  In this study, we propose a novel surrogate modelling approach to efficiently
and accurately approximate the response of complex dynamical systems driven by
time-varying Recently, there has been increased interest in assessing the
seismic fragility of industrial plants and process equipment. This is reflected
in the growing number of studies, community-funded research projects and
experimental campaigns on the matter.Nonetheless, the complexity of the problem
and its inherent modelling, coupled with a general scarcity of available data
on process equipment, has limited the development of risk assessment methods.
In fact, these limitations have led to the creation of simplified and
quick-to-run models. In this context, we propose an innovative framework for
developing state-dependent fragility functions. This new methodology combines
limited data with the power of metamodelling and statistical techniques, namely
polynomial chaos expansions (PCE) and bootstrapping. Therefore, we validated
the framework on a simplified and inexpensive-to-run MDoF system endowed with
Bouc-Wen hysteresis.Then, we tested it on a real nonstructural industrial
process component. Specifically, we applied the state-dependent fragility
framework to a critical vertical tank of a multicomponent full-scale 3D steel
braced frame (BF). The seismic performance of the BF endowed with process
components was captured by means of shake table campaign within the European
SPIF project. Finally, we derived state-dependent fragility functions based on
the combination of PCE and bootstrap at a greatly reduced computational cost.
