Application of Artificial Neural Networks ( ANNs ) in prediction and Interpretation of Pressuremeter Test Results

The successful application of in-situ testing of soils heavily depends on development of methods of interpretation of the tests. With recent developments in equipment and theoretical methods of interpretation, in situ testing has become a standard requirement of most site investigations. The Pressuremeter test is the one of most important in-situ tests of soil. Pressuremeters are devices for carrying out in-situ testing of soils and rocks for strength and stiffness parameters. They are generally cylindrical, long with respect to their diameter, part of this length being covered by a flexible membrane. One of the considerable notes in this test is that Pressuremeter test can measure simultaneously deformation and strength parameters of soil. In this paper the pre-boring pressuremeter (PBP) was used and data for developing the ANN models included: results of pressuremeter tests at Tabriz, Northwest of Iran. In this paper, MLP (multi layer perceptron) artificial neural networks (ANNs) have been used. The single model output is the menard modulus Em. The modulus Em is frequently used to estimste the displacement of geotechnical structures: for vertically and/or horizontally loaded foundations, flexible earth retaining structures, and even as a first assessment for embankment lying on compressible soil.

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