Modeling the complex and long term swelling behavior of argillaceous rocks

• 作 者: Doostmohammadi R. Mutschler Th. Osan C.
• 机 构: Mining Engineering Department, Zanjan University, Zanjan 4537138111, Iran,Institute of Soil Mechanic
• 出 处: Mining Science and Technology
• 发表日期: 2011年05期
• 关键词: Argillaceous rock Artificial neural network Long term swelling potential Short t

The swelling behavior of argillaceous rocks is a complex phenomenon and has been determined using a lot of indexes in the literature. Determining the required modeling indexes that need to be performed requires expensive tests and extensive time in different laboratories. In some of the cases, it is too diffi- cult to find a relation between the effective variables and swelling potential. This paper suggests a method for modeling the time dependent swelling pressure of argillaceous rocks. The trend of short term swelling potential during the first 3 days of the swelling pressure testing is used for modeling the long term swelling pressure of mudstone that is recorded during months. The artificial neural network (ANN) as a power tool is used for modeling this nonlinear and complex behavior. This method enables predicting the swelling potential of argillaceous rocks when the required indexes and also correlation between them is unattainable. This method facilitates the model of all studied samples under a unique formulation.

The swelling behavior of argillaceous rocks is a complex phenomenon and has been determined using a lot of indexes in the literature. Determining the required modeling indexes that need to be performed requires expensive tests and extensive time in different laboratories. In some of the cases, it is too diffi- cult to find a relation between the effective variables and swelling potential. This paper suggests a method for modeling the time dependent swelling pressure of argillaceous rocks. The trend of short term swelling potential during the first 3 days of the swelling pressure testing is used for modeling the long term swelling pressure of mudstone that is recorded during months. The artificial neural network (ANN) as a power tool is used for modeling this nonlinear and complex behavior. This method enables predicting the swelling potential of argillaceous rocks when the required indexes and also correlation between them is unattainable. This method facilitates the model of all studi ed samples under a unique formulation.