A novel approach to predicting young’s modulus of jet grouting laboratory formulations over time using data mining techniques

[1]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[2]  Paulo Cortez,et al.  Using sensitivity analysis and visualization techniques to open black box data mining models , 2013, Inf. Sci..

[3]  Ebru Akcapinar Sezer,et al.  An application of adaptive neuro fuzzy inference system for estimating the uniaxial compressive strength of certain granitic rocks from their mineral contents , 2013, Expert Syst. Appl..

[4]  Suksun Horpibulsuk,et al.  Jet grouting with a newly developed technology: The Twin-Jet method , 2013 .

[5]  Pedro M. Domingos A few useful things to know about machine learning , 2012, Commun. ACM.

[6]  Ali Akbar Ramezanianpour,et al.  Hybrid support vector regression – Particle swarm optimization for prediction of compressive strength and RCPT of concretes containing metakaolin , 2012 .

[7]  Joaquim Agostinho Barbosa Tinoco Application of a sensitivity analysis procedure to interpret uniaxial compressive strength prediction of jet grouting laboratory formulations performed by SVM model , 2012 .

[8]  António Gomes Correia,et al.  Using Data Mining Techniques to Predict Deformability Properties of Jet Grouting Laboratory Formulations over Time , 2011, EPIA.

[9]  Joaquim Agostinho Barbosa Tinoco,et al.  A data mining approach for predicting jet grouting geomechanical parameters , 2011 .

[10]  M. Azenha,et al.  Continuous Stiffness Monitoring of Cemented Sand through Resonant Frequency , 2011 .

[11]  A. Correia,et al.  A macro geomechanical approach to rank non-standard unbound granular materials for pavements , 2011 .

[12]  Paulo Cortez,et al.  Opening black box Data Mining models using Sensitivity Analysis , 2011, 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

[13]  Joaquim Agostinho Barbosa Tinoco,et al.  Application of data mining techniques in the estimation of the uniaxial compressive strength of jet grouting columns over time , 2011 .

[14]  António Gomes Correia,et al.  Application of Data Mining Techniques in the Estimation of Mechanical Properties of Jet Grouting Laboratory Formulations over Time , 2011 .

[15]  Joaquim Agostinho Barbosa Tinoco,et al.  Application of data mining techniques to estimate elastic young modulus over time of jet grouting laboratory formulations , 2010 .

[16]  Paulo Cortez,et al.  Data Mining with Neural Networks and Support Vector Machines Using the R/rminer Tool , 2010, ICDM.

[17]  Kaveh Ahangari,et al.  Estimation of jet grouting parameters in Shahriar dam, Iran , 2010 .

[18]  António Gomes Correia,et al.  A data mining approach for Jet Grouting Uniaxial Compressive Strength prediction , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[19]  Joaquim Agostinho Barbosa Tinoco,et al.  Evaluation of mechanical properties of jet-grouting columns using different test methods , 2009 .

[20]  Yong-Quan Zhou,et al.  Application of Functional Network to Solving Classification Problems , 2005, IEC.

[21]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[22]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[23]  A. Correia Evaluation of mechanical properties of unbound granular materials for pavements and rail tracks , 2004 .

[24]  Yunqian Ma,et al.  Practical selection of SVM parameters and noise estimation for SVM regression , 2004, Neural Networks.

[25]  Amparo Alonso-Betanzos,et al.  Shear strength prediction using dimensional analysis and functional networks , 2004, ESANN.

[26]  Jinbo Bi,et al.  Regression Error Characteristic Curves , 2003, ICML.

[27]  Mitsuhiro Shibazaki,et al.  State of Practice of Jet Grouting , 2003 .

[28]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[29]  Arie Ben-David,et al.  Control of properties in injection molding by neural networks , 2001 .

[30]  Zhongfu Wu,et al.  Complex functional networks , 2001 .

[31]  Enrique F. Castillo,et al.  Some Applications of Functional Networks in Statistics and Engineering , 2001, Technometrics.

[32]  Enrique F. Castillo,et al.  Functional networks with applications: a neural-based paradigm [Book Review] , 1999, IEEE Transactions on Neural Networks.

[33]  José Manuel Gutiérrez,et al.  Functional Networks with Applications: A Neural-Based , 1999 .

[34]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[35]  Jyh-Jong Liao,et al.  An empirical strength criterion for jet grouted soilcrete , 1994 .

[36]  C. S. Covil,et al.  Jet grouting—a review of some of the operating parameters that form the basis of the jet grouting process , 1994 .

[37]  Lee W. Abramson,et al.  Ground control and improvement , 1994 .