Assessment of Machine Learning Models in Liquefaction Prediction with Emphasis on ROC Curve and AUC Index Differences
محورهای موضوعی : Soil-Structure Interaction
shima aghakasiri
1
,
Mehdi Shahraki
2
,
sanaz aghakasiri
3
1 - Department of Civil Engineering, ST.C , Islamic Azad University, Teharn, Iran.
2 - Department of Civil Engineering, Zah.C., Islamic Azad University, Zahedan, Iran.
3 - M.Sc., Grad., Department of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran.
کلید واژه: Soil liquefaction potential, Artificial Neural Network, Logistic Regression, Neuro-Fuzzy Network, Cone Penetration Test (CPT),
چکیده مقاله :
This study presents a novel approach for predicting soil liquefaction potential, a critical concern in geotechnical engineering. Liquefaction refers to the behavior of soil under dynamic loading or transient shear wave excitation, during which the soil completely loses its shear strength and temporarily transforms into a fluid-like state. By integrating empirical geotechnical relationships with advanced machine learning techniques, the research offers a modern perspective on evaluating the likelihood of liquefaction occurrence. The analysis is based on data derived from Cone Penetration Test (CPT) records. Three soft computing models were implemented: Artificial Neural Networks (ANN), Logistic Regression (LR), and Neuro-Fuzzy Network. Their performance was evaluated using Receiver Operating Characteristic (ROC) curves. Among the models compared, Logistic Regression demonstrated superior performance, with the Area Under the Curve (AUC) from the “All” dataset reaching approximately 0.975, indicating high reliability in classification accuracy. In this study, the logistic regression model achieved an AUC of 0.975 on the full dataset, followed by the artificial neural network (AUC = 0.925) and the fuzzy logic system (AUC = 0.71).
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This study presents a novel approach for predicting soil liquefaction potential, a critical concern in geotechnical engineering. Liquefaction refers to the behavior of soil under dynamic loading or transient shear wave excitation, during which the soil completely loses its shear strength and temporarily transforms into a fluid-like state. By integrating empirical geotechnical relationships with advanced machine learning techniques, the research offers a modern perspective on evaluating the likelihood of liquefaction occurrence. The analysis is based on data derived from Cone Penetration Test (CPT) records. Three soft computing models were implemented: Artificial Neural Networks (ANN), Logistic Regression (LR), and Neuro-Fuzzy Network. Their performance was evaluated using Receiver Operating Characteristic (ROC) curves. Among the models compared, Logistic Regression demonstrated superior performance, with the Area Under the Curve (AUC) from the “All” dataset reaching approximately 0.975, indicating high reliability in classification accuracy. In this study, the logistic regression model achieved an AUC of 0.975 on the full dataset, followed by the artificial neural network (AUC = 0.925) and the fuzzy logic system (AUC = 0.71).
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