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.

Lateral spreading is one of the most common secondary earthquake effects that cause severe damage to structures and lifelines. While there is no widely accepted approach to predicting lateral spread displacements, challenges to the existing empirical and machine learning models include obscurity, overfitting, and reluctance of practical users. This study reveals patterns in the available lateral displacement database, identifying rules that describe the significant relationships among various attributes that led to lateral spreading. Seven conditional attributes (earthquake magnitude, epicentral distance, maximum acceleration, fines content, mean grain size, thickness of liquefiable layer, and free -face ratio) and one decision attribute (horizontal displacement) were considered in modeling a binary class rough set machine learning. There are eighteen rules generated in the form of if -then statements. The decision support system reveals that the severity of lateral spreading clearly comes from the combinations of relevant attributes. Moreover, five clusters of rules were also observed from the generated rules. Useful information regarding the different lateral spreading case scenarios emerges from the results. Statistical validation and interpretation of rules using principles of soil mechanics and related studies were also performed. The output of this study, a decision support system, can be very useful to decision -makers and planners in understanding the lateral spreading phenomena. Recommendations for the model improvement and for further studies were discussed.