Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

A robust data mining approach for formulation of geotechnical engineering systems

A robust data mining approach for formulation of geotechnical engineering systems Purpose – The complexity of analysis of geotechnical behavior is due to multivariable dependencies of soil and rock responses. In order to cope with this complex behavior, traditional forms of engineering design solutions are reasonably simplified. Incorporating simplifying assumptions into the development of the traditional models may lead to very large errors. The purpose of this paper is to illustrate capabilities of promising variants of genetic programming (GP), namely linear genetic programming (LGP), gene expression programming (GEP), and multi‐expression programming (MEP) by applying them to the formulation of several complex geotechnical engineering problems. Design/methodology/approach – LGP, GEP, and MEP are new variants of GP that make a clear distinction between the genotype and the phenotype of an individual. Compared with the traditional GP, the LGP, GEP, and MEP techniques are more compatible with computer architectures. This results in a significant speedup in their execution. These methods have a great ability to directly capture the knowledge contained in the experimental data without making assumptions about the underlying rules governing the system. This is one of their major advantages over most of the traditional constitutive modeling methods. Findings – In order to demonstrate the simulation capabilities of LGP, GEP, and MEP, they were applied to the prediction of: relative crest settlement of concrete‐faced rockfill dams; slope stability; settlement around tunnels; and soil liquefaction. The results are compared with those obtained by other models presented in the literature and found to be more accurate. LGP has the best overall behavior for the analysis of the considered problems in comparison with GEP and MEP. The simple and straightforward constitutive models developed using LGP, GEP and MEP provide valuable analysis tools accessible to practicing engineers. Originality/value – The LGP, GEP, and MEP approaches overcome the shortcomings of different methods previously presented in the literature for the analysis of geotechnical engineering systems. Contrary to artificial neural networks and many other soft computing tools, LGP, GEP, and MEP provide prediction equations that can readily be used for routine design practice. The constitutive models derived using these methods can efficiently be incorporated into the finite element or finite difference analyses as material models. They may also be used as a quick check on solutions developed by more time consuming and in‐depth deterministic analyses. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Engineering Computations Emerald Publishing

A robust data mining approach for formulation of geotechnical engineering systems

Download PDF
33 pages
Read Article

Loading next page...
 
/lp/emerald-publishing/a-robust-data-mining-approach-for-formulation-of-geotechnical-SLImZqYCKT
Publisher
Emerald Publishing
Copyright
Copyright © 2011 Emerald Group Publishing Limited. All rights reserved.
ISSN
0264-4401
DOI
10.1108/02644401111118132
Publisher site
See Article on Publisher Site

Abstract

Purpose – The complexity of analysis of geotechnical behavior is due to multivariable dependencies of soil and rock responses. In order to cope with this complex behavior, traditional forms of engineering design solutions are reasonably simplified. Incorporating simplifying assumptions into the development of the traditional models may lead to very large errors. The purpose of this paper is to illustrate capabilities of promising variants of genetic programming (GP), namely linear genetic programming (LGP), gene expression programming (GEP), and multi‐expression programming (MEP) by applying them to the formulation of several complex geotechnical engineering problems. Design/methodology/approach – LGP, GEP, and MEP are new variants of GP that make a clear distinction between the genotype and the phenotype of an individual. Compared with the traditional GP, the LGP, GEP, and MEP techniques are more compatible with computer architectures. This results in a significant speedup in their execution. These methods have a great ability to directly capture the knowledge contained in the experimental data without making assumptions about the underlying rules governing the system. This is one of their major advantages over most of the traditional constitutive modeling methods. Findings – In order to demonstrate the simulation capabilities of LGP, GEP, and MEP, they were applied to the prediction of: relative crest settlement of concrete‐faced rockfill dams; slope stability; settlement around tunnels; and soil liquefaction. The results are compared with those obtained by other models presented in the literature and found to be more accurate. LGP has the best overall behavior for the analysis of the considered problems in comparison with GEP and MEP. The simple and straightforward constitutive models developed using LGP, GEP and MEP provide valuable analysis tools accessible to practicing engineers. Originality/value – The LGP, GEP, and MEP approaches overcome the shortcomings of different methods previously presented in the literature for the analysis of geotechnical engineering systems. Contrary to artificial neural networks and many other soft computing tools, LGP, GEP, and MEP provide prediction equations that can readily be used for routine design practice. The constitutive models derived using these methods can efficiently be incorporated into the finite element or finite difference analyses as material models. They may also be used as a quick check on solutions developed by more time consuming and in‐depth deterministic analyses.

