The soft soil that has not enough in situ bearing capacity needs proper stabilization before any construction can be done on this soil. Cement stabilization has been found to be an effective method to improve the soil properties by many researchers. The strength development in a cement stabilized mix depends on a number of factors such as the soil properties, the water-cement ratio and the percentage of cement in the mix. In the present study an attempt is made to develop prediction model to determine the maximum dry density (MDD) and the unconfined compressive strength (UCS) of cement stabilized soil with the use of two recently developed artificial intelligence (AI) techniques; functional networks (FN) and multivariate adaptive regression splines (MARS). Database previously available in the literature was used to develop the prediction models. Based on different statistical performance criteria, it was found that the FN and MARS techniques, are better at prediction of MDD and UCS as compared to previously used AI techniques, artificial neural network and support vector machine. The prediction model presented here is more comprehensive and can be used by professional engineers.
a b s t r a c tLandslides 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), NashSutcliff 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. Ó 2015, China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
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