Predicting standard penetration test N-value from cone penetration test data using artificial neural networks
By B. Tarawneh. 2017
61 citations recorded by the source corpus.
Abstract Standard Penetration Test (SPT) and Cone Penetration Test (CPT) are the most frequently used field tests to estimate soil parameters for geotechnical analysis and design. Numerous soil parameters are related to the SPT N -value. In contrast, CPT is becoming more popular for site investigation and geotechnical design. Correlation 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 N 60 -value from CPT data. Data used in this study consisted of 109 CPT-SPT pairs for sand, sandy silt, and silty sand soils. The ANN model input variables are: CPT tip resistance ( q c ), effective vertical stress ( σ v ′ ) , and CPT sleeve friction ( f s ). A different set of SPT-CPT data was used to check the reliability of the developed ANN model. It was shown that ANN model either under-predicted the N 60 -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 N 60 -value from CPT data with acceptable accuracy.
Subfields: Civil and Structural Engineering, Safety, Risk, Reliability and Quality
View Paper: https://www.semanticscholar.org/paper/bbc26372437fd0e4f04105a46196377627a8ca6c
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In Civil and Structural Engineering
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Citations in the corpus
- Machine learning-based prediction of soil compression modulus with application of 1D settlement (2020, methodology): “Over the last decade, ML has been applied successfully to prediction problem in geotechnical engineering (Arditi and Pulket, 2010; Viswanathan and Samui, 2016; Tarawneh, 2017).”