Abstract
The cone penetration test (CPT) is widely used in soil characterization and the determination of physical parameters. The traditional interpretation of the results of CPTs relies on the experience and expertise of geotechnical engineers. However, recent innovations in machine learning have led to the development of predictive models that can accurately predict soil properties based on CPT data. The application of these techniques can provide more accurate and consistent predictions, even for complex soil conditions. Therefore, this article sought to evaluate the performance of two different machine learning algorithms, random forest and deep learning, with CPT test data to predict tip resistance (qc) and sleeve resistance (fs) based on soil classification inputs. The work was conducted with a database of tests in the regions of Germany and Austria, initially consisting of more than two million related observations. This allowed for an assessment of model generalization across different regions. The random forest regressor algorithm presented a coefficient of determination of 0.94 for tip resistance (qc) and 0.82 for sleeve resistance (fs) prediction, thus outperforming deep neural networks. The study applied the model to obtain coefficients of determination between 0.65 and 0.68 for tip resistance (qc) and 0.14 to 0.75 for sleeve resistance (fs) for different regions of testing. Practical implications include the possibility of obtaining the design parameters qc and fs from inputs obtained from simpler tests, which would reduce project costs, improve the quality and efficiency of CPTs, and assist in making decisions about geotechnical projects.
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Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Also, the Python script are also available in the following Github repository: https://github.com/vlpacheco.
Abbreviations
- AI:
-
Artificial intelligence
- CNN:
-
Convolutional neural networks
- CPT:
-
Cone penetration test
- CPTu:
-
Cone penetration test with pore pressure measurement
- DNNs:
-
Deep neural network
- fs :
-
Sleeve resistance
- GPUs:
-
Graphics processing unit
- kPa:
-
Kilopascal
- MAE:
-
Mean absolute error
- MAPE:
-
Mean absolute percentage error
- ML:
-
Machine learning
- MPa:
-
Megapascal
- MSE:
-
Mean square error
- qc :
-
Tip resistance
- R2 :
-
Coefficient of determination
- RF:
-
Random forest
- RFE:
-
Recursive feature elimination method
- RFR:
-
Random forest regression
- RMSE:
-
Root mean square error
- SBT:
-
Soil behavior type
- SBTn:
-
Normalized soil behavior type
- SCPT:
-
Seismic cone penetration test
- SCPTu:
-
Seismic cone penetration test with pore pressure measurement
- SPT:
-
Standard penetration test
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This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001 and by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq–Grants #312756/2017-8 and #314643/2020-6).
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VLP, LB, FDR and AT designed the content and logic of this experimental feature and algorithms. VLP and LB finished the first-hand manuscript, also FDR and AT revised this manuscript. All authors read and approved the final manuscript.
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Pacheco, V.L., Bragagnolo, L., Dalla Rosa, F. et al. Cone Penetration Test Prediction Based on Random Forest Models and Deep Neural Networks. Geotech Geol Eng 41, 4595–4628 (2023). https://doi.org/10.1007/s10706-023-02535-0
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DOI: https://doi.org/10.1007/s10706-023-02535-0