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Formulation of secant and reloading soil deformation moduli using multi expression programming

Formulation of secant and reloading soil deformation moduli using multi expression programming Purpose – The purpose of this paper is to develop new constitutive models to predict the soil deformation moduli using multi expression programming (MEP). The soil deformation parameters formulated are secant (Es) and reloading (Er) moduli. Design/methodology/approach – MEP is a new branch of classical genetic programming. The models obtained using this method are developed upon a series of plate load tests conducted on different soil types. The best models are selected after developing and controlling several models with different combinations of the influencing parameters. The validation of the models is verified using several statistical criteria. For more verification, sensitivity and parametric analyses are carried out. Findings – The results indicate that the proposed models give precise estimations of the soil deformation moduli. The Es prediction model provides considerably better results than the model developed for Er. The Es formulation outperforms several empirical models found in the literature. The validation phases confirm the efficiency of the models for their general application to the soil moduli estimation. In general, the derived models are suitable for fine‐grained soils. Originality/value – These equations may be used by designers to check the general validity of the laboratory and field test results or to control the solutions developed by more in‐depth deterministic analyses. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Engineering Computations Emerald Publishing

Formulation of secant and reloading soil deformation moduli using multi expression programming

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Publisher
Emerald Publishing
Copyright
Copyright © 2012 Emerald Group Publishing Limited. All rights reserved.
ISSN
0264-4401
DOI
10.1108/02644401211206043
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to develop new constitutive models to predict the soil deformation moduli using multi expression programming (MEP). The soil deformation parameters formulated are secant (Es) and reloading (Er) moduli. Design/methodology/approach – MEP is a new branch of classical genetic programming. The models obtained using this method are developed upon a series of plate load tests conducted on different soil types. The best models are selected after developing and controlling several models with different combinations of the influencing parameters. The validation of the models is verified using several statistical criteria. For more verification, sensitivity and parametric analyses are carried out. Findings – The results indicate that the proposed models give precise estimations of the soil deformation moduli. The Es prediction model provides considerably better results than the model developed for Er. The Es formulation outperforms several empirical models found in the literature. The validation phases confirm the efficiency of the models for their general application to the soil moduli estimation. In general, the derived models are suitable for fine‐grained soils. Originality/value – These equations may be used by designers to check the general validity of the laboratory and field test results or to control the solutions developed by more in‐depth deterministic analyses.

Journal

Engineering Computations – Emerald Publishing

Published: Feb 24, 2012

Keywords: Soils; Deformation; Modelling; Substructures; Soil deformation moduli; Multi expression programming; Plate load test; Soil physical properties; Prediction