Journal

Engineering Computations – Emerald Publishing

Published: Apr 5, 2011

Keywords: Data collection; Geotechnical engineering; Programming and algorithm theory; Systems analysis

References

  • Neural networks in civil engineering: 1989‐2000
    Adeli, H.
  • Non‐deterministic tunneling analysis using AI based techniques genetic programming vs ANNs
    Ahmed, A.A.; Ali, H.A.; ElAraby, S.M.; ElKateb, M.; Noureldin, S.M.
  • Energy‐based numerical correlations for soil liquefaction assessment
    Alavi, A.H.; Gandomi, A.H.
  • Discussion on soft computing approach for real‐time estimation of missing wave heights
    Alavi, A.H.; Gandomi, A.H.; Heshmati, A.A.R.
  • Prediction of maximum dry density and optimum moisture content of stabilized soil using RBF neural networks
    Alavi, A.H.; Gandomi, A.H.; Gandomi, M.; Sadat Hosseini, S.S.
  • Multi expression programming: a new approach to formulation of soil classification
    Alavi, A.H.; Gandomi, A.H.; Sahab, M.G.; Gandomi, M.
  • Modeling of maximum dry density and optimum moisture content of stabilized soil using artificial neural networks
    Alavi, A.H.; Gandomi, A.H.; Mollahasani, A.; Heshmati, A.A.R.; Rashed, A.
  • Utilisation of computational intelligence techniques for stabilised soil
    Alavi, A.H.; Heshmati, A.A.R.; Gandomi, A.H.; Askarinejad, A.; Mirjalili, M.
  • Prediction of subsidence due to underground mining by artificial neural networks
    Ambrozic, T.; Turk, G.
  • Guidelines on Concrete‐faced Rockfill Dams
    ANCLDI
  • Subsidence above shallow tunnels in soft ground
    Atkinson, J.H.; Potts, D.M.
  • Ground deformations resulting from shield tunneling in London clay
    Attewell, P.B.; Farmer, I.W.
  • Genetic Programming – An Introduction. On the Automatic Evolution of Computer Programs and its Application
    Banzhaf, W.; Nordin, P.; Keller, R.; Francone, F.
  • Generating prediction rules for liquefaction through data mining
    Baykasoglu, A.; Çevik, A.; Özbakır, L.; Sinem, K.
  • Prediction of compressive and tensile strength of limestone via genetic programming
    Baykasoglu, A.; Gullub, H.; Canakcı, H.; Ozbakır, L.
  • Highlights of Brazilian Dam Engineering
    BCD
  • A comparison of linear genetic programming and neural networks in medical data mining
    Brameier, M.; Banzhaf, W.
  • Linear Genetic Programming
    Brameier, M.; Banzhaf, W.
  • Genetic programming‐based attenuation relationships: an application of recent earthquakes in Turkey
    Cabalar, A.F.; Cevik, A.
  • Modelling damping ratio and shear modulus of sand‐mica mixtures using genetic programming
    Cevik, A.; Cabalar, A.F.
  • Post‐construction deformation of rockfill dams
    Clements, R.P.
  • Design and performance of excavations and tunnels in softclay
    Clough, W.; Schmidt, B.
  • Discipulus – Fast Genetic Programming based on AIM Learning Technology
    Conrads, M.; Dolezal, O.; Francone, F.D.; Nordin, P.
  • Progress in rockfill dams
    Cooke, J.B.
  • A representation for the adaptive generation of simple sequential programs
    Cramer, N.L.
  • Genetic algorithms in probabilistic finite element analysis of geotechnical problems
    Cui, L.; Sheng, D.
  • Liquefaction phenomenon of granular materials and constitutive instability
    Darve, F.
  • Constitutive Laws for Engineering Materials: with Emphasis on Geologic Materials
    Desai, C.S.; Siriwardane, H.J.
  • Prediction of pore water pressure buildup and liquefaction of sands during earthquakes by the cyclic strain method
    Dobry, R.; Ladd, R.S.; Yokel, F.Y.; Chung, R.M.; Powell, D.
  • State of the art: limit equilibrium and finite element analysis of slopes
    Duncan, J.M.
  • Artificial neural networks applied to landslide susceptibility assessment
    Ermini, L.; Catani, F.; Casagli, N.
