journal article·2025-11-07Open access

Differences in the Correlation of Rock Mass–Structural Plane–Structural Block Shear Strength Parameters Between Sandstones and Mudstones in Continuous Strata

Congyan Ran, Jin Liao, Jinshan Hu, Xiaodong Wang, Tao Xu, Enze Bao, Zhen Liu, Cuiying Zhou

Applied Sciences· Volume 15· Issue 22

Abstract

In continuous strata engineering, such as foundations and underground caverns, the differences in shear strength between sandstone and mudstone rock mass–structural plane–structural block systems critically affect design and safety. However, the underlying mechanisms and controlling factors of these shear strength parameters remain poorly understood, leading to challenges in optimizing engineering strategies. This study investigates the differences in shear strength parameter correlations between sandstone and mudstone and develops an intelligent model for predicting rock mass displacement. We constructed multi-parameter correlation models using laboratory and field shear test data combined with a random forest algorithm. The results show that the model achieved high prediction accuracy (R2 = 0.997–0.998, RMSE = 1.649–3.898, MAE = 1.110–2.991). For instance, the peak shear strength of sandstone structural planes was approximately 54% higher than that of mudstone. Sensitivity analysis revealed that for sandstone, structural plane shear stress (27.80%) and structural block stress (25.50%) are the most sensitive factors, while for mudstone, structural plane shear displacement (35.20%) and structural block strain (34.20%) dominate. These correlations are model-predicted based on empirical data from shear tests. These findings provide a mechanistic understanding of plastic instability in sandstone and slip-strain-induced fissure extension in mudstone, and they can guide shear strength prediction and stability assessment in mixed sandstone–mudstone strata. The study contributes to the field by offering a quantitative basis for stratified adaptive design in continuous strata engineering, enhancing the efficiency and safety of foundation treatment and cavern support.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Rock Mechanics and Modeling · Geotechnical Engineering and Analysis · Karst Systems and Hydrogeology

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

POWERCHINA Chengdu Engineering Corporation Limited, Chengdu 610072, China

Guangdong Engineering Research Centre for Major Infrastructure Safety, Sun Yat-sen University, Guangzhou 510275, China

Institute of Disaster Prevention and Reduction of the Guangdong-Hong Kong-Macao Greater Bay Area, Guangdong University of Technology, Guangzhou 510006, China

School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China

Funding

National Natural Science Foundation of China

References (32)

Show source-provided references
1. Morphological anisotropy of shear failure surfaces of granite rock bridges10.1007/s40948-025-00974-y ↗
2. Xia, P., Zeng, B., Liu, J., and Pan, Y. (2025). Study on the Stability Evaluation Index System for Rock Slope–Anchoring Systems. Appl. Sci., 15.10.3390/app15169147 ↗
3. Design of Rock Support in Low Overburden and Hard Rock Conditions with the use of Rock Mass Classification Systems and Numerical Analyses: A Study Based on the Construction of the Hestnes Railway Tunnel, Norway
4. Discontinuity characterization for slope stability assessment using combined aerial photogrammetry, and geophysics approach10.1007/s11069-024-06932-3 ↗
5. Rockburst mechanism of rock mass with structural planes in underground chamber excavation10.1016/j.engfailanal.2022.106501 ↗
6. Preliminary analysis of the mechanisms, characteristics, and causes of a recent catastrophic structurally controlled rock planar slide in Esposende (northern Portugal)10.1007/s10346-023-02082-y ↗
7. Machine Learning-Based Prediction of Shear Strength Parameters of Rock Materials10.1007/s00603-024-04012-3 ↗
8. Data-driven machine learning approaches for predicting the shear strength of rock joints10.1016/j.rockmb.2025.100209 ↗
9. Advances in statistical mechanics of rock masses and its engineering applications10.1016/j.jrmge.2020.11.003 ↗
10. Zhang, X., Fan, C., Yao, C., Yang, J., Ma, Y., Ye, Z., and Zhou, C. (2025). Water-induced shear strength deterioration of sandstone-mudstone interface: Experimental and theoretical approach. J. Rock Mech. Geotech. Eng.10.1016/j.jrmge.2025.07.010 ↗
11. Maintaining geological reality in application of GSI for design of engineering structures in rock10.1016/j.enggeo.2018.03.022 ↗
12. A new and unified approach to improved scalability and volumetric fracture intensity quantification for GSI and rockmass strength and deformability estimation10.1016/j.ijrmms.2018.06.021 ↗
13. Testing method of rock structural plane using digital drilling10.1016/j.jrmge.2023.11.022 ↗
14. Estimation of the BQ system and rock mass modulus based on the P-wave velocity of the rock mass: A case study from the Himalayas tunneling10.1007/s12665-025-12499-4 ↗
15. Brittle rock mass failure in deep tunnels: The role of infilled structural plane with varying dip angles10.1016/j.ijrmms.2024.105721 ↗
16. Mechanical failure analysis during direct shear of double-joint rock mass10.1007/s10064-022-02930-6 ↗
17. Micro-meso-macroscale correlation mechanism of red-bed soft rocks failure within static water based on energy analysis10.1007/s11440-023-01893-6 ↗
18. Mechanical behavior of strengthened 3D sand-printed specimens under Brazilian test: A novel postprocessing approach10.1007/s40948-025-00975-x ↗
19. Numerical determination of strength and deformability of fractured rock mass by FEM modeling10.1016/j.compgeo.2014.10.011 ↗
20. Meso/Microscale Parameter Correlation Model of Sandstone Based on the Hoek-Brown Criterion and Parallel Bond Model10.1061/IJGNAI.GMENG-9381 ↗
21. Quantitative Evaluation and Classification Method of the Cataclastic Texture Rock Mass Based on the Structural Plane Network Simulation10.1007/s00603-018-1635-6 ↗
22. Three-dimensional numerical simulation of dynamic strength and failure mode of a rock mass with cross joints10.1007/s40789-024-00665-1 ↗
23. DNN-GA-RF prediction model for rock strength indicators based on sound level and drilling parameters10.1007/s10064-024-03854-z ↗
24. Seismic stability assessment of an arch dam-foundation system10.1007/s11803-015-0041-2 ↗
25. Deformation characteristics and damage evolution analysis of weak interlayer zone in fractured underground cavern10.1016/j.tust.2024.105686 ↗
26. Ulusay, R. (2015). The ISRM Suggested Methods for Rock Characterization, Testing and Monitoring: 2007–2014, Springer.10.1007/978-3-319-07713-0 ↗
27. (2013). Standard for Test Methods of Engineering Rock Mass (Standard No. GB/T 50266-2013 (2013)).
28. Study on mechanical properties of sand-mudstone interbedded rock mass under different layer thickness ratio-dip angle conditions10.1016/j.kscej.2025.100252 ↗
29. An alternative method for predicting internal friction angle of rock materials10.1007/s12665-024-11606-1 ↗
30. Analysing real world data streams with spatio-temporal correlations: Entropy vs. Pearson correlation10.1016/j.autcon.2017.12.036 ↗
31. Hematibahar, M., Kharun, M., Beskopylny, A.N., Stel’makh, S.A., Shcherban’, E.M., and Razveeva, I. (2024). Analysis of Models to Predict Mechanical Properties of High-Performance and Ultra-High-Performance Concrete Using Machine Learning. J. Compos. Sci., 8.10.3390/jcs8080287 ↗
32. Liang, B.Y., Liu, H.Y., Cressey, E.L., Xu, C.Y., Shi, L., Wang, L., Dai, J.Y., Wang, Z., and Wang, J. (2023). Uncertainty of Partial Dependence Relationship between Climate and Vegetation Growth Calculated by Machine Learning Models. Remote Sens., 15.10.3390/rs15112920 ↗