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Predictive models for estimating strong-motion duration in sites characterized by soft-soil profiles are presented in this paper. The models were developed using a strong-motion database that includes observations from subduction interface earthquakes that occurred from 1989 to 2020 and recorded in Mexico City, which is located at source-to-site distances up to 600 km. A linear mixed-effects regression model, which is a statistical fitting procedure that allows to consider the correlation structure of grouped data, was used to develop the predictive models. Relative significant duration was selected to measure strong-motion duration. This measure can be directly associated with the accumulation of energy of the ground movement. The proposed predictive models relate relative significant duration with moment magnitude, either hypocentral distance or closest distance to the rupture plane, and dominant period of the soil. Regression analyses were performed grouping the ground-motion data by both seismic event and site class. Model assumptions, such as homoscedasticity, normality, and linearity of effects, were verified from residual analyses. From the results, the expected value of the natural logarithm of relative significant duration was found to be ∼1.2 times greater for an earthquake with a moment magnitude equal to 8.0 than for one of 6.0. An insightful discussion about the sources and character of the uncertainties detected in the proposed predictive models is also presented in this study. The predictive models proposed in this paper are of valuable application in seismic and structural engineering because they allow to circumscribe properly the dimension and randomness of strong-motion duration.

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