The performance comparison of the decision tree models on the prediction of seismic gravelly soil liquefaction potential based on dynamic penetration test
Document Type
Article
Publication Date
7-1-2023
Abstract
Seismic liquefaction has been reported in sandy soils as well as gravelly soils. Despite sandy soils, a comprehensive case history record is still lacking for developing empirical, semi-empirical, and soft computing models to predict this phenomenon in gravelly soils. This work compiles documentation from 234 case histories of gravelly soil liquefaction from across the world to generate a database, which will then be used to develop seismic gravelly soil liquefaction potential models. The performance measures, namely, accuracy, precision, recall, F-score, and area under the receiver operating characteristic curve, were used to evaluate the training and testing tree-based models' performance and highlight the capability of the logistic model tree over reduced error pruning tree, random tree and random forest models. The findings of this research can provide theoretical support for researchers in selecting appropriate tree-based models and improving the predictive performance of seismic gravelly soil liquefaction potential.
Keywords
gravelly soil, liquefaction, reduced error pruning tree, random forest, dynamic penetration test, logistic model tree, random tree
Divisions
sch_civ
Funders
Ministry of Science and Higher Education of the Russian Federation (075-15-2021-1333)
Publication Title
Frontiers in Earth Science
Volume
11
Publisher
Frontiers Media
Publisher Location
AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND