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KSCE JOURNAL OF CIVIL AND
ENVIRONMENTAL ENGINEERING RESEARCH
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ISSN : 1015-6348 (Print)
ISSN : 2799-9629 (Online)
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Journal of the Korean Society of Civil Engineers
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KSCE J. Civ. Environ. Eng. Res.
Open Access, Bi-monthly
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2026-06
(v.46 n.3)
10.12652/Ksce.2026.46.3.0237
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References
1
Abdi, Y., Garavand, A. T., Sahamieh, R. Z. (2018). Prediction of strength parameters of sedimentary rocks using artificial neural networks and regression analysis, Arabian Journal of Geosciences, 11(19).
2
Altindag, R. (2012). Correlation between P-wave velocity and some mechanical properties for sedimentary rocks, Journal of the Southern African Institute of Mining and Metallurgy, 112(3), 229-237.
3
Angelis, D., Sofos, F., Karakasidis, T. E. (2023). Artificial intelligence in physical sciences: Symbolic regression trends and perspectives, Archives of Computational Methods in Engineering, 30(6), 3845-3865.
4
Armaghani, D. J., Hajihassani, M., Bejarbaneh, B. Y., Marto, A., Mohamad, E. T. (2014). Indirect measure of shale shear strength parameters by means of rock index tests through an optimized artificial neural network, Measurement, 55, 487-498.
5
Cranmer, M. (2023). Interpretable machine learning for science with PySR and SymbolicRegression. jl, arXiv preprint arXiv.
6
Ding, X., Amiri, M., Hasanipanah, M. (2024). Enhancing shear strength predictions of rocks using a hierarchical ensemble model, Scientific Reports, 14(1).
7
Fox, C., Tran, N. D., Nacion, F. N., Sharlin, S., Josephson, T. R. (2024). Incorporating background knowledge in symbolic regression using a computer algebra system, Machine Learning: Science and Technology, 5(2).
8
Gong, F., Luo, S., Lin, G., Li, X. (2020). Evaluation of shear strength parameters of rocks by preset angle shear, direct shear and triaxial compression tests, Rock Mechanics and Rock Engineering, 53(5), 2505-2519.
9
Han, D., Xue, X. (2024). Machine learning-based prediction of shear strength parameters of rock materials, Rock Mechanics and Rock Engineering, 57(10), 8795-8819.
10
Horsrud, P. (2001). Estimating mechanical properties of shale from empirical correlations, SPE Drilling & Completion, 16(2), 68-73.
11
Huang, X., Tang, J., Shen, Y., Yi, H., Zhang, C. (2026). Formula for irregular wave overtopping on vegetated seawall derived from physics-guided deep symbolic regression, Advanced Engineering Informatics, 70.
12
Hussain, J., Fu, X., Chen, J., Ali, N., Iqbal, S. M., Hussain, W., Hussain, A., Saleem, A. (2025). Estimation of rock strength parameters from petrological contents using tree-based machine learning techniques, AI in Civil Engineering, 4(1).
13
Kahraman, S. (2001). Evaluation of simple methods for assessing the uniaxial compressive strength of rock, International Journal of Rock Mechanics and Mining Sciences, 38(7), 981-994.
14
Kainthola, A., Singh, P. K., Verma, D., Singh, R., Sarkar, K., Singh, T. N. (2015). Prediction of strength parameters of himalayan rocks: a statistical and ANFIS approach, Geotechnical and Geological Engineering, 33(5), 1255-1278.
15
Karaman, K., Cihangir, F., Ercikdi, B., Kesimal, A., Demirel, S. (2015). Utilization of the Brazilian test for estimating the uniaxial compressive strength and shear strength parameters, Journal of the Southern African Institute of Mining and Metallurgy, 115(3), 185-192.
16
Kwon, K., Kang, M., Kim, D., Choi, H. (2023). Prioritization of hazardous zones using an advanced risk management model combining the analytic hierarchy process and fuzzy set theory, Sustainability, 15(15).
17
Kwon, K., Kang, M., Kim, D., Pham, K., Choi, H. (2025). Optimized ground settlement classification during TBM tunneling by combining machine learning with statistical analysis, Geomechanics & Engineering, 42(3), 179-189.
