125 citations as recorded by crossref.

1. Stacking model based on six base classifiers to improve prediction of soil liquefaction: a multi-dataset study X. Yao et al.
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3. Assessment of liquefaction potential of the Erzincan, Eastern Turkey E. Duman et al.
4. Seismic liquefaction potential assessed by neural networks X. Xue & E. Liu
5. Patient rule-induction method for liquefaction potential assessment based on CPT data A. Kaveh et al.
6. 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.
7. On the tracking of shelly carbonate sands using deep learning M. Wu et al.
8. Detection of fatigue cracking in steel bridge girders: A support vector machine approach H. Hasni et al.
9. New insights on rainfall triggering flow-like landslides and flash floods in Campania (Southern Italy) N. Santangelo et al.
10. 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.
11. Probabilistic evaluation of CPT-based seismic soil liquefaction potential: towards the integration of interpretive structural modeling and bayesian belief network M. Ahmad et al.
12. A hyper parameterized artificial neural network approach for prediction of the factor of safety against liquefaction T. Kurnaz et al.
13. Assessing probabilistic hazard and risk for building, road network, and ecosystems: the case study of La Florida municipality, Galeras Volcano, Colombia G. Córdoba et al.
14. Empirical approach for prediction of bearing pressure of spread footings on clayey soil using artificial intelligence (AI) techniques P. Sultana et al.
15. Explainable machine learning model for liquefaction potential assessment of soils using XGBoost-SHAP K. Jas & G. Dodagoudar
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17. Seismically Induced Liquefaction Potential Assessment by Different Artificial Intelligence Procedures D. Kumar et al.
18. Evaluation of Liquefaction Potential of Soil Using Soft Computing Techniques P. Karna et al.
19. Predicting the strut forces of the steel supporting structure of deep excavation considering various factors by machine learning methods H. Hu et al.
20. Evaluation of liquefaction potential in central Taiwan using random forest method C. Liu et al.
21. A Comparative Study of Soil Liquefaction Assessment Using Machine Learning Models S. Hanandeh et al.
22. Prediction of pore-water pressure response to rainfall using support vector regression N. Babangida et al.
23. Influence of Sewage Sludge Ash on Clay Properties A. Cabalar et al.
24. Particle Swarm Optimization Variants for Solving Geotechnical Problems: Review and Comparative Analysis A. Kashani et al.
25. 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
26. Soil Saturation and Stability Analysis of a Test Site Slope Using the Shallow Landslide Instability Prediction (SLIP) Model L. Montrasio et al.
27. Estimation of soil liquefaction using artificial intelligence techniques: an extended comparison between machine and deep learning approaches E. Şehmusoğlu et al.
28. Liquefaction study of fine-grained soil using computational model S. Ghani & S. Kumari
29. Delegated Regressor, A Robust Approach for Automated Anomaly Detection in the Soil Radon Time Series Data M. Rafique et al.
30. SPT-Based Probabilistic Method for Evaluation of Liquefaction Potential of Soil Using Multi-Gene Genetic Programming P. Muduli & S. Das
31. CPT-based Seismic Liquefaction Potential Evaluation Using Multi-gene Genetic Programming Approach P. Muduli & S. Das
32. Assessment of liquefaction potential of Erzincan Province and its vicinity, Turkey E. Subası Duman & S. Ikizler
33. Predicting Earthquake-Induced Soil Liquefaction Based on Machine Learning Classifiers: A Comparative Multi-Dataset Study H. Guo et al.
34. Integrating the LSSVM and RBFNN models with three optimization algorithms to predict the soil liquefaction potential M. Cai et al.
35. Modeling of tensile strength of rocks materials based on support vector machines approaches N. Ceryan et al.
36. Prediction of Liquefied Soil Settlement Using Multilayer Perceptron with Bayesian Optimization N. Van Nguyen et al.
37. 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.
38. Entropy analysis for identifying significant parameters for seismic soil liquefaction A. Dalvi et al.
39. 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.
40. Prediction of Probability of Liquefaction Using Soft Computing Techniques D. Kumar et al.
41. Shear wave Velocity-Based Machine Learning Modeling for Prediction of Liquefaction Potential of Soil J. Naik et al.
42. Explainable Machine Learning-Based Ground Motion Characterization: Evaluating the Role of Geotechnical Variabilities on Response Parameters A. Chala et al.
43. The Application of Machine Learning Techniques in Geotechnical Engineering: A Review and Comparison W. Shao et al.
44. Deep learning to evaluate seismic-induced soil liquefaction and modified transfer learning between various data sources H. Guo et al.
