Prediction of the residual strength of clay using functional networks
Autor: | S. Z. Khan, M. Pavani, Sarat Kumar Das, Shakti Suman |
---|---|
Jazyk: | angličtina |
Rok vydání: | 2016 |
Předmět: |
Mean squared error
Correlation coefficient 0211 other engineering and technologies Earth and Planetary Sciences(all) 02 engineering and technology 010502 geochemistry & geophysics Residual 01 natural sciences Stability (probability) Shear strength (soil) Prediction model Statistics Geotechnical engineering Index properties 021101 geological & geomatics engineering 0105 earth and related environmental sciences Mathematics lcsh:QE1-996.5 Statistical parameter Residual strength lcsh:Geology Functional networks General Earth and Planetary Sciences Nash–Sutcliffe model efficiency coefficient Landslides |
Zdroj: | Geoscience Frontiers, Vol 7, Iss 1, Pp 67-74 (2016) |
ISSN: | 1674-9871 |
Popis: | Landslides are common natural hazards occurring in most parts of the world and have considerable adverse economic effects. Residual shear strength of clay is one of the most important factors in the determination of stability of slopes or landslides. This effect is more pronounced in sensitive clays which show large changes in shear strength from peak to residual states. This study analyses the prediction of the residual strength of clay based on a new prediction model, functional networks (FN) using data available in the literature. The performance of FN was compared with support vector machine (SVM) and artificial neural network (ANN) based on statistical parameters like correlation coefficient ( R ), Nash--Sutcliff coefficient of efficiency ( E ), absolute average error (AAE), maximum average error (MAE) and root mean square error (RMSE). Based on R and E parameters, FN is found to be a better prediction tool than ANN for the given data. However, the R and E values for FN are less than SVM. A prediction equation is presented that can be used by practicing geotechnical engineers. A sensitivity analysis is carried out to ascertain the importance of various inputs in the prediction of the output. |
Databáze: | OpenAIRE |
Externí odkaz: |