104 citations as recorded by crossref.

1. Machine learning approaches for prediction of fine-grained soils liquefaction M. Ozsagir et al. 10.1016/j.compgeo.2022.105014
2. Assessment of liquefaction potential of the Erzincan, Eastern Turkey E. Duman et al. 10.12989/gae.2014.7.6.589
3. Seismic liquefaction potential assessed by neural networks X. Xue & E. Liu 10.1007/s12665-017-6523-y
4. Patient rule-induction method for liquefaction potential assessment based on CPT data A. Kaveh et al. 10.1007/s10064-016-0990-3
5. Soil liquefaction assessment by using hierarchical Gaussian Process model with integrated feature and instance based domain adaption for multiple data sources H. Guo et al. 10.1007/s43503-022-00004-w
6. On the tracking of shelly carbonate sands using deep learning M. Wu et al. 10.1680/jgeot.21.00129
7. Detection of fatigue cracking in steel bridge girders: A support vector machine approach H. Hasni et al. 10.1016/j.acme.2016.11.005
8. New insights on rainfall triggering flow-like landslides and flash floods in Campania (Southern Italy) N. Santangelo et al. 10.1007/s10346-021-01667-9
9. A hybrid approach for computational determination of liquefaction potential of Erzurum City Center based on SPT data using response surface methodology F. Yılmaz et al. 10.1007/s12517-021-09312-4
10. Probabilistic evaluation of CPT-based seismic soil liquefaction potential: towards the integration of interpretive structural modeling and bayesian belief network M. Ahmad et al. 10.3934/mbe.2021454
11. A hyper parameterized artificial neural network approach for prediction of the factor of safety against liquefaction T. Kurnaz et al. 10.1016/j.enggeo.2023.107109
12. Empirical approach for prediction of bearing pressure of spread footings on clayey soil using artificial intelligence (AI) techniques P. Sultana et al. 10.1016/j.rineng.2022.100489
13. Explainable machine learning model for liquefaction potential assessment of soils using XGBoost-SHAP K. Jas & G. Dodagoudar 10.1016/j.soildyn.2022.107662
14. Application of artificial intelligence in geotechnical engineering: A state-of-the-art review A. Baghbani et al. 10.1016/j.earscirev.2022.103991
15. Seismically Induced Liquefaction Potential Assessment by Different Artificial Intelligence Procedures D. Kumar et al. 10.1007/s40515-023-00327-w
16. Evaluation of Liquefaction Potential of Soil Using Soft Computing Techniques P. Karna et al. 10.1007/s40098-023-00786-5
17. Predicting the strut forces of the steel supporting structure of deep excavation considering various factors by machine learning methods H. Hu et al. 10.1016/j.undsp.2023.12.005
18. Evaluation of liquefaction potential in central Taiwan using random forest method C. Liu et al. 10.1038/s41598-024-79127-2
19. A Comparative Study of Soil Liquefaction Assessment Using Machine Learning Models S. Hanandeh et al. 10.1007/s10706-022-02180-z
20. Prediction of pore-water pressure response to rainfall using support vector regression N. Babangida et al. 10.1007/s10040-016-1429-4
21. Particle Swarm Optimization Variants for Solving Geotechnical Problems: Review and Comparative Analysis A. Kashani et al. 10.1007/s11831-020-09442-0
22. An investigation of feature selection methods for soil liquefaction prediction based on tree-based ensemble algorithms using AdaBoost, gradient boosting, and XGBoost S. Demir & E. Sahin 10.1007/s00521-022-07856-4
23. Soil Saturation and Stability Analysis of a Test Site Slope Using the Shallow Landslide Instability Prediction (SLIP) Model L. Montrasio et al. 10.1007/s10706-018-0465-3
24. Liquefaction study of fine-grained soil using computational model S. Ghani & S. Kumari 10.1007/s41062-020-00426-4
25. Delegated Regressor, A Robust Approach for Automated Anomaly Detection in the Soil Radon Time Series Data M. Rafique et al. 10.1038/s41598-020-59881-9
26. SPT-Based Probabilistic Method for Evaluation of Liquefaction Potential of Soil Using Multi-Gene Genetic Programming P. Muduli & S. Das 10.4018/jgee.2013010103
