Automatic classification of fine-grained soils using CPT measurements and Artificial Neural Networks

Automatic classification of fine-grained soils using CPT measurements and Artificial Neural Networks

Reale, C. (TU Delft Geo-engineering)
Gavin, Kenneth (TU Delft Geo-engineering)
Librić, Lovorka (University of Zagreb)
Jurić-Kaćunić, Danijela (University of Zagreb)

2018-04-01

Soil classification is a means of grouping soils into categories according to a shared set of properties or characteristics that will exhibit similar engineering behaviour under loading. Correctly classifying site conditions is an important, costly, and time-consuming process which needs to be carried out at every building site prior to the commencement of construction or the design of foundation systems. This paper presents a means of automating classification for fine-grained soils, using a feed-forward ANN (Artificial Neural Networks) and CPT (Cone Penetration Test) measurements. Thus representing a significant saving of both time and money streamlining the construction process. 216 pairs of laboratory results and CPT tests were gathered from five locations across Northern Croatia and were used to train, test, and validate the ANN models. The resultant Neural Networks were saved and were subjected to a further external verification using CPT data from the Veliki vrh landslide. A test site, which the model had not previously been exposed to. The neural network approach proved extremely adept at predicting both ESCS (European Soil Classification System) and USCS (Unified Soil Classification System) soil classifications, correctly classifying almost 90% of soils. While the soils that were incorrectly classified were only partially misclassified. The model was compared to a previously published model, which was compiled using accepted industry standard soil parameter correlations and was shown to be a substantial improvement, in terms of correlation coefficient, absolute average error, and the accuracy of soil classification according to both USCS and ESCS guidelines. The study confirms the functional link between CPT results, the percentage of fine particles FC, the liquid limit w_L and the plasticity index I_P. As the training database grows in size, the approach should make soil classification cheaper, faster and less labour intensive.

ANN
CPT
Machine learning
Neural networks
Soil classification

http://resolver.tudelft.nl/uuid:6d5a7884-a62e-4502-8148-6710d1663a5e

https://doi.org/10.1016/j.aei.2018.04.003

2018-10-14

1474-0346

Advanced Engineering Informatics: the science of supporting knowledge-intensive activities, 36, 207-215

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2018 C. Reale, Kenneth Gavin, Lovorka Librić, Danijela Jurić-Kaćunić