45. Estimation of spatiotemporal response of rooted soil using a machine learning approach Z. Cheng et al.
46. Liquefaction behavior of Indo-Gangetic region using novel metaheuristic optimization algorithms coupled with artificial neural network S. Ghani & S. Kumari
47. Drought Severity and Frequency Analysis Aided by Spectral and Meteorological Indices in the Kurdistan Region of Iraq H. Gaznayee et al.
48. Enhancing seismic activity classification in augmented soil gas radon time series data through computational intelligence techniques N. Bashir et al.
49. A computationally efficient system for assessing near-real-time instability of regional unsaturated soil slopes under rainfall L. Li et al.
50. Employing a genetic algorithm and grey wolf optimizer for optimizing RF models to evaluate soil liquefaction potential J. Zhou et al.
51. Machine learning modeling for predicting soil liquefaction utilizing laboratory test data C. Huang et al.
52. Predicting seismic-induced liquefaction through ensemble learning frameworks M. Alobaidi et al.
53. Multi-objective feature selection (MOFS) algorithms for prediction of liquefaction susceptibility of soil based on in situ test methods S. Das et al.
54. A new approach for modeling of flow number of asphalt mixtures A. Alavi et al.
55. Prediction and interpretation of liquefaction occurrences using explainable machine learning models K. Kumar et al.
56. 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.
57. Large, deepwater slope failures: Implications for landslide-generated tsunamis C. Lo Iacono et al.
58. Assessment of Soil Liquefaction Potential Using Genetic Programming Using a Probability-Based Approach N. Reddy et al.
59. Adoption of tree-based machine learning algorithms and CPT data for liquefaction potential assessment S. Tian et al.
60. A novel approach for assessment of seismic induced liquefaction susceptibility of soil D. Kumar et al.
61. Exploring the possibility of assessing the damage degree of liquefaction based only on seismic records by artificial neural networks A. Kamura et al.
62. Application of machine learning in early warning system of geotechnical disaster: a systematic and comprehensive review S. Lin et al.
63. Machine Learning Techniques for Soil Characterization Using Cone Penetration Test Data A. Chala & R. Ray
64. Liquefaction assessment of soil based on SPT and CPT data using novel machine learning techniques: a practical solution R. Mustafa et al.
65. Bioclimatic analysis in a region of southern Italy (Calabria) T. Caloiero et al.
66. Explainable AI using ensemble machine learning with integrated SHapley additive explanations (SHAP)-Borda approach for estimation of the safety factor against soil liquefaction U. Dağdeviren et al.
67. Revealing the nature of soil liquefaction using machine learning S. Ghani et al.
68. Combining spatial autocorrelation with artificial intelligence models to estimate spatial distribution and risks of heavy metal pollution in agricultural soils E. Günal et al.
69. ESTIMATION OF SOIL LIQUEFACTION POTENTIAL IN THE DALAMAN RESIDENTIAL AREA USING A SUPERVISED MACHINE LEARNING MODEL O. Türe & E. Karacan
70. Liq-EBM: data-driven assessment of liquefaction triggering and associated ground damage Y. Hwang et al.
71. Engineering-Geological Features Supporting a Seismic-Driven Multi-Hazard Scenario in the Lake Campotosto Area (L’Aquila, Italy) B. Antonielli et al.
72. Evaluation of soil liquefaction potential index based on SPT data in the Erzincan, Eastern Turkey E. Duman et al.
73. Kernel-based machine learning model for liquefaction probability prediction and its application to the seismically active indo-gangetic plain S. Ghani
74. Coastal vulnerability assessment based on video wave run-up observations at a mesotidal, steep-sloped beach M. Vousdoukas et al.
75. A novel ensemble model based on GMDH-type neural network for the prediction of CPT-based soil liquefaction T. Kurnaz & Y. Kaya
76. Assessment of Soil Liquefaction Potential Prediction Using Synthetic Data and Soft Computing Techniques J. Naik et al.
77. Estimation of contact forces of granular materials under uniaxial compression based on a machine learning model Z. Cheng & J. Wang
78. Evaluation of Liquefaction Potential of Coastal Alluvium Using SPT Data - A Comparative Case Study P. Muduli et al.
79. Neural transfer learning for soil liquefaction tests Y. Fang et al.
80. Developing data driven framework to model earthquake induced liquefaction potential of granular terrain by machine learning classification models K. Onyelowe et al.
81. Soft voting ensemble classifier for liquefaction prediction based on SPT data P. Chithuloori & J. Kim
82. Suitability assessment of the best liquefaction analysis procedure based on SPT data D. Kumar et al.
83. A novel liquefaction study for fine-grained soil using PCA-based hybrid soft computing models S. GHANI et al.
84. Back-calculation of soil parameters from displacement-controlled cavity expansion under geostatic stress by FEM and machine learning F. Patino-Ramirez et al.
