Prediction of Soil Liquefaction Triggering Using Rule-Based Interpretable Machine Learning

Abstract

Seismic events remain a significant threat, causing loss of life and extensive damage in vulnerable regions. Soil liquefaction, a complex phenomenon where soil particles lose confinement, poses a substantial risk. The existing conventional simplified procedures, and some current machine learning techniques, for liquefaction assessment reveal limitations and disadvantages. Utilizing the publicly available liquefaction case history database, this study aimed to produce a rule-based liquefaction triggering classification model using rough set-based machine learning, which is an interpretable machine learning tool. Following a series of procedures, a set of 32 rules in the form of IF-THEN statements were chosen as the best rule set. While some rules showed the expected outputs, there are several rules that presented attribute threshold values for triggering liquefaction. Rules that govern fine-grained soils emerged and challenged some of the common understandings of soil liquefaction. Additionally, this study also offered a clear flowchart for utilizing the rule-based model, demonstrated through practical examples using a borehole log. Results from the state-of-practice simplified procedures for liquefaction triggering align well with the proposed rule-based model. Recommendations for further evaluations of some rules and the expansion of the liquefaction database are warranted.

Keywords

• artificial intelligence
• data mining
• rough set theory
• soil liquefaction triggering
• decision support tool
• cyclic softening