Predicting standard penetration test N-value from cone penetration test data using artificial neural networks

作     者：Bashar Tarawneh 

作者机构：Department of Civil EngineeringThe University of JordanAmman11942Jordan 

出 版 物：《Geoscience Frontiers》 (地学前缘（英文版）)

年 卷 期：2017年第8卷第1期

页      面：199-204页

核心收录：

学科分类：081401[工学-岩土工程] 08[工学] 0814[工学-土木工程] 

基　　金：DOE Public Access Plan United States Government U.S. Department of Energy UT-Battelle(DE-AC05-00OR22725) 

主　　题：SPT CPT Correlation Artificial neural networ Sand Silt 

摘      要：Standard Penetration Test（SPT） and Cone Penetration Test（CPT） are the most frequently used field tests to estimate soil parameters for geotechnical analysis and *** soil parameters are related to the SPT *** contrast,CPT is becoming more popular for site investigation and geotechnical *** of CPT data with SPT N-value is very beneficial since most of the field parameters are related to SPT N-values.A back-propagation artificial neural network（ANN） model was developed to predict the N6o-value from CPT *** used in this study consisted of 109 CPT-SPT pairs for sand,sandy silt,and silty sand *** ANN model input variables are：CPT tip resistance（qc）,effective vertical stress（σ’v）,and CPT sleeve friction（fs）.A different set of SPT-CPT data was used to check the reliability of the developed ANN *** was shown that ANN model either under-predicted the N60-value by 7-16%or over-predicted it by 7-20%.It is concluded that back-propagation neural networks is a good tool to predict N60-value from CPT data with acceptable accuracy.