Comparison of Artificial Neural Network and Regression Methods in Predicting the Modulus of Deformation of Stone using Dilatometry Test.

Subject Areas : محاسبات نرم در علوم مهندسی

Manouchehr  Hoseine ¹ (M.Sc. of Geotechnical Engieering, Department of Civil Engineering, Maragheh Branch, Islamic Azad University, Maragheh, Iran)
Rouzbeh  Dabiri ² (Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran)
Larissa  Khodadadi ³ (Assistant Professor, Department of Electrical Engineering, Tabriz Branch, Islamic Azad university, Tabriz , Iran)

Received: 2024-03-14 Accepted : 2024-06-10 Published : 2024-06-26

Keywords: Deformation modulus, Dilatometry, Rock, Neural network, Regression Analysis,

Abstract :

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.

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