Document Type : Original/Review Paper

Authors

• A. Ardakani ¹
• V. R. Kohestani ²

¹ Department of Engineering and Technology, Imam Khomeini International University, Qazvin, Iran

² Department of Civil Engineering, Imam Khomeini International University, Qazvin, Iran

Abstract

The prediction of liquefaction potential of soil due to an earthquake is an essential task in Civil Engineering. The decision tree is a tree structure consisting of internal and terminal nodes which process the data to ultimately yield a classification. C4.5 is a known algorithm widely used to design decision trees. In this algorithm, a pruning process is carried out to solve the problem of the over-fitting. This article examines the capability of C4.5 decision tree for the prediction of seismic liquefaction potential of soil based on the Cone Penetration Test (CPT) data. The database contains the information about cone resistance (q_c), total vertical stress (σ_0), effective vertical stress (σ_0^'), mean grain size (D_50), normalized peak horizontal acceleration at ground surface (a_max), cyclic stress ratio (τ/σ_0^') and earthquake magnitude (M_w). The overall classification success rate for the entire data set is 98%. The results of C4.5 decision tree have been compared with the available artificial neural network (ANN) and relevance vector machine (RVM) models. The developed C4.5 decision tree provides a viable tool for civil engineers to determine the liquefaction potential of soil.

Keywords

• Soil liquefaction
• Cone Penetration Test
• Artificial intelligence
• C4.5 Decision tree

Main Subjects

• I.3.7. Engineering