  • Gene expression programming: a new adaptive algorithm for solving problems
    Ferreira, C.
  • Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence
    Ferreira, C.
  • Extending the boundaries of design optimization by integrating fast optimization techniques with machine‐code‐based, linear genetic programming
    Francone, F.D.; Deschaine, L.M.
  • The Data Analysis Handbook
    Frank, I.E.; Todeschini, R.
  • A learning machine: Part I
    Friedberg, R.M.
  • Nonlinear genetic‐based models for prediction of flow number of asphalt mixtures
    Gandomi, A.H.; Alavi, A.H.; Mirzahosseini, M.R.; Moghadas Nejad, F.
  • GeneXproTools Owner's Manual
    GEPSOFT
  • Seismic liquefaction potential assessed by neural networks
    Goh, A.T.C.
  • Genetic algorithm search for critical slip surface in multiple‐wedge stability analysis
    Goh, A.T.C.
  • Support vector machines: their use in geotechnical engineering as illustrated using seismic liquefaction data
    Goh, A.T.C.; Goh, S.H.
  • Evaluation of liquefaction potential of soil deposits using artificial neural networks
    Hanna, A.M.; Ural, D.; Saygili, G.
  • Comparison of two inverse analysis techniques for learning deep excavation response
    Hashash, Y.M.A.; Levasseurb, S.; Osoulia, A.; Finno, R.; Malecot, Y.
  • The pre‐ and post‐failure deformation behavior of soil slopes
    Hunter, G.J.
  • Rockfill modulus and settlement of concrete face rockfill dams
    Hunter, G.J.; Fell, R.
  • Estimation of air losses in compressed air tunneling using neural network
    Javadi, A.A.
  • Applications of artificial intelligence and data mining techniques in soil modeling
    Javadi, A.A.; Rezania, M.
  • Evaluation of liquefaction induced lateral displacements using genetic programming
    Javadi, A.A.; Rezani, M.; Mousavi Nezhad, M.
  • Intelligent finite element method
    Javadi, A.A.; Tan, T.P.; Elkassas, A.S.I.
  • Prediction of soil‐water characteristic curve using genetic programming
    Johari, A.; Habibagahi, G.; Ghahramani, A.
  • Three‐dimensional site characterisation: neural network approach
    Juang, C.H.; Jiang, T.; Christopher, R.A.
  • Simplified cone penetration test‐based method for evaluating liquefaction resistance of soils
    Juang, C.H.; Yuan, H.; Lee, D.; Lin, P.
  • Modeling of the angle of shearing resistance of soils using soft computing systems
    Kayadelen, C.; Günaydın, O.; Fener, M.; Demir, A.; Özvan, A.
  • Prediction of relative crest settlement of concrete‐faced rockfill dams analyzed using an artificial neural network model
    Kim, Y.S.; Kim, B.T.
  • Genetic Programming, on the Programming of Computers by Means of Natural Selection
    Koza, J.R.
  • Fuzzy neural network as instance generator for case‐based reasoning system: an example of selection of heat exchange equipment in mixing
    Kraslawski, A.; Pedrycz, W.; Nyström, L.
  • Soil parameter identification using a genetic algorithm
    Levasseur, S.; Malécot, Y.; Boulon, M.; Flavigny, E.
  • Statistical inverse analysis based on genetic algorithm and principal component analysis: method and developments using synthetic data
    Levasseur, S.; Malécot, Y.; Boulon, M.; Flavigny, E.
  • Fuzzy genetic programming method for analysis of ground movements due to underground mining, technical note
    Li, W.; Daib, L.; Houa, X.; Leia, W.
  • Fuzzy models for analysis of rock mass displacements due to underground mining in mountainous areas
    Li, W.; Mei, S.; Zhai, S.; Zhao, S.; Liang, X.
  • Development of an energy method for evaluating the liquefaction potential of a soil deposit
    Liang, L.
  • Construction materials selection and characteristics of Wan An Xi concrete faced rockfill dam
    Liu, F.M.; Chen, Y.B.; Liu, J.; Ni, Y.L.
