@inproceedings{Nardin2024PVP,
title = {State-Dependent Seismic Fragility Functions for Bolted Flange Joints on Special-Risk Industrial Substructures},
booktitle = {Proceedings of the ASME 2024 Pressure Vessels \& Piping Conference (PVP2024)},
volume = {Volume 5: Operations, Applications, \& Components; Seismic Engineering; ASME Nondestructive Evaluation, Diagnosis and Prognosis (NDPD) Division},
pages = {V005T08A010},
publisher = {American Society of Mechanical Engineers},
address = {Bellevue, Washington, USA},
year = {2024},
doi = {10.1115/PVP2024-123237},
url = {https://doi.org/10.1115/PVP2024-123237},
author = {C. Nardin and O. S. Bursi and M. Broccardo and S. Marelli},
keywords = {industrial facilities, piping, modelling, seismic loading, earthquakes, shaking-table},
abstract = {Seismic vulnerability assessment of industrial plants and process equipment has gained attention lately. However, the complexity of the problem and its modelling, combined with a general scarcity of available data on industrial systems, contributed to limiting or developing risk assessment methods based on extremely simplified models. On these premises, a new methodology that combines limited data from finite element (FE) models with cutting-edge machine learning techniques is developed to generate state-dependent fragility functions for critical components mounted on archetypical industrial substructure modules. Specifically, referring to the classical forward uncertainty quantification scheme, a physics-informed FE model, calibrated on the results of a comprehensive shake table test campaign of a real-scale industrial braced-frame (BF) steel substructure, is considered. Time histories of seismic event sequences generated by a site-based ground motion model compose the input provided to the FE model. The uncertainty is then propagated through Monte Carlo analysis on inexpensive-to-run polynomial chaos expansion (PCE) metamodels set for each starting damage level condition measure. As a result, empirical state-dependent fragilities are evaluated, assuming the lognormal distribution.}
}