An Approach for USCS Identical Classification of Soil using Artificial Neural Network Technique

摘要

Percentage of particle sizes, liquid limit, plastic limit and plasticity index are some of the basicrninformation of soil needed to classify it in Unified Soil Classification System (USCS). Though aforementionedrnproperties are sufficient for engineering classification of soil but we may include other properties also which may causernbehavioral alteration in a specific soil. Present study is an approach of applying Artificial Neural Network (ANN)rnmodeling technique by embracing some physical properties indicator of in-situ state of soil and some index propertiesrnof soil to predict specific class. Though it is site specific phenomenon but efforts were made to include both classes thatrnare coarse and find grained soils in the study. The results obtained from ANN models found satisfactory in agreementrnwith the Unified Soil Classification System and may be used to predict specific soil class. Further it is suggested thatrnincorporating other soft computing tools in connection with ANN may prove far better.