27. CPT-based Seismic Liquefaction Potential Evaluation Using Multi-gene Genetic Programming Approach P. Muduli & S. Das 10.1007/s40098-013-0048-4
28. Assessment of liquefaction potential of Erzincan Province and its vicinity, Turkey E. Subası Duman & S. Ikizler 10.1007/s11069-014-1170-z
29. Predicting Earthquake-Induced Soil Liquefaction Based on Machine Learning Classifiers: A Comparative Multi-Dataset Study H. Guo et al. 10.1142/S0219876221420044
30. Integrating the LSSVM and RBFNN models with three optimization algorithms to predict the soil liquefaction potential M. Cai et al. 10.1007/s00366-021-01392-w
31. Modeling of tensile strength of rocks materials based on support vector machines approaches N. Ceryan et al. 10.1002/nag.2154
32. Prediction of Liquefied Soil Settlement Using Multilayer Perceptron with Bayesian Optimization N. Van Nguyen et al. 10.1007/s40098-024-00894-w
33. Performance evaluation of hybrid GA–SVM and GWO–SVM models to predict earthquake-induced liquefaction potential of soil: a multi-dataset investigation J. Zhou et al. 10.1007/s00366-021-01418-3
34. Entropy analysis for identifying significant parameters for seismic soil liquefaction A. Dalvi et al. 10.1080/17486025.2013.805255
35. Improvement of spatial estimation for soil organic carbon stocks in Yuksekova plain using Sentinel 2 imagery and gradient descent–boosted regression tree M. Budak et al. 10.1007/s11356-023-26064-8
36. Prediction of Probability of Liquefaction Using Soft Computing Techniques D. Kumar et al. 10.1007/s40030-022-00683-9
37. Shear wave Velocity-Based Machine Learning Modeling for Prediction of Liquefaction Potential of Soil J. Naik et al. 10.1007/s40098-024-01121-2
38. The Application of Machine Learning Techniques in Geotechnical Engineering: A Review and Comparison W. Shao et al. 10.3390/math11183976
39. Deep learning to evaluate seismic-induced soil liquefaction and modified transfer learning between various data sources H. Guo et al. 10.1016/j.undsp.2024.08.010
40. Estimation of spatiotemporal response of rooted soil using a machine learning approach Z. Cheng et al. 10.1631/jzus.A1900555
41. Liquefaction behavior of Indo-Gangetic region using novel metaheuristic optimization algorithms coupled with artificial neural network S. Ghani & S. Kumari 10.1007/s11069-021-05165-y
42. Drought Severity and Frequency Analysis Aided by Spectral and Meteorological Indices in the Kurdistan Region of Iraq H. Gaznayee et al. 10.3390/w14193024
43. A computationally efficient system for assessing near-real-time instability of regional unsaturated soil slopes under rainfall L. Li et al. 10.1007/s10346-019-01307-3
44. Employing a genetic algorithm and grey wolf optimizer for optimizing RF models to evaluate soil liquefaction potential J. Zhou et al. 10.1007/s10462-022-10140-5
45. Predicting seismic-induced liquefaction through ensemble learning frameworks M. Alobaidi et al. 10.1038/s41598-019-48044-0
46. Multi-objective feature selection (MOFS) algorithms for prediction of liquefaction susceptibility of soil based on in situ test methods S. Das et al. 10.1007/s11069-020-04089-3
47. A new approach for modeling of flow number of asphalt mixtures A. Alavi et al. 10.1016/j.acme.2016.06.004
48. Accuracy Assessment of Kriging, artificial neural network, and a hybrid approach integrating spatial and terrain data in estimating and mapping of soil organic carbon M. Kılıç et al. 10.1371/journal.pone.0268658
49. Large, deepwater slope failures: Implications for landslide-generated tsunamis C. Lo Iacono et al. 10.1130/G33446.1
50. Assessment of Soil Liquefaction Potential Using Genetic Programming Using a Probability-Based Approach N. Reddy et al. 10.1007/s40996-024-01421-w
51. A novel approach for assessment of seismic induced liquefaction susceptibility of soil D. Kumar et al. 10.1007/s12040-024-02341-z
