Generation and Evaluation of CPT Data Using Kriging Interpolation Technique

Chala, Ayele Tesema; Ray, Richard D.

doi:10.3311/ppci.21865

Cited by 1 publication

(1 citation statement)

References 16 publications

(27 reference statements)

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“…The CPTs were collected from an area measuring 50 by 50 m. Each CPT was performed to a depth of 5 m below the ground surface, and the measurement spacing of qc and fs was 5 mm. Further information about the specifics of the CPTs can be found in [47,49,50].…”

Section: Datasetsmentioning

confidence: 99%

Assessing the Performance of Machine Learning Algorithms for Soil Classification Using Cone Penetration Test Data

Chala

Ray

2023

Applied Sciences

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Conventional soil classification methods are expensive and demand extensive field and laboratory work. This research evaluates the efficiency of various machine learning (ML) algorithms in classifying soils based on Robertson’s soil behavioral types. This study employs 4 ML algorithms, including artificial neural network (ANN), random forest (RF), support vector machine (SVM), and decision trees (DT), to classify soils from 232 cone penetration test (CPT) datasets. The datasets were randomly split into training and testing datasets to train and test the ML models. Metrics such as overall accuracy, sensitivity, precision, F1_score, and confusion matrices provided quantitative evaluations of each model. Our analysis showed that all the ML models accurately classified most soils. The SVM model achieved the highest accuracy of 99.84%, while the ANN model achieved an overall accuracy of 98.82%. The RF and DT models achieved overall accuracy scores of 99.23% and 95.67%, respectively. Additionally, most of the evaluation metrics indicated high scores, demonstrating that the ML models performed well. The SVM and RF models exhibited outstanding performance on both majority and minority soil classes, while the ANN model achieved lower sensitivity and F1_score for minority soil class. Based on these results, we conclude that the SVM and RF algorithms can be integrated into software programs for rapid and accurate soil classification.

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Section: Datasetsmentioning

confidence: 99%