85. Assessment of earthquake-induced liquefaction susceptibility using ensemble learning S. Dadhich et al.
86. Mapping the vulnerability for evacuation of the Campi Flegrei territorial system in case of a volcanic unrest I. Alberico et al.
87. Application of machine learning to the Vs-based soil liquefaction potential assessment Q. Sui et al.
88. Relevance vector machines approach for long-term flow prediction U. Okkan et al.
89. Probabilistic-Based Assessment of Liquefaction-Induced Damage with Analytical Fragility Curves D. Forcellini
90. Stiffness and Strength of Stabilized Organic Soils—Part II/II: Parametric Analysis and Modeling with Machine Learning N. Yousefpour et al.
91. Development of Prediction Models for Shear Strength of Rockfill Material Using Machine Learning Techniques M. Ahmad et al.
92. Integration models for predicting earthquake-induced liquefaction considering influences of integration methods and base model selection strategies J. Hu et al.
93. Green AI for Energy-Efficient Ground Investigation: A Greedy Algorithm-Optimized AI Model for Subsurface Data Prediction S. Zhang et al.
94. Model uncertainty of SPT-based method for evaluation of seismic soil liquefaction potential using multi-gene genetic programming P. Muduli & S. Das
95. Effects of the subtropical anticyclones over North Africa and Arabian Peninsula on the African easterly jet J. Spinks et al.
96. Modelling and validation of liquefaction potential index of fine-grained soils using ensemble learning paradigms S. Ghani et al.
97. Liquefaction Potential Assessment of Soils Using Machine Learning Techniques: A State-of-the-Art Review from 1994–2021 K. Jas & G. Dodagoudar
98. A deep learning approach for rapid detection of soil liquefaction using time–frequency images W. Zhang et al.
99. Equivalent-Linear Seismic Ground Response Analysis of Some Typical Sites in Mumbai V. Phanikanth et al.
100. A Novel Methodology to Classify Soil Liquefaction Using Deep Learning D. Kumar et al.
101. 35 Years of (AI) in Geotechnical Engineering: State of the Art A. Ebid
102. Block and boulder accumulations along the coastline between Fins and Sur (Sultanate of Oman): tsunamigenic remains? G. Hoffmann et al.
103. Seismic Liquefaction Analysis of MCDM Weighted SPT Data Using Support Vector Machine Classification V. Alla et al.
104. A Liquefaction Study Using ENN, CA, and Biogeography Optimized-Based ANFIS Technique S. Umar et al.
105. Evaluation and analysis of liquefaction potential of gravelly soils using explainable probabilistic machine learning model K. Jas et al.
106. Evaluation of soil liquefaction potential using ensemble classifier based on grey wolves optimizer (GWO) N. Reddy et al.
107. Evaluation of liquefaction potential of soil based on standard penetration test using multi-gene genetic programming model P. Muduli & S. Das
108. 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
109. Rainfall–runoff modeling using least squares support vector machines U. Okkan & Z. Serbes
110. Evaluation of liquefaction potential based on CPT data using random forest V. Kohestani et al.
111. Deterministic and Probabilistic Analysis of Liquefaction for Different Regions in Bihar S. Umar et al.
112. Seismic liquefaction potential assessed by support vector machines approaches X. Xue & X. Yang
113. SPT-based liquefaction assessment with a novel ensemble model based on GMDH-type neural network T. Kurnaz & Y. Kaya
114. Application of Feedforward Neural Network and SPT Results in the Estimation of Seismic Soil Liquefaction Triggering T. Pham & J. Hernández-Pérez
115. A comparative analysis of ensemble learning algorithms with hyperparameter optimization for soil liquefaction prediction A. Demir et al.
116. Interpretable AI for Site-Adaptive Soil Liquefaction Assessment E. Torres & J. Dungca
117. Ground based radon (222Rn) observations in Bucharest, Romania and their application to geophysics M. Zoran et al.
118. Prognostic of Soil Nutrients and Soil Fertility Index Using Machine Learning Classifier Techniques . Swapna B. et al.
119. Earthquake-induced liquefaction hazard mapping at national-scale in Australia using deep learning techniques R. Jena et al.
120. Soil Liquefaction Prediction Based on Bayesian Optimization and Support Vector Machines X. Zhang et al.
121. Why “AI” models for predicting soil liquefaction have been ignored, plus some that shouldn’t be B. Maurer & M. Sanger
122. Long-term sustainability of biogas bubbles in sand X. Hu et al.
123. Application of genetic algorithm-based support vector machines for prediction of soil liquefaction X. Xue & M. Xiao
124. Evaluation of soil liquefaction using AI technology incorporating a coupled ENN / t-SNE model P. Atangana Njock et al.
125. Application of artificial intelligence techniques in prediction of cyclic resistance ratio (CRR) of clean sands V. Akhila & S. Adarsh