Predicting liquefaction-induced lateral spreading by using the multigene genetic programming (MGGP), multilayer perceptron (MLP), and random forest (RF) techniques

[1]  A. Derakhshani,et al.  Uncertainty analysis of liquefaction-induced lateral spreading using fuzzy variables and genetic algorithm , 2021, Bulletin of Engineering Geology and the Environment.

[2]  T. Pham.  Application of Feedforward Neural Network and SPT Results in the Estimation of Seismic Soil Liquefaction Triggering , 2021, Comput. Intell. Neurosci..

[3]  Y. Ko,et al.  A Comparison of Simplified Modelling Approaches for Performance Assessment of Piles Subjected to Lateral Spreading of Liquefied Ground , 2020 .

[4]  Jie Luo,et al.  The deformation monitoring of foundation pit by back propagation neural network and genetic algorithm and its application in geotechnical engineering , 2020, PloS one.

[5]  A. Namdar.  Forecasting bearing capacity of the mixed soil using artificial neural networking , 2020 .

[6]  Wengang Zhang,et al.  Assessment of basal heave stability for braced excavations in anisotropic clay using extreme gradient boosting and random forest regression , 2020 .

[7]  S. H. Li,et al.  Multiple data-driven approach for predicting landslide deformation , 2019, Landslides.

[8]  Amir Hossein Alavi,et al.  An Intelligent Model for the Prediction of Bond Strength of FRP Bars in Concrete: A Soft Computing Approach , 2019, Technologies.

[9]  A. Dey,et al.  Assessment of bearing capacity of interfering strip footings located near sloping surface considering artificial neural network technique , 2018, Journal of Mountain Science.

[10]  Ali Derakhshani,et al.  New formulas for predicting liquefaction-induced lateral spreading: model tree approach , 2018, Bulletin of Engineering Geology and the Environment.

[11]  Zulkuf Kaya,et al.  Predicting Liquefaction-Induced Lateral Spreading by Using Neural Network and Neuro-Fuzzy Techniques , 2016 .

[12]  Zhihua Feng,et al.  Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks , 2015, Comput. Intell. Neurosci..

[13]  Han-long Liu,et al.  Physical modeling of lateral spreading induced by inclined sandy foundation in the state of zero effective stress , 2015 .

[14]  Shuhui Li,et al.  Training Recurrent Neural Networks With the Levenberg–Marquardt Algorithm for Optimal Control of a Grid-Connected Converter , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[15]  Rafiqul A. Tarefder,et al.  Neural Network–Based Thickness Determination Model to Improve Backcalculation of Layer Moduli without Coring , 2015 .

[16]  Dominic P. Searson.  GPTIPS 2: An Open-Source Software Platform for Symbolic Data Mining , 2014, Handbook of Genetic Programming Applications.

[17]  Anthony T. C. Goh,et al.  An improvement to MLR model for predicting liquefaction-induced lateral spread using multivariate adaptive regression splines , 2014 .

[18]  Solomon Tesfamariam,et al.  Prediction of lateral spread displacement: data-driven approaches , 2012, Bulletin of Earthquake Engineering.

[19]  A. Alipour,et al.  Application of ANNs and MVLRA for Estimation of Specific Charge in Small Tunnel , 2012 .

[20]  A. Roli.  Artificial Neural Networks , 2012, Lecture Notes in Computer Science.

[21]  A. Gandomi,et al.  A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems , 2012, Neural Computing and Applications.

[22]  Huiming Tang,et al.  Time Series Prediction of Chimney Foundation Settlement by Neural Networks , 2011 .

[23]  Akbar A. Javadi,et al.  An evolutionary based approach for assessment of earthquake-induced soil liquefaction and lateral displacement , 2011, Eng. Appl. Artif. Intell..

[24]  Thomas Hilker,et al.  Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery , 2011 .

[25]  Thomas Oommen,et al.  Model Development and Validation for Intelligent Data Collection for Lateral Spread Displacements , 2010, J. Comput. Civ. Eng..

[26]  Oguz Kaynar,et al.  Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils , 2010, Expert Syst. Appl..

