Machine Learning Techniques for Soil Characterization Using Cone Penetration Test Data

Abstract

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.

A Review of Machine Learning Techniques in Agroclimatic Studies

“…Agricultural Application [18,26] Decision Tree Crop Yield Prediction, Disease Detection, Soil Assessment [18][19][20] Random Forest Crop Yield Prediction, Disease Detection, Soil Assessment [18,27] Extreme Gradient Boosting Crop Yield Prediction, Soil Assessment [18,20] Naive Bayes Crop Yield Prediction, Disease Detection [18,21] K-Nearest Neighbors Crop Yield Prediction, Disease Detection [28] Ensemble Traditional ML Models Crop Yield Prediction [26] Multi-Linear Regressor Crop Yield Prediction [29] RNN Crop Yield Prediction [29] LSTM Crop Yield Prediction [29] Support Vector Regression Crop Yield Prediction [23,24,30,31] CNN Crop Yield Prediction, Disease Detection [30] GNN Crop Yield Prediction [30] U-Net Crop Yield Prediction [23,25,32] ANN Crop Yield Prediction, Disease Detection [25] DBSCAN Crop Yield Prediction [23,25] Support Vector Machine Crop Yield Prediction, Disease Detection, Smart Farming [33] Vision Transformers Disease Detection [22] VGG-RNN Hybrid Soil Assessment [23,24] MLP Soil Assessment…”

“…Figure 5 outlines the methodologies employed in the analyzed studies, focusing on feature engineering, model selection, hyperparameter optimization, and validation strategies. Of the 66 papers examined, merely 12 provided comprehensive discussions on these methodologies, supported by examples and citations [27,67,75]. Notably, 39% (26 studies) omitted documentation on feature selection, and a significant 61% (40 studies) acknowledged the features used without detailing the selection process.…”

Developing Nomographs for the Unit Weight of Soils

“…These modern tools offer more precise solutions and additional insights into soil behavior. Recent studies have provided valuable insights into the application of machine learning in geotechnical engineering [26][27][28][29][30]. These contributions contribute to a deeper awareness of incorporating machine learning techniques in geotechnical engineering.…”

Examination of Determinants and Predictive Modeling of Artificially Frozen Soil Strength Utilizing the XGBoost Algorithm

“…The extreme gradient boosting (XGB) algorithm [25], based on gradient boosting trees, is renowned for its high predictive accuracy and strong generalization capabilities. It has been widely applied in various domains, including land subsidence [26,27], soil strength [28,29], and slope stability [30,31]. This study, by collecting a substantial amount of existing research data, employed the XGB algorithm to establish a predictive model for the unconfined compressive strength of frozen soil.…”