A multiple model machine learning approach for soil classification from cone penetration test data

Carvalho, Lucas Orbolato; Ribeiro, Dimas Betioli

doi:10.28927/sr.2021.072121

Cited by 8 publications

(3 citation statements)

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“…These two models achieved almost perfect scores for all performance metrics for all soil types, indicating their high accuracy in the classification task. Overall, the performance of the ML models in classifying soils based on the CPT dataset is consistent with previous similar research carried out on ML techniques (e.g., see [12,21]).…”

Section: Comparison Of ML Models' Performancesupporting

confidence: 88%

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: Comparison Of ML Models' Performancesupporting

confidence: 88%

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

Chala

Ray

2023

Applied Sciences

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“…Many ML algorithms, such as gradient boosting, random forest, support vector machine (SVM) artificial neural network (ANN), and decision trees (DT), have been used in various geotechnical applications, including soil classification [27][28][29][30][31][32][33], V s prediction [23][24][25][26]34], liquefaction analysis [35][36][37][38][39][40], stability analysis [41][42][43][44][45], and settlement prediction [46][47][48]. The application of ML algorithms in geotechnical engineering has shown promising results in terms of efficiency and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques for Soil Characterization Using Cone Penetration Test Data

Chala

Ray

2023

Applied Sciences

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Seismic response assessment requires reliable information about subsurface conditions, including soil shear wave velocity (Vs). To properly assess seismic response, engineers need accurate information about Vs, an essential parameter for evaluating the propagation of seismic waves. However, measuring Vs is generally challenging due to the complex and time-consuming nature of field and laboratory tests. This study aims to predict Vs using machine learning (ML) algorithms from cone penetration test (CPT) data. The study utilized four ML algorithms, namely Random Forests (RFs), Support Vector Machine (SVM), Decision Trees (DT), and eXtreme Gradient Boosting (XGBoost), to predict Vs. These ML models were trained on 70% of the datasets, while their efficiency and generalization ability were assessed on the remaining 30%. The hyperparameters for each ML model were fine-tuned through Bayesian optimization with k-fold cross-validation techniques. The performance of each ML model was evaluated using eight different metrics, including root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), coefficient of determination (R2), performance index (PI), scatter index (SI), A10−I, and U95. The results demonstrated that the RF model consistently performed well across all metrics. It achieved high accuracy and the lowest level of errors, indicating superior accuracy and precision in predicting Vs. The SVM and XGBoost models also exhibited strong performance, with slightly higher error metrics compared with the RF model. However, the DT model performed poorly, with higher error rates and uncertainty in predicting Vs. Based on these results, we can conclude that the RF model is highly effective at accurately predicting Vs using CPT data with minimal input features.

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“…With the recent development of data acquisition and processing methods, data mining techniques such as decision trees, support vector machines, clustering, and artificial neural networks have Sustainability 2023, 15, 2914 2 of 11 been proposed as alternatives to traditional soil classification methods. Additionally, stratification is carried out via semi-supervised clustering, which combines supervised learning, which makes predictions based on patterns in the data, and unsupervised learning, which unearths the hidden patterns in the data [4][5][6][7][8][9][10][11][12][13][14][15][16]. If the decision tree approach is applied to data stratification, the analysis results can provide a soil classification scheme that considers the local engineering characteristics of the in situ soils.…”

Section: Introduction 1data Mining In Geotechnical Engineeringmentioning

confidence: 99%

Locally Specified CPT Soil Classification Based on Machine Learning Techniques

Cho

Kim

2023

Sustainability

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Cone penetration tests (CPTs) can provide highly accurate and detailed information and characteristics relevant to the stiffness, strength, and consolidation of tested geomaterials, but they do not directly recover real soil samples. Thus, when CPT results are applied to soil classification, experience-based classification charts or tables are generally used. However, such charts or tables have the inherent drawback of being derived from the test data applied to each classification method, which promotes their failure to cover the engineering features of soils from other places. This study proposes a machine learning approach using C4.5 decision tree algorithm to develop a locally specified CPT-based soil classification system. The findings demonstrate that a locally specified soil classification scheme can be attained by utilizing a simple and trained decision tree model with appropriate combinations of training data and input attributes. Additionally, it is confirmed that oversampling the minor classes makes the classification accuracy for data with highly unbalanced classes appear more balanced for each class.

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A multiple model machine learning approach for soil classification from cone penetration test data

Cited by 8 publications

References 0 publications

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

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

Machine Learning Techniques for Soil Characterization Using Cone Penetration Test Data

Locally Specified CPT Soil Classification Based on Machine Learning Techniques

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