Prediction of Soil Composition from CPT Data Using General Regression Neural Network

• Published in: Journal of computing in civil engineering Vol. 20; no. 4; pp. 281 - 289
• Main Authors: Kurup, Pradeep U, Griffin, Erin P
• Format: Journal Article
• Language: English
• Published: Reston, VA American Society of Civil Engineers 01.07.2006
• Subjects: Applied sciences; Buildings. Public works; Computation methods. Tables. Charts; Exact sciences and technology; Geotechnics; Soil investigations. Testing; Structural analysis. Stresses; TECHNICAL PAPERS; CE Database subject headings: Neural networks; Cone penetration tests; Data analysis; Penetration test; Geotechnical engineering; Regression analysis; Compaction; Neural network; Forecast model; Soil classification; Recommendation; Soil test; Soil compaction; Classification; Predictions; Artificial intelligence; Geotechnics
• ISSN: 0887-3801; 1943-5487
• DOI: 10.1061/(ASCE)0887-3801(2006)20:4(281)

Soil type is typically inferred from the information collected during a cone penetration test (CPT) using one of the many available soil classification methods. In this study, a general regression neural network (GRNN) was developed for predicting soil composition from CPT data. Measured values of cone resistance and sleeve friction obtained from CPT soundings, together with grain-size distribution results of soil samples retrieved from adjacent standard penetration test boreholes, were used to train and test the network. The trained GRNN model was tested by presenting it with new, previously unseen CPT data, and the model predictions were compared with the reference particle-size distribution and the results of two existing CPT soil classification methods. The profiles of soil composition estimated by the GRNN generally compare very well with the actual grain-size distribution profiles, and overall the neural network had an 86% success rate at classifying soils as coarse grained or fine grained.