List of Articles Manouchehr Hoseine

• Open Access Article
  • Abstract Page
  • Full-Text
  
  1 - Comparison of Artificial Neural Network and Regression Methods in Predicting the Modulus of Deformation of Stone using Dilatometry Test.
  Manouchehr Hoseine Rouzbeh Dabiri Larissa Khodadadi
  In geotechnical engineering, the modulus of deformation (Em) is actually the ratio of stress to strain. The application of this module is in the fields of dam construction, tunnel construction, road construction, etc. Today, there are various methods to obtain the defor More

  In geotechnical engineering, the modulus of deformation (Em) is actually the ratio of stress to strain. The application of this module is in the fields of dam construction, tunnel construction, road construction, etc. Today, there are various methods to obtain the deformation modulus, among which we can refer to in-situ tests (loading plate-dilatometry), laboratory tests, and practical relationships. Also, there are different methods to predict and determine the relationships between several different parameters, which can be referred to regression analysis and artificial neural network. The main goal of the present research is to provide a new relationship to predict the modulus of deformation of rocks before performing the dilatometry test with the least error. The results of the studies have shown that neural network modeling is more efficient than regression analysis in all input independent variables, and it has a higher level of confidence only with the input of Q parameter to the regression analysis equation. Also, by comparing these two methods, it was found that the more the number of input variables, the better the neural network works. Manuscript profile