52. Exploring the possibility of assessing the damage degree of liquefaction based only on seismic records by artificial neural networks A. Kamura et al. 10.1016/j.sandf.2021.01.014
53. Machine Learning Techniques for Soil Characterization Using Cone Penetration Test Data A. Chala & R. Ray 10.3390/app13148286
54. Bioclimatic analysis in a region of southern Italy (Calabria) T. Caloiero et al. 10.1080/11263504.2015.1037814
55. Revealing the nature of soil liquefaction using machine learning S. Ghani et al. 10.1007/s12145-024-01688-7
56. Combining spatial autocorrelation with artificial intelligence models to estimate spatial distribution and risks of heavy metal pollution in agricultural soils E. Günal et al. 10.1007/s10661-022-10813-2
57. Engineering-Geological Features Supporting a Seismic-Driven Multi-Hazard Scenario in the Lake Campotosto Area (L’Aquila, Italy) B. Antonielli et al. 10.3390/geosciences11030107
58. Evaluation of soil liquefaction potential index based on SPT data in the Erzincan, Eastern Turkey E. Duman et al. 10.1007/s12517-014-1550-4
59. Coastal vulnerability assessment based on video wave run-up observations at a mesotidal, steep-sloped beach M. Vousdoukas et al. 10.1007/s10236-011-0480-x
60. A novel ensemble model based on GMDH-type neural network for the prediction of CPT-based soil liquefaction T. Kurnaz & Y. Kaya 10.1007/s12665-019-8344-7
61. Estimation of contact forces of granular materials under uniaxial compression based on a machine learning model Z. Cheng & J. Wang 10.1007/s10035-021-01160-z
62. Evaluation of Liquefaction Potential of Coastal Alluvium Using SPT Data - A Comparative Case Study P. Muduli et al. 10.1088/1757-899X/970/1/012033
63. Neural transfer learning for soil liquefaction tests Y. Fang et al. 10.1016/j.cageo.2022.105282
64. Suitability assessment of the best liquefaction analysis procedure based on SPT data D. Kumar et al. 10.1007/s41939-023-00148-x
65. A novel liquefaction study for fine-grained soil using PCA-based hybrid soft computing models S. GHANI et al. 10.1007/s12046-021-01640-1
66. Back-calculation of soil parameters from displacement-controlled cavity expansion under geostatic stress by FEM and machine learning F. Patino-Ramirez et al. 10.1007/s11440-022-01698-z
67. Assessment of earthquake-induced liquefaction susceptibility using ensemble learning S. Dadhich et al. 10.1007/s41939-023-00146-z
68. Mapping the vulnerability for evacuation of the Campi Flegrei territorial system in case of a volcanic unrest I. Alberico et al. 10.1007/s11069-012-0335-x
69. Application of machine learning to the Vs-based soil liquefaction potential assessment Q. Sui et al. 10.1007/s11629-022-7809-4
70. Relevance vector machines approach for long-term flow prediction U. Okkan et al. 10.1007/s00521-014-1626-9
71. Probabilistic-Based Assessment of Liquefaction-Induced Damage with Analytical Fragility Curves D. Forcellini 10.3390/geosciences10080315
72. Stiffness and Strength of Stabilized Organic Soils—Part II/II: Parametric Analysis and Modeling with Machine Learning N. Yousefpour et al. 10.3390/geosciences11050218
73. Development of Prediction Models for Shear Strength of Rockfill Material Using Machine Learning Techniques M. Ahmad et al. 10.3390/app11136167
74. Model uncertainty of SPT-based method for evaluation of seismic soil liquefaction potential using multi-gene genetic programming P. Muduli & S. Das 10.1016/j.sandf.2015.02.003
75. Effects of the subtropical anticyclones over North Africa and Arabian Peninsula on the African easterly jet J. Spinks et al. 10.1002/joc.4017
76. Modelling and validation of liquefaction potential index of fine-grained soils using ensemble learning paradigms S. Ghani et al. 10.1016/j.soildyn.2023.108399
77. Liquefaction Potential Assessment of Soils Using Machine Learning Techniques: A State-of-the-Art Review from 1994–2021 K. Jas & G. Dodagoudar 10.1061/IJGNAI.GMENG-7788
