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Title of Journal: Neural Comput Applic

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Abbravation: Neural Computing and Applications

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Springer London

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DOI

10.1016/0014-4827(63)90167-8

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ISSN

1433-3058

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Compressibility indices of saturated clays by grou

Authors: Reza Ziaie Moayed Afshin Kordnaeij Hossein MolaAbasi
Publish Date: 2016/06/02
Volume: 28, Issue: 1, Pages: 551-564
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Abstract

Compression index C c and recompression index C r are used to estimate the consolidation settlement of finegrained soils As the determination of these indices from oedometer test is relatively timeconsuming in present research group method of data handlingtype neural network optimized using genetic algorithms is used to estimate the compressibility indices C c and C r of saturated clays C c and C r were modeled as a function of three variables including the initial void ratio e 0 liquid limit LL and specific gravity G s Three hundred data sets collected from multiple sites in the province of Mazandaran Iran were used for the training and testing of the models The predicted compressibility indices were compared with those of experimentally measured values to evaluate the performances of the proposed models The results showed that appreciable improvement toward other correlations has been achieved At the end sensitivity analyses of the obtained models were carried out to evaluate the influence of input parameters on model outputs and showed that e 0 and LL are the most influential parameters on C c and C r respectively Also it has been demonstrated that the compressibility indices predicted by models are considerably influenced by changing measured G s uncertainty In other words the mean absolute percent error values increase greatly by G s variation Therefore it needs more accuracy to measure this parameter in the laboratory

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