Probabilistic seismic analysis of bridges through machine learning approaches

Probabilistic seismic demands of bridge components such as bridge column and deck are conventionally expressed as a power-law function of a single ground motion intensity measure. This unidimensional probabilistic seismic demand model (PSDM) was introduced more than two decades ago, and since then, it was commonly used to estimate seismic demands. Over the recent years, an extensive body of research has been evolved to propose alternative PSDMs, but none has been proved to be dominantly superior over other approaches. There yet remains a milestone to enrich predictions provided by PSDMs and expanding their application beyond certain methodology, particular functional form, and corresponding assumptions on the distribution of the demands. Given the advancements in computational technologies which lead to the growth of diverse analytically-driven data, machine learning (ML) approaches have a tremendous potential to revolutionize predictions of seismic demands. This study presents a comprehensive appraisal of ML-based PSDMs to further expand the research advances in this domain and leverage the efficiency and advantages that ML methods offer compared to the unidimensional model. To this end, the efficiency of a variety of parametric and non-parametric ML algorithms with different degrees of flexibility are explored to estimate the demands associated with the primary bridge components. Moreover, by applying ML-based variable selection techniques, this study assesses the level of influence of the random variables on the generated PSDMs. These variables are used for the treatment of inherent uncertainties in material, geometric, structural, and ground motion parameters. As part of the appraisal, a ranking is provided for the investigated 39 models, such as Generalized Linear Models, Multi-order regressions, Bagging and Boosting, and Kernel-based models, according to their statistical performance in estimating the individual demands.