Soil liquefaction assessment by using hierarchical Gaussian Process model with integrated feature and instance based domain adaption for multiple data sources

[1]  Xianzhong Zhao.  AI in Civil Engineering , 2022, AI in Civil Engineering.

[2]  Antoine de Mathelin,et al.  ADAPT : Awesome Domain Adaptation Python Toolbox , 2021, ArXiv.

[3]  Jian Zhou,et al.  Performance evaluation of hybrid GA–SVM and GWO–SVM models to predict earthquake-induced liquefaction potential of soil: a multi-dataset investigation , 2021, Engineering with Computers.

[4]  Jilei Hu,et al.  Datasets for gravelly soil liquefaction case histories , 2021, Data in brief.

[5]  Mahdi Hasanipanah,et al.  Integrating the LSSVM and RBFNN models with three optimization algorithms to predict the soil liquefaction potential , 2021, Engineering with Computers.

[6]  Yong-gang Zhang,et al.  The adoption of deep neural network (DNN) to the prediction of soil liquefaction based on shear wave velocity , 2021, Bulletin of Engineering Geology and the Environment.

[7]  Yanfeng Shu,et al.  Transfer Learning and Deep Domain Adaptation , 2020, Advances and Applications in Deep Learning.

[8]  P. Samui,et al.  A Novel Methodology to Classify Soil Liquefaction Using Deep Learning , 2020, Geotechnical and Geological Engineering.

[9]  Wengang Zhang,et al.  Soil Liquefaction Assessment Using Soft Computing Approaches Based on Capacity Energy Concept , 2020, Geosciences.

[10]  Yuhang Wang,et al.  An ensemble method to improve prediction of earthquake-induced soil liquefaction: a multi-dataset study , 2020, Neural Computing and Applications.

[11]  Fabio Sigrist,et al.  Gaussian Process Boosting , 2020, J. Mach. Learn. Res..

[12]  Gerhard Nahler,et al.  Pearson Correlation Coefficient , 2020, Definitions.

[13]  Maral Goharzay,et al.  Computer-aided SPT-based reliability model for probability of liquefaction using hybrid PSO and GA , 2020, J. Comput. Des. Eng..

[14]  Mohamed A. Meguid,et al.  Predicting seismic-induced liquefaction through ensemble learning frameworks , 2019, Scientific Reports.

[15]  Yilmaz Kaya,et al.  SPT-based liquefaction assessment with a novel ensemble model based on GMDH-type neural network , 2019, Arabian Journal of Geosciences.

[16]  Jian Zhou,et al.  Feasibility of Stochastic Gradient Boosting Approach for Evaluating Seismic Liquefaction Potential Based on SPT and CPT Case Histories , 2019, Journal of Performance of Constructed Facilities.

[17]  H. Cui,et al.  Soil liquefaction mitigation in geotechnical engineering: An overview of recently developed methods , 2019, Soil Dynamics and Earthquake Engineering.

[18]  Mohammad Azadi,et al.  Improving prediction of soil liquefaction using hybrid optimization algorithms and a fuzzy support vector machine , 2019, Bulletin of Engineering Geology and the Environment.

[19]  Anna Veronika Dorogush,et al.  CatBoost: gradient boosting with categorical features support , 2018, ArXiv.

[20]  R. Kayen,et al.  Dataset on SPT-based seismic soil liquefaction , 2018, Data in brief.

[21]  Nicolas Macris,et al.  Entropy and mutual information in models of deep neural networks , 2018, NeurIPS.

[22]  Aleksey Bilogur,et al.  Missingno: a missing data visualization suite , 2018, J. Open Source Softw..

[23]  Ming Shao,et al.  Incomplete Multisource Transfer Learning , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[24]  Nhat-Duc Hoang,et al.  Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: a multi-dataset study , 2018, Bulletin of Engineering Geology and the Environment.

[25]  Tie-Yan Liu,et al.  LightGBM: A Highly Efficient Gradient Boosting Decision Tree , 2017, NIPS.

[26]  Maral Goharzay,et al.  A worldwide SPT-based soil liquefaction triggering analysis utilizing gene expression programming and Bayesian probabilistic method , 2017 .

[27]  José R. Dorronsoro,et al.  Deep Fisher Discriminant Analysis , 2017, IWANN.

[28]  R. Srikant,et al.  Why Deep Neural Networks for Function Approximation? , 2016, ICLR.

