A hybrid GMDH neural network and logistic regression framework for state parameter–based liquefaction evaluation

Abstract

The cyclic stress or liquefaction behavior of granular materials is strongly affected by the relative density and confining pressure of the soil. In this study, the state parameter accounting for both relative density and effective stress was used to evaluate soil liquefaction potential. Based on case histories along with the cone penetration test (CPT) database, models for calculating the state parameter using a group method of data handling (GMDH) neural network were developed and recommended according to their performance. The state parameter was then used to develop a state parameter–based probabilistic liquefaction evaluation method using a logistic regression model. From a conservative point of view, the boundary curve of 20% probability of liquefaction was suggested as a deterministic criterion for state parameter–based liquefaction evaluation. Subsequently, a mapping function relating the calculated factor of safety (FS) to the probability of liquefaction (PL) was proposed based on the compiled CPT database. Based on the developed PL–FS function, a new risk criterion associated with the state parameter–based design chart was proposed. Finally, a flowchart of state-based probabilistic liquefaction evaluation and quality control for ground-improvement projects was presented for the benefit of practitioners.