13 citations as recorded by crossref.

1. Combination models of random forest for predicting seismic liquefaction based on SPT, CPT, Vs databases considering sampling strategies J. Hu et al. https://doi.org/10.1016/j.soildyn.2025.109642
2. Seismic Liquefaction Analysis of MCDM Weighted SPT Data Using Support Vector Machine Classification V. Alla et al. https://doi.org/10.1007/s40996-023-01293-6
3. Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks M. Ahmad et al. https://doi.org/10.1007/s11709-021-0682-3
4. A New Approach of Hybrid Bee Colony Optimized Neural Computing to Estimate the Soil Compression Coefficient for a Housing Construction Project P. Samui et al. https://doi.org/10.3390/app9224912
5. A Bayesian decision network–based pre-disaster mitigation model for earthquake-induced cascading events to balance costs and benefits on a limited budget W. Gu et al. https://doi.org/10.1016/j.cie.2024.110161
6. Interpretive Structural Modeling and MICMAC Analysis for Identifying and Benchmarking Significant Factors of Seismic Soil Liquefaction M. Ahmad et al. https://doi.org/10.3390/app9020233
7. Bayesian network models for probabilistic evaluation of earthquake-induced liquefaction based on CPT and Vs databases J. Hu & H. Liu https://doi.org/10.1016/j.enggeo.2019.04.003
8. Evaluating Seismic Soil Liquefaction Potential Using Bayesian Belief Network and C4.5 Decision Tree Approaches M. Ahmad et al. https://doi.org/10.3390/app9204226
9. Cone penetration test-based assessment of liquefaction potential using machine and hybrid learning approaches J. Khatti et al. https://doi.org/10.1007/s41939-024-00447-x
10. Assessment of Soil Liquefaction Potential in Kamra, Pakistan M. Ahmad et al. https://doi.org/10.3390/su10114223
11. Application of artificial intelligence in geotechnical engineering: A state-of-the-art review A. Baghbani et al. https://doi.org/10.1016/j.earscirev.2022.103991
12. Geotechnical assessment of seismicity and liquefaction potential at the Banarli landfill in Tekirdag, Turkey M. Kim et al. https://doi.org/10.1080/19475705.2024.2346148
13. Physics-Guided Machine Learning for Liquefaction-Induced Settlement Prediction A. Rathor & D. Khandelwal https://doi.org/10.1007/s10706-026-03775-6