  • The use of the simple genetic algorithm in finding the critical factor of safety in slope stability analysis
    McCombie, P.; Wilkinson, P.
  • Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses
    Majdi, A.; Beiki, M.
  • Practical Neural Network Recipes in C++
    Masters, T.
  • Cartesian genetic programming
    Miller, J.; Thomson, P.
  • Prediction of unconfined compressive strength of soft grounds using computational intelligence techniques: a comparative study
    Narendra, B.S.; Sivapullaiah, P.V.; Suresh, S.; Omkar, S.N.
  • A comparative review of limit equilibrium methods of stability analysis
    Nash, D.
  • In evaluation of liquefaction resistance of soils
    NCEER
  • Use of backpropagation neural network for landslide monitoring: a case study in the higher Himalaya
    Neaupane, K.M.; Achet, S.H.
  • Prediction of tunneling‐induced ground movement with the multi‐layer perceptron
    Neaupane, K.M.; Adhikari, N.R.
  • Liquefaction of Soils During Earthquakes
    NRC
  • Multi expression programming source code
    Oltean, M.
  • Multi expression programming
    Oltean, M.; Dumitrescu, D.
  • A comparison of several linear genetic programming techniques
    Oltean, M.; Grosşan, C.
  • Evolving evolutionary algorithms using multi expression programming
    Oltean, M.; Grosşan, C.
  • Solving classification problems using infix form genetic programming
    Oltean, M.; Grosşan, C.
  • Support vector machines‐based modelling of seismic liquefaction potential
    Pal, M.
  • Calibration of a constitutive model using genetic algorithms
    Pal, S.; Wije Wathugala, G.; Kundu, S.
  • Genetic programming with context‐sensitive grammars
    Patterson, N.
  • Deep excavation and tunneling in soft ground
    Peck, R.B.
  • Genetic programming: an introductory tutorial and a survey of techniques and applications
    Poli, R.; Langdon, W.B.; McPhee, N.F.; Koza, J.R.
  • A new genetic programming model for predicting settlement of shallow foundations
    Rezania, M.; Javadi, A.A.
  • Parallel Distributed Processing: Explorations in Microstructure of Cognition
    Rumelhart, D.E.; McClelland, J.
  • Simplified procedure for evaluating soil liquefaction potential
    Seed, H.B.; Idriss, I.M.
  • Recent advances in soil liquefaction engineering: a unified and consistent framework
    Seed, R.B.; Cetin, K.O.; Moss, R.E.S.; Kammerer, A.; Wu, J.; Pestana, J.M.; Riemer, M.F.; Sancio, R.B.; Bray, J.D.; Kayen, R.E.; Faris, A.
  • State of the art of artificial neural networks in geotechnical engineering
    Shahin, M.A.; Jaksa, M.B.; Maier, H.R.
  • Recent advances and future challenges for artificial neural systems in geotechnical engineering applications
    Shahin, M.A.; Jaksa, M.B.; Maier, H.R.
  • Artificial neural network applications in geotechnical engineering
    Shahin, M.A.; Maier, H.R.; Jaksa, M.B.
  • On self‐learning finite element code based on monitored response of structures
    Shin, H.S.; Pande, G.N.
  • The application of genetic algorithms to optimisation problems in geotechnics
    Simpson, A.R.; Priest, S.D.
  • Ground movement associated with 11 km water transmission bored tunnel in Bangkok subsoil
    Thongyot, T.
  • A genetic programming framework for content‐based image retrieval
    Torres, R.S.; Falcão, A.X.; Gonçalves, M.A.; Papa, J.P.; Zhang, B.; Fan, W.; Fox, E.A.
  • Slope stability evaluation using back propagation neural networks
    Wang, H.B.; Xu, W.Y.; Xu, R.C.
  • Resistance of soil to liquefaction and settlement
    Whitman, R.V.
  • Two‐stepped evolutionary algorithm and its application to stability analysis of slopes
    Yang, C.X.; Tham, L.G.; Feng, X.T.; Wang, Y.J.; Lee, P.K.K.
  • Prediction of ground settlements associated with shield tunneling
    Yoshikoshi, W.; Osamu, W.; Takagaki, N.