Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Prediction of Maximum Dry Density and Unconfined Compressive Strength of Cement Stabilised Soil Using Artificial Intelligence Techniques
Abstract 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.
Prediction of Maximum Dry Density and Unconfined Compressive Strength of Cement Stabilised Soil Using Artificial Intelligence Techniques
Abstract 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.
Prediction of Maximum Dry Density and Unconfined Compressive Strength of Cement Stabilised Soil Using Artificial Intelligence Techniques
Suman, Shakti (Autor:in) / Mahamaya, Mahasakti (Autor:in) / Das, Sarat Kumar (Autor:in)
08.04.2016
11 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
| British Library Online Contents | 2011