Compressibility indices of saturated clays by group method of data handling and genetic algorithms View Full Text


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Article Info

DATE

2017-12

AUTHORS

Reza Ziaie Moayed, Afshin Kordnaeij, Hossein Mola-Abasi

ABSTRACT

Compression index (Cc) and recompression index (Cr) are used to estimate the consolidation settlement of fine-grained soils. As the determination of these indices from oedometer test is relatively time-consuming, in present research group method of data handling-type neural network optimized using genetic algorithms is used to estimate the compressibility indices (Cc and Cr) of saturated clays. Cc and Cr were modeled as a function of three variables including the initial void ratio (e0), liquid limit (LL) and specific gravity (Gs). Three hundred data sets collected from multiple sites in the province of Mazandaran, Iran, were used for the training and testing of the models. The predicted compressibility indices were compared with those of experimentally measured values to evaluate the performances of the proposed models. The results showed that appreciable improvement toward other correlations has been achieved. At the end, sensitivity analyses of the obtained models were carried out to evaluate the influence of input parameters on model outputs and showed that e0 and LL are the most influential parameters on Cc and Cr, respectively. Also, it has been demonstrated that the compressibility indices predicted by models are considerably influenced by changing measured Gs (uncertainty). In other words, the mean absolute percent error values increase greatly by Gs variation. Therefore, it needs more accuracy to measure this parameter in the laboratory. More... »

PAGES

551-564

References to SciGraph publications

  • 2017-06. Application of adaptive neuro-fuzzy technique and regression models to predict the compressive strength of geopolymer composites in NEURAL COMPUTING AND APPLICATIONS
  • 2016-07. Use of neural networks for the prediction of the CBR value of some Aegean sands in NEURAL COMPUTING AND APPLICATIONS
  • 2006-11. Regression analysis of compression index for Kwangyang marine clay in KSCE JOURNAL OF CIVIL ENGINEERING
  • 2014-02. Prediction of swelling pressures of expansive soils using soft computing methods in NEURAL COMPUTING AND APPLICATIONS
  • 2014-03. The use of neural networks for the prediction of the settlement of one-way footings on cohesionless soils based on standard penetration test in NEURAL COMPUTING AND APPLICATIONS
  • 2013-04. Shear Wave Velocity by Polynomial Neural Networks and Genetic Algorithms Based on Geotechnical Soil Properties in ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
  • 2013-08. Estimation of pressuremeter modulus and limit pressure of clayey soils by various artificial neural network models in NEURAL COMPUTING AND APPLICATIONS
  • 2013-03. Correlation between Compression Index of Silt–Clay Matrices and their Index Properties in JOURNAL OF THE INSTITUTION OF ENGINEERS (INDIA): SERIES A
  • 2008-11. Statistical and neural network assessment of the compression index of clay-bearing soils in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2012-07. Neural computing models for prediction of permeability coefficient of coarse-grained soils in NEURAL COMPUTING AND APPLICATIONS
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    44 schema:description Compression index (Cc) and recompression index (Cr) are used to estimate the consolidation settlement of fine-grained soils. As the determination of these indices from oedometer test is relatively time-consuming, in present research group method of data handling-type neural network optimized using genetic algorithms is used to estimate the compressibility indices (Cc and Cr) of saturated clays. Cc and Cr were modeled as a function of three variables including the initial void ratio (e0), liquid limit (LL) and specific gravity (Gs). Three hundred data sets collected from multiple sites in the province of Mazandaran, Iran, were used for the training and testing of the models. The predicted compressibility indices were compared with those of experimentally measured values to evaluate the performances of the proposed models. The results showed that appreciable improvement toward other correlations has been achieved. At the end, sensitivity analyses of the obtained models were carried out to evaluate the influence of input parameters on model outputs and showed that e0 and LL are the most influential parameters on Cc and Cr, respectively. Also, it has been demonstrated that the compressibility indices predicted by models are considerably influenced by changing measured Gs (uncertainty). In other words, the mean absolute percent error values increase greatly by Gs variation. Therefore, it needs more accuracy to measure this parameter in the laboratory.
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