Pressuremeter Modulus and Limit Pressure of Clayey Soils Using GMDH-Type Neural Network and Genetic Algorithms View Full Text


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Article Info

DATE

2018-02

AUTHORS

Reza Ziaie Moayed, Afshin Kordnaeij, Hossein Mola-Abasi

ABSTRACT

Pressuremeter modulus (EM) and limit pressure (PL) are used for the calculation of the settlement and bearing capacity of foundation respectively. As the determination of these parameters from pressuremeter test (PMT) is relatively time-consuming and expensive, various empirical correlations have been proposed to correlate the EM and PL to other soil parameters. For the existing equations are incapable of estimating these PMT parameters well, in present research group method of data handling type neural network is used to estimate the EM and PL of clayey soils. The EM and PL were modeled as a function of three variables including the moisture content (ω), plasticity index and corrected SPT blow counts (N60). A database containing 51 data sets have been used for training and testing of the models. The performances of proposed models are compared with those of existing empirical equations. The results demonstrate that appreciable improvement with respect to the other correlations has been achieved. At the end, sensitivity analysis of the obtained models has been performed to study the influence of input parameters on model outputs and shows that the N60 is the most influential parameter on the PMT parameters. More... »

PAGES

165-178

References to SciGraph publications

  • 2013-08. Artificial Neural Networks: A Solution to the Ambiguity in Prediction of Engineering Properties of Fine-Grained Soils in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • 2017-12. Compressibility indices of saturated clays by group method of data handling and genetic algorithms in NEURAL COMPUTING AND APPLICATIONS
  • 2011-09. Classification of slopes and prediction of factor of safety using differential evolution neural networks in ENVIRONMENTAL EARTH SCIENCES
  • 2014-04. Modeling and Interpretation of Pressuremeter Test Results with Artificial Neural Networks in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • 2011-05. Application of Artificial Intelligence to Maximum Dry Density and Unconfined Compressive Strength of Cement Stabilized Soil in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • 2010-11. Correlation of standard penetration test and pressuremeter data: a case study from Istanbul, Turkey in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2013-04. Shear Wave Velocity by Polynomial Neural Networks and Genetic Algorithms Based on Geotechnical Soil Properties in ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
  • 2014-04. Extreme Learning Machine Based Modeling of Resilient Modulus of Subgrade Soils in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • 2016-06. Assessment and Prediction of Liquefaction Potential Using Different Artificial Neural Network Models: A Case Study in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • 2016-04. Prediction of zeolite-cement-sand unconfined compressive strength using polynomial neural network in THE EUROPEAN PHYSICAL JOURNAL PLUS
  • 2012-08. Prediction of pressuremeter modulus and limit pressure of clayey soils by simple and non-linear multiple regression techniques: a case study from Mersin, Turkey in ENVIRONMENTAL EARTH SCIENCES
  • 2012-08. Planning Geotechnical Investigation Using ANFIS in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • 2016-04. An Optimized Artificial Neural Network Structure to Predict Clay Sensitivity in a High Landslide Prone Area Using Piezocone Penetration Test (CPTu) Data: A Case Study in Southwest of Sweden in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • 2008-08. Relationship between the standard penetration test and the pressuremeter test on sandy silty clays: a case study from Denizli in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2015-10. Prediction of Strength Parameters of Himalayan Rocks: A Statistical and ANFIS Approach in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
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