| Title: | New computational models for better predictions of the soil-compression index |
|---|
| Authors: | ID Demir, Ahmet (Author)
ID Fakulteta za gradbeništvo, prometno inženirstvo in arhitekturo Univerze v Mariboru (Copyright holder) |
|---|
| Files: |  Acta_geotechnica_Slovenica_2015_Demir_New_computational_models_for_better_predictions_of_the_soil-compression_index.pdf (274,65 KB)
MD5: 534ACE34C47905C41DDA0A284A76910F
PID: 20.500.12556/dkum/bee92470-49a3-4e46-a841-171e8b5372f2
 http://fgserver3.fg.um.si/journal-ags/2015-1/article-6.asp
|
|---|
| Language: | English |
|---|
| Work type: | Scientific work |
|---|
| Typology: | 1.01 - Original Scientific Article |
|---|
| Organization: | FGPA - Faculty of Civil Engineering, Transportation Engineering and Architecture
|
|---|
| Abstract: | The compression index is one of the important soil parameters that are essential for geotechnical designs. Because laboratory and in-situ tests for determining the compression index (Cc) value are laborious, time consuming and costly, empirical formulas based on soil parameters are commonly used. Over the years a number of empirical formulas have been proposed to relate the compressibility to other soil parameters, such as the natural water content, the liquid limit, the plasticity index, the specific gravity. These empirical formulas provide good results for a specific test set, but cannot accurately or reliably predict the compression index from various test sets. The other disadvantage is that they tend to use a single parameter to estimate the compression index (Cc), even though Cc exhibits spatial characteristics depending on several soil parameters. This study presents the potential for Genetic Expression Programming (GEP) and the Adaptive Neuro-Fuzzy (ANFIS) computing paradigm to predict the compression index from soil parameters such as the natural water content, the liquid limit, the plastic index, the specific gravity and the void ratio. A total of 299 data sets collected from the literature were used to develop the models. The performance of the models was comprehensively evaluated using several statistical verification tools. The predicted results showed that the GEP and ANFIS models provided fairly promising approaches to the prediction of the compression index of soils and could provide a better performance than the empirical formulas. |
|---|
| Keywords: | compression index, statistical analysis, genetic expression programming, adaptive neuro-fuzzy, empirical equations |
|---|
| Publication status: | Published |
|---|
| Publication version: | Version of Record |
|---|
| Year of publishing: | 2015 |
|---|
| Number of pages: | str. 59-69 |
|---|
| Numbering: | Letn. 12, št. 1 |
|---|
| PID: | 20.500.12556/DKUM-70832  |
|---|
| ISSN: | 1854-0171 |
|---|
| UDC: | 624.13 |
|---|
| ISSN on article: | 1854-0171 |
|---|
| COBISS.SI-ID: | 284359168 |
|---|
| NUK URN: | URN:SI:UM:DK:EOAM828I |
|---|
| Copyright: | Fakulteta za gradbeništvo, prometno inženirstvo in arhitekturo Univerze v Mariboru |
|---|
| Publication date in DKUM: | 14.06.2018 |
|---|
| Views: | 1499 |
|---|
| Downloads: | 91 |
|---|
| Metadata: |    |
|---|
| Categories: | Misc.
|
|---|
|
:
|
Copy citation |
|---|
| | | | Average score: | (0 votes) |
|---|
| Your score: | Voting is allowed only for logged in users. |
|---|
| Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
