Despite over six decades of field and laboratory investigations, theoretical studies, and advances in constitutive modeling, questions remain on the fundamental issues concerning liquefaction mechanisms, the collective influence of multiple factors on excess pore water pressure (EPWP) generation, and liquefaction triggering criteria. This paper presents the general apparent viscosity-and average flow coefficient-based methodology for quantifying the solid-liquid phase-change process of liquefiable soil under undrained cyclic loading. The analysis reveals that the evolution of the soil particle-fabric system is the fundamental physico-mechanical mechanism behind EPWP generation in a liquefiable soil, with the accompanying change in soil physical state serving as the intrinsic mechanism driving EPWP generation. The study further identifies the physico-mechanical foundations of EPWP generation, as well as the inherent causes and a unified quantitative characterization of the coupled influences of multiple factors on EPWP generation. This work presents the novel observation that the marginal peak excess pore pressure ratio (ru,pm) between the solid-liquid mixed phase and the liquid phase of liquefiable soil can be identified accurately and that ru,pm is characterized by its inherent robustness. A ru,pm value of 0.90 can be used as a liquefaction triggering criterion for soils both in laboratory element tests and in the field. Another original finding is that the liquefaction triggering resistance curve is the threshold state curve between solid-liquid mixed phase and transiently liquid phase of a liquefiable soil and is unique for a specific initial physical state. The definitions of liquefaction triggering and corresponding liquefaction triggering resistance are clear and unambiguous and have the same physico-mechanical basis. The insights obtained in this paper will potentially enable the scientific and engineering communities to reinterpret the liquefaction mechanism, its evaluation, and liquefaction mitigation strategies.

Infrastructure failure due to soil liquefaction has been repeatedly observed in past megathrust earthquakes, causing significant material and structural functionality losses. In most seismic regions, soil liquefaction potential is assessed using updated versions of the cyclic-stress-based simplified procedure initially proposed by Seed and Idriss in 1971. However, the application of these procedures to large-magnitude (Mw > 7.5) subduction earthquakes has shown discrepancies between forward predictions and field observations, particularly regarding liquefaction triggering and manifestation. This paper proposes an alternative model to assess soil liquefaction due to large-magnitude subduction earthquakes based on excess pore water pressure ratios and shear deformations. The triggering criteria are based on the peak values of excess pore pressure ratio and shear strain anticipated within the critical, potentially liquefiable soil layer. The model considers liquefiable layer thickness and relative density, along with input motion's Cumulative Absolute Velocity (CAV), as the main predictors of soil liquefaction. To this end, a numerical model was first developed and validated against results from a dynamic centrifuge test simulating free-field conditions. The calibrated numerical model was then used to perform a numerical parametric study to identify the trends and key predictors of liquefaction in layered soil deposits subjected to large-magnitude subduction earthquakes. Finally, a simplified probabilistic procedure, validated against available case histories, was developed to estimate the probabilities of full, marginal, and no liquefaction occurrence within each critical layer.

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.