Volume 14, Issue 5 (2014)                   MCEJ 2014, 14(5): 11-25 | Back to browse issues page

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Emami M, Yasrebi S. Application of artificial neural networks in interpretation of pressuremeter test results. MCEJ 2014; 14 (5) :11-25
URL: http://mcej.modares.ac.ir/article-16-5107-en.html
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Abstract:   (10530 Views)
In-situ tests play important role in any geotechnical investigation. Pressuremeter test can be considered one of the most important in situ tests.This test can be considered one of the most important in situ tests in Geotechnical Engineering. This test is capable to properly estimate deflection parameters of soil. Three types of pressuremeters exist based on their placement in the boreholes: Predrilled pressuremeters (P.D.P), Self-boring pressuremeters (S.B.P) and Push-in pressuremeters (P.I.P). The Predrilled pressuremeters (P.D.P) have been used in this project. Based on expansion of a cylinder that is placed inside the borehole the pressure-volume variation during testing is recorded. In this research, the results of approximately 500 conducted Pressuremeter tests on the soils by Pajohesh Omran Rahvar Ltd (2006-2007) are employed. The number of tests decreased to 400 due to lack of accuracy and also high changes in the range of Pressuremeter modules. The tests have been carried out on the soils of Northwest Iran (Tabriz), South Iran (Kharg Island) and Northeast Iran (Mashhad). The Pressuremeter instrument used is menard pre-boring. Conducted tests in accordance with ASTM-D4719 represented acceptable accuracy.  In the current paper, three types of Artificial Neural Network (ANN) are employed in interpretation of pressuremeter test results. First, multi layer perceptron neural network, one of the most applicable neural networks, is used. Then, neuro-fuzzy network, combination of neural-phase network is employed and finally radial basis function, a successful network in solving nonlinear problems, is applied. Neural network models showed prosperity to interpret Pressuremeter test. Soil physical and compaction properties are used in all these models. The applied models own 5 input parameters and 1 output parameters. Hidden layers with different number of neurons are tested in both one and two layers networks so as to select the most proper network architecture. It has been shown that a three-layer perceptron with differential transfer functions and sufficient number of neurons in hidden layer can approximate any nonlinear relationship. Consequently, one hidden layer is used in the present study. The neural network toolbox of MATLAB7.4, a popular numerical computation and visualization software, is used for training and testing of the MLPs. Transfer functions of networks are selected by trial and error.  A large complex of carried out tests on the extensive range of fine and course grained soils is used as database. In order to determine the most exact network in the perceptron neural network, some networks with different architecture are employed. Of all neural network models, multi-layer perceptron neural network proved to be the most effective. However, other applied networks have shown favorable performance. Finally, different models have been compared and network with the most outstanding performance is determined. In order to evaluate the capability of model generalization, the performance of mentioned network against inexperienced data has been compared with empirical results. Contrary to conventional behavioral models, models based neural network do not demonstrate the effect of input parameters on output parameters. This research is response to this need through conducting sensitivity analysis on the optimal structure of proposed models. Also, derivation of governing equation for neural network model give more assurance to user to employ such models and consequently this facilitates the application of models in the engineering practices.
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Received: 2011/05/9 | Accepted: 2014/11/22 | Published: 2015/01/31

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