18
Kwon, K., Kang, M., Shin, Y. J., Ahn, B., Choi, H. (2025). An interpretable framework for risk management in TBM excavation using expert elicitation integrated with fuzzy set theory, Scientific Reports, 15(1).
19
Lal, M. (1999). Shale stability: drilling fluid interaction and shale strength, SPE-54356-MS.
20
Makke, N., Chawla, S. (2024). Interpretable scientific discovery with symbolic regression: a review, Artificial Intelligence Review, 57(1).
21
Monjezi, M., Singh, T. N. (2000). Slope instability in an open cast mine, Coal International, 8, 145-147.
22
Mothibe, L. B., Shongwe, S. C. (2026). Model averaging and grid maps for modeling heavy-tailed insurance data, Risks, 14(1).
23
Pham, K., Nguyen, K., Lim, K., Kim, Y., Choi, H. (2024). A generalized formula for predicting soil compression index using multi-evolutionary algorithm, Engineering Geology, 343.
24
Shen, J., Jimenez, R. (2018). Predicting the shear strength parameters of sandstone using genetic programming, Bulletin of Engineering Geology and the Environment, 77(4), 1647-1662.
25
Sivakugan, N., Das, B. M., Lovisa, J., Patra, C. R. (2014). Determination of c and φ of rocks from indirect tensile strength and uniaxial compression tests, International Journal of Geotechnical Engineering, 8(1), 59-65.
26
Sousa, L. M., del Río, L. M. S., Calleja, L., de Argandona, V. G. R., Rey, A. R. (2005). Influence of microfractures and porosity on the physico-mechanical properties and weathering of ornamental granites, Engineering Geology, 77(1-2), 153-168.
27
Wang, D., Feng, D., Zhou, K., Chen, Y., Liao, S. J., Chen, S. (2025). Symbolic regression-enhanced dynamic wake meandering: fast and physically consistent wind turbine wake modelling, Journal of Fluid Mechanics, 1025.
28
Wang, Y., Wei, Y., Du, Y., Li, Z., Wang, T. (2024). Causality analysis and prediction of soil saturated hydraulic conductivity by combining empirical modeling and machine learning techniques, Journal of Hydrology, 644.
29
Yang, G., Li, X., Wang, J., Lian, L., Ma, T. (2015). Modeling oil production based on symbolic regression, Energy Policy, 82, 48-61.
30
Yang, Y., Choi, H., Kim, Y., Kwon, K. (2026). Symbolic regression-based prediction of coefficient of permeability for granular soils, Engineering Geology, 364.
31
Yang, Y., Choi, H., Yeom, Y., Kwon, K. (2025). Data‐augmented machine learning for risk management of tunnel boring machine jamming considering coupled geological factors, Computer‐Aided Civil and Infrastructure Engineering, 40(27), 5010-5026.
32
Yasar, E., Erdogan, Y. (2004). Correlating sound velocity with the density, compressive strength and Young's modulus of carbonate rocks, International Journal of Rock Mechanics and Mining Sciences, 41(5), 871-875.
33
Yu, X., Gen, M. (2010). Introduction to evolutionary algorithms, Springer Science & Business Media, Berlin.
34
Zhang, F. P., Li, D. Q., Cao, Z. J., Xiao, T., Zhao, J. (2018). Revisiting statistical correlation between Mohr-Coulomb shear strength parameters of Hoek-Brown rock masses, Tunnelling and Underground Space Technology, 77, 36-44.
35
Zhang, H., Wu, S., Liu, W., Long, Y. (2025). Uncertainty estimation of rock shear strength parameters based on gene expression programming—Bayesian inference, KSCE Journal of Civil Engineering, 29(12).
36
Zhao, T., Shen, F., Xu, L. (2024). Review and comparison of machine learning methods in developing optimal models for predicting geotechnical properties with consideration of feature selection, Soils and Foundations, 64(6).
37
Zhu, D., Yu, B., Wang, D., Zhang, Y. (2024). Fusion of finite element and machine learning methods to predict rock shear strength parameters, Journal of Geophysics and Engineering, 21(4), 1183-1193.
38
Zhu, Z., Ranjith, P. G., Tian, H., Jiang, G., Dou, B., Mei, G. (2021). Relationships between P-wave velocity and mechanical properties of granite after exposure to different cyclic heating and water cooling treatments, Renewable Energy, 168, 375-392.