78. A deep learning approach for rapid detection of soil liquefaction using time–frequency images W. Zhang et al. 10.1016/j.soildyn.2023.107788
79. Equivalent-Linear Seismic Ground Response Analysis of Some Typical Sites in Mumbai V. Phanikanth et al. 10.1007/s10706-011-9443-8
80. A Novel Methodology to Classify Soil Liquefaction Using Deep Learning D. Kumar et al. 10.1007/s10706-020-01544-7
81. 35 Years of (AI) in Geotechnical Engineering: State of the Art A. Ebid 10.1007/s10706-020-01536-7
82. Block and boulder accumulations along the coastline between Fins and Sur (Sultanate of Oman): tsunamigenic remains? G. Hoffmann et al. 10.1007/s11069-012-0399-7
83. Seismic Liquefaction Analysis of MCDM Weighted SPT Data Using Support Vector Machine Classification V. Alla et al. 10.1007/s40996-023-01293-6
84. A Liquefaction Study Using ENN, CA, and Biogeography Optimized-Based ANFIS Technique S. Umar et al. 10.4018/IJAMC.290535
85. Evaluation and analysis of liquefaction potential of gravelly soils using explainable probabilistic machine learning model K. Jas et al. 10.1016/j.compgeo.2023.106051
86. Evaluation of soil liquefaction potential using ensemble classifier based on grey wolves optimizer (GWO) N. Reddy et al. 10.1016/j.soildyn.2024.108750
87. Evaluation of liquefaction potential of soil based on standard penetration test using multi-gene genetic programming model P. Muduli & S. Das 10.2478/s11600-013-0181-6
88. Liquefaction prediction with robust machine learning algorithms (SVM, RF, and XGBoost) supported by genetic algorithm-based feature selection and parameter optimization from the perspective of data processing S. Demir & E. Şahin 10.1007/s12665-022-10578-4
89. Rainfall–runoff modeling using least squares support vector machines U. Okkan & Z. Serbes 10.1002/env.2154
90. Evaluation of liquefaction potential based on CPT data using random forest V. Kohestani et al. 10.1007/s11069-015-1893-5
91. Deterministic and Probabilistic Analysis of Liquefaction for Different Regions in Bihar S. Umar et al. 10.1007/s10706-018-0498-7
92. Seismic liquefaction potential assessed by support vector machines approaches X. Xue & X. Yang 10.1007/s10064-015-0741-x
93. SPT-based liquefaction assessment with a novel ensemble model based on GMDH-type neural network T. Kurnaz & Y. Kaya 10.1007/s12517-019-4640-5
94. Application of Feedforward Neural Network and SPT Results in the Estimation of Seismic Soil Liquefaction Triggering T. Pham & J. Hernández-Pérez 10.1155/2021/1058825
95. A comparative analysis of ensemble learning algorithms with hyperparameter optimization for soil liquefaction prediction A. Demir et al. 10.1007/s12665-024-11600-7
96. Ground based radon (222Rn) observations in Bucharest, Romania and their application to geophysics M. Zoran et al. 10.1007/s10967-012-1761-7
97. Prognostic of Soil Nutrients and Soil Fertility Index Using Machine Learning Classifier Techniques . Swapna B. et al. 10.4018/IJeC.304034
98. Earthquake-induced liquefaction hazard mapping at national-scale in Australia using deep learning techniques R. Jena et al. 10.1016/j.gsf.2022.101460
99. Soil Liquefaction Prediction Based on Bayesian Optimization and Support Vector Machines X. Zhang et al. 10.3390/su141911944
100. Why “AI” models for predicting soil liquefaction have been ignored, plus some that shouldn’t be B. Maurer & M. Sanger 10.1177/87552930231173711
101. Long-term sustainability of biogas bubbles in sand X. Hu et al. 10.1038/s41598-020-69324-0
102. Application of genetic algorithm-based support vector machines for prediction of soil liquefaction X. Xue & M. Xiao 10.1007/s12665-016-5673-7
103. Evaluation of soil liquefaction using AI technology incorporating a coupled ENN / t-SNE model P. Atangana Njock et al. 10.1016/j.soildyn.2019.105988
104. Application of artificial intelligence techniques in prediction of cyclic resistance ratio (CRR) of clean sands V. Akhila & S. Adarsh 10.1088/1755-1315/491/1/012048