutions, United KingdomCopyright © 2026 Liu, Chen, Song, Feng and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Pengfei Liu, cGZsaXUwMjI5QGZveG1haWwuY29t