References

  • Compilers: Principles, Techniques, Tools
    Aho, A.; Sethi, R.; Ullman, J.
  • A robust data mining approach for formulation of geotechnical engineering systems
    Alavi, A.H.; Gandomi, A.H.
  • Formulation of flow number of asphalt mixes using a hybrid computational method
    Alavi, A.H.; Ameri, M.; Gandomi, A.H.; Mirzahosseini, M.R.
  • High‐precision modeling of uplift capacity of suction caissons using a hybrid computational method
    Alavi, A.H.; Gandomi, A.H.; Mousavi, M.; Mollahasani, A.
  • Multi expression programming: a new approach to formulation of soil classification
    Alavi, A.H.; Gandomi, A.H.; Sahab, M.G.; Gandomi, M.
  • Approximation model building using genetic programming methodology: applications
    Alvarez, L.F.; Toropov, V.V.; Hughes, D.C.; Ashour, A.F.
  • Empirical modelling of shear strength of RC deep beams by genetic programming
    Ashour, A.F.; Alvarez, L.F.; Toropov, V.V.
  • Standard Test Method for Bearing Capacity of Soil for Static Load. Spread Footings
    ASTM D1194‐94
  • Standard Test Method for Density, Unit Weight of Soil in Place by the Rubber Balloon Method
    ASTM D2167‐94
  • Integrated optimal design of structures under multiple loads for topology and shape using genetic algorithm
    Balamurugan, R.; Ramakrishnan, C.V.; Swaminathan, N.
  • Genetic Programming – An Introduction. On the Automatic Evolution of Computer Programs and Its Application
    Banzhaf, W.; Nordin, P.; Keller, R.; Francone, F.
  • Prediction of compressive and tensile strength of limestone via genetic programming
    Baykasoglu, A.; Gullub, H.; Canakcı, H.; Ozbakır, L.
  • Introduction to soil moduli
    Briaud, J.L.
  • BCD: a soil modulus device for compaction control
    Briaud, J.L.; Li, Y.; Rhee, K.
  • Canadian Foundation Engineering Manual
    Canadian Geotechnical Society
  • Modelling damping ratio and shear modulus of sand‐mica mixtures using genetic programming
    Cevik, A.; Cabalar, A.F.
  • The Data Analysis Handbook
    Frank, I.E.; Todeschini, R.
  • New formulation for compressive strength of CFRP confined concrete cylinders using linear genetic programming
    Gandomi, A.H.; Alavi, A.H.; Sahab, M.G.
  • Nonlinear genetic‐based models for prediction of flow number of asphalt mixtures
    Gandomi, A.H.; Alavi, A.H.; Mirzahosseini, R.; Moghadas Nezhad, F.
  • Beware of q 2
    Golbraikh, A.; Tropsha, A.
  • Lessons from oedometer tests on high quality samples
    Holtz, R.D.; Jamiolkowski, M.B.; Lancelotta, R.
  • Allowable pressure from loading tests on Kuwaiti soils
    Ismael, N.F.
  • 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.
  • Simultaneous topology and size optimization of structures by genetic algorithm using minimal length chromosome
    Kaveh, A.; Shahrouzi, M.
  • Modeling of the angle of shearing resistance of soils using soft computing systems
    Kayadelen, C.; Günaydın, O.; Fener, M.; Demir, A.; Özvan, A.
  • 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.
  • The general, congruent effects of structure in natural soils, weak rocks
    Leroueil, S.; Vaughan, P.R.
  • Subgrade reaction, load‐settlement characteristics of gravelly cobble deposits by plate load test
    Lin, P.S.; Yang, L.W.; Hsein Juang, C.
  • Handbook of Geotechnical Investigation, Design Tables
    Look, B.G.
  • Large in situ tests to measure the properties of stiff fissure clay
    Marsland, A.
  • Empirical modeling of plate load test moduli of soil via gene expression programming
    Mollahasani, A.; Alavi, A.H.; Gandomi, A.H.
  • Geotechnical Engineering. Principles, Practices of Soil Mechanics
    Murthy, S.
  • 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.
  • Solving classification problems using infix form genetic programming
    Oltean, M.; Grosşan, C.
  • Study of properties of loessial soils deposited in the Poltava region
    Platonov, E.V.; Yakovlev, A.V.; Zavalij, B.I.
  • A new genetic programming model for predicting settlement of shallow foundations
    Rezania, M.; Javadi, A.A.
  • Plate load tests of collapsible soils
    Reznik, Y.M.
  • Comparison of results of oedometer, plate load tests performed on collapsible soils
    Reznik, Y.M.
  • Influence of physical properties on deformation characteristics of collapsible soils
    Reznik, Y.M.
  • The undisturbed consolidation behavior of clay
    Schmertmann, J.H.
  • Recent advances and future challenges for artificial neural systems in geotechnical engineering applications
    Shahin, M.A.; Jaksa, M.B.; Maier, H.R.
  • The application of genetic algorithms to optimisation problems in geotechnics
    Simpson, A.R.; Priest, S.D.
  • Probability, Statistics in Civil Engineering
    Smith, G.N.
  • Applying Neural Networks a Practical Guide
    Swingler, K.
  • Design optimization of structural steelwork using a genetic algorithm FEM and a system of design rules
    Toropov, V.V.; Mahfouz, S.Y.
  • 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.
  • Optimising engineering problems using genetic algorithms
    Yeo, M.F.; Agyei, E.O.