Modeling sulfuric acid induced swell in carbonate clays using artificial neural networks
| Autor: | P. V. Sivapullaiah, B. Guru Prasad, M. M. Allam |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Artificial neural network
Soil science Sulfuric acid Atterberg limits Geotechnical Engineering and Engineering Geology Oedometer test Swell Cross-validation chemistry.chemical_compound chemistry Environmental science Feedforward neural network Carbonate Geotechnical engineering Civil and Structural Engineering |
| Zdroj: | Geomechanics and Engineering. 1:307-321 |
| ISSN: | 2005-307X |
| DOI: | 10.12989/gae.2009.1.4.307 |
| Popis: | The paper employs a feed forward neural network with back-propagation algorithm for modeling time dependent swell in clays containing carbonate in the presence of sulfuric acid. The oedometer swell percent is estimated at a nominal surcharge pressure of 6.25 kPa to develop 612 data sets for modeling. The input parameters used in the network include time, sulfuric acid concentration, carbonate percentage, and liquid limit. Among the total data sets, 280 (46%) were assigned to training, 175 (29%) for testing and the remaining 157 data sets (25%) were relegated to cross validation. The network was programmed to process this information and predict the percent swell at any time, knowing the variable involved. The study demonstrates that it is possible to develop a general BPNN model that can predict time dependent swell with relatively high accuracy with observed data ( |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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