[27]  Miguel P. Romo,et al.  A neurofuzzy system to analyze liquefaction-induced lateral spread , 2008 .

[28]  Y. Hashash,et al.  Novel Approach to Integration of Numerical Modeling and Field Observations for Deep Excavations , 2006 .

[29]  Akbar A. Javadi,et al.  Evaluation of liquefaction induced lateral displacements using genetic programming , 2006 .

[30]  H. Wang,et al.  Slope stability evaluation using Back Propagation Neural Networks , 2005 .

[31]  Jian Zhang,et al.  Empirical models for estimating liquefaction-induced lateral spread displacement , 2005 .

[32]  Mahesh Pal,et al.  Random forest classifier for remote sensing classification , 2005 .

[33]  Steven F. Bartlett,et al.  Revised Multilinear Regression Equations for Prediction of Lateral Spread Displacement , 2002 .

[34]  J. Bray,et al.  Liquefaction-induced ground deformations at Hotel Sapanca during Kocaeli (Izmit),Turkey earthquake , 2002 .

[35]  L. Breiman.  Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[36]  M Chiru-Danzer,et al.  Estimation of liquefaction-induced horizontal displacements using artificial neural networks , 2001 .

[37]  Alan F. Rauch,et al.  EPOLLS Model for Predicting Average Displacements on Lateral Spreads , 2000 .

[38]  Jun Wang,et al.  A neural network model for liquefaction-induced horizontal ground displacement , 1999 .

[39]  C. Hsein Juang,et al.  CPT‐Based Liquefaction Evaluation Using Artificial Neural Networks , 1999 .

[40]  Kohji Tokimatsu,et al.  New charts for predicting large residual post-liquefaction ground deformation , 1998 .

[41]  Alan F. Rauch,et al.  EPOLLS: An Empirical Method for Prediciting Surface Displacements Due to Liquefaction-Induced Lateral Spreading in Earthquakes , 1997 .

[42]  Anthony T. C. Goh,et al.  Pile Driving Records Reanalyzed Using Neural Networks , 1996 .

[43]  John R. Koza,et al.  Genetic programming as a means for programming computers by natural selection , 1994 .

[44]  N. N. Ambraseys,et al.  Earthquake‐induced ground displacements , 1988 .

[45]  T. Leslie Youd,et al.  Mapping of Liquefaction Severity Index , 1987 .

[46]  Wengang Zhang,et al.  Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization , 2021 .

[47]  Hongyuan Zha,et al.  Computational Statistics Data Analysis , 2021 .

[48]  H. Zedira,et al.  C OMPARING NONLINEAR REGRESSION ANALYSIS AND ARTIFICIAL NEURAL NETWORKS TO PREDICT GEOTECHNICAL PARAMETERS FROM STANDARD PENETRATION TEST , 2018 .

[49]  Maria Jolanta Sulewska,et al.  Neural modelling of compactibility characteristics of cohesionless soil , 2010 .

[50]  Dominic P. Searson,et al.  GPTIPS: An Open Source Genetic Programming Toolbox For Multigene Symbolic Regression , 2010 .

[51]  Kellie J. Archer,et al.  Empirical characterization of random forest variable importance measures , 2008, Comput. Stat. Data Anal..

[52]  M. Baziar,et al.  Evaluation of lateral spreading using artificial neural networks , 2005 .

[53]  Mark Kotanchek,et al.  Pareto-Front Exploitation in Symbolic Regression , 2005 .

[54]  Holger R. Maier,et al.  ARTIFICIAL NEURAL NETWORK APPLICATIONS IN GEOTECHNICAL ENGINEERING , 2001 .

[55]  M. Hamada.  Large ground deformations and their effects on lifelines : 1964 Niigata earthquake, Case Studies of Liquefaction and Lifeline Performance during Past Earthquakes , 1992 .

[56]  Steven F. Bartlett,et al.  Empirical Analysis of Horizontal Ground Displacement Generated by Liquefaction-Induced Lateral Spreads , 1992 .

[57]  M. Hamada.  Large Ground Deformations and Their Effects on Lifelines: 1983 Nihonkai-chubu Earthquake,Case Studies of Liquefaction and Lifeline Performance during Past Earthquakes , 1992 .