Plus-Cloud and Cloud with limited single stripe analyses: Two new methodologies for efficient and accurate estimation of fragility curves

Abstract

The generation of fragility curves typically involves three primary methodologies: ‎Cloud analysis, Incremental Dynamic Analysis (IDA), and Multiple-Stripe ‎Analysis (MSA). MSA and IDA analyses require time-consuming nonlinear analyses to accurately estimate the fragility curve. In contrast, Cloud analysis requires less computational effort but faces challenges in selecting the appropriate record for structural analysis. ‎Recent research have introduced methodologies, such as Pushover analysis and supplementary rules for record selection, to address the limitations of existing methods and reduce computational costs. The first novel method aims to overcome the limitations of cloud analysis at the collapse ‎‎performance level by applying a scale factor to the records. This approach allows ‎data that ‎previously didn’t meet the collapse performance level during cloud analysis to ‎be ‎effectively included within that range. Nonetheless, to improve regression data accuracy, a ‎‎constraint is implemented, easily attainable by the scale factor used.‎ The second method integrates the Cloud and SSA approaches, making use of the computational efficiency of Cloud and the accuracy of SSA. these two methods eliminate the need for supplementary criteria in record selection or Pushover analysis and avoids reliance on constant parameters or calibration-dependent equations. The methods are introduced under the names Plus-Cloud (PC) and Cloud with Limited SSA (CLS). These approaches have been assessed using the fragility curves of optimized structures. The results indicate that the fragility curves generated by these proposed methods exhibit a satisfactory alignment with MSA analysis. The median difference between the fragility curves of the two proposed methods and MSA analysis is approximately 0.1 g. Moreover, these methods reduce computational efforts by four to five times compared to the MSA analysis.