Evaluating the accuracy and efficiency of Cloud-to-IDA for drift-based collapse assessment of steel frames under near- and far-field motions

Accurate estimation of seismic collapse capacity is essential for performance-based earthquake engineering and fragility assessment. Incremental Dynamic Analysis (IDA) is widely regarded as a reliable benchmark for collapse capacity evaluation; however, its high computational cost limits its application in large-scale or rapid assessment studies. This study evaluates the accuracy and computational efficiency of conventional Cloud Analysis and the Cloud-to-IDA procedure compared with IDA for steel moment-resisting frames subjected to near-field and far-field ground motions and investigates the influence of structural height and ground-motion characteristics on the accuracy of each assessment method. Three two-dimensional steel moment-resisting frames with 3, 5, and 9 stories were modeled in OpenSees and analyzed using 22 far-field and 28 near-field ground motion records from the FEMA P695 suite. Spectral acceleration at the fundamental period, Sa(T1), was adopted as the intensity measure, and maximum inter-story drift ratio was used as the engineering demand parameter. A drift-based collapse limit state was defined using HAZUS inter-story drift thresholds to provide a consistent comparison basis among the three methods. The results show that conventional Cloud Analysis can produce height- and record-dependent bias, with the bias diminishing as frame height increases from 3 to 9 stories, in estimating median collapse capacity. The largest discrepancy was observed for the 3-story frame under far-field records, where Cloud Analysis overestimated the IDA-based median collapse capacity by approximately 20%. In contrast, Cloud-to-IDA closely reproduced the IDA results across all studied frame heights and ground-motion categories, with median collapse capacity errors limited to only a few percent. Dispersion estimates from Cloud-to-IDA were also more consistent with IDA than those obtained from conventional Cloud Analysis. In terms of computational demand, Cloud-to-IDA substantially reduced the number of nonlinear dynamic analyzes and achieved approximately 86–91% reduction in computational time relative to IDA, while maintaining close agreement in both median capacity and dispersion. These findings indicate that Cloud-to-IDA can provide an efficient and reliable alternative for drift-based collapse fragility assessment of steel moment-resisting frames, provided that its applicability is carefully limited to the structural systems, ground-motion sets, and collapse criteria considered in the study.