[29]  J. Jagan,et al.  An Alternative Method for Determination of Liquefaction Susceptibility of Soil , 2016, Geotechnical and Geological Engineering.

[30]  Mohamed S. Kamel,et al.  Ensemble Kernel Mean Matching , 2015, 2015 IEEE International Conference on Data Mining.

[31]  M. M. Monkul,et al.  Estimation of liquefaction potential from dry and saturated sandy soils under drained constant volume cyclic simple shear loading , 2015 .

[32]  Hesham A. Rakha,et al.  Applying Machine Learning Techniques to Transportation Mode Recognition Using Mobile Phone Sensor Data , 2015, IEEE Transactions on Intelligent Transportation Systems.

[33]  Gavin Hackeling,et al.  Mastering Machine Learning With scikit-learn , 2014 .

[34]  Roderick J. A. Little,et al.  Single Imputation Methods , 2014, Statistical Analysis with Missing Data, Third Edition.

[35]  David A. Roke,et al.  Decision Tree Approach for Soil Liquefaction Assessment , 2013, TheScientificWorldJournal.

[36]  R.B.J. Brinkgreve,et al.  Finite Element Modelling of Seismic Liquefaction in Soils , 2013 .

[37]  Gilles Louppe,et al.  Independent consultant , 2013 .

[38]  Max Kuhn,et al.  Applied Predictive Modeling , 2013 .

[39]  Miao Yu,et al.  Review of soil liquefaction characteristics during major earthquakes of the twenty-first century , 2013, Natural Hazards.

[40]  Xinhua Xue,et al.  Application of the adaptive neuro-fuzzy inference system for prediction of soil liquefaction , 2013, Natural Hazards.

[41]  A. Gandomi,et al.  Energy-based numerical models for assessment of soil liquefaction , 2012 .

[42]  D. Dunson,et al.  Efficient Gaussian process regression for large datasets. , 2011, Biometrika.

[43]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[44]  Pijush Samui,et al.  Machine learning modelling for predicting soil liquefaction susceptibility , 2011 .

[45]  Deepankar Choudhury,et al.  GIS-based soil liquefaction susceptibility map of Mumbai city for earthquake events , 2010 .

[46]  M. Kawanabe,et al.  Direct importance estimation for covariate shift adaptation , 2008 .

[47]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[48]  A. Goh,et al.  Support vector machines: Their use in geotechnical engineering as illustrated using seismic liquefaction data , 2007 .

[49]  Qiang Yang,et al.  Boosting for transfer learning , 2007, ICML '07.

[50]  A. Hanna,et al.  Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data , 2007 .

[51]  J. Demšar.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[52]  Ken Been,et al.  Soil Liquefaction: A Critical State Approach , 2006 .

[53]  Mahesh Pal,et al.  Support vector machines‐based modelling of seismic liquefaction potential , 2006 .

[54]  W. F. Marcuson,et al.  Liquefaction Resistance of Soils: Summary Report from the 1996 NCEER and 1998 NCEER/NSF Workshops on Evaluation of Liquefaction Resistance of Soils , 2001 .

[55]  I. Sobola,et al.  Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .

[56]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[57]  M Huberman,et al.  Application of the missing-indicator method in matched case-control studies with incomplete data. , 1999, American journal of epidemiology.

[58]  M. Clark,et al.  Increased metabolism of infused 1-methylxanthine by working muscle. , 1999, Acta physiologica Scandinavica.

[59]  Shuai Zhou,et al.  The Prediction of Self-Healing Capacity of Bacteria-Based Concrete Using Machine Learning Approaches , 2019, Computers, Materials & Continua.

[60]  Zhiqiang Ge,et al.  Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data , 2018, Annu. Rev. Control..

[61]  Tanja Hueber,et al.  Gaussian Processes For Machine Learning , 2016 .

[62]  Ferat Sahin,et al.  A survey on feature selection methods , 2014, Comput. Electr. Eng..

[63]  I. Towhata.  Geotechnical Earthquake Engineering , 2008 .

[64]  C. Hsein Juang,et al.  A rational method for development of limit state for liquefaction evaluation based on shear wave velocity measurements , 2000 .

[65]  Daryl Pregibon,et al.  Tree-based models , 1992 .

[66]  E. R. Kanasewich,et al.  Time sequence analysis in geophysics , 1973 .