A number of simplified methodologies have been developed and used to estimate seismic drift demands in buildings. However, none of them have been systematically tested against a large number of buildings subjected to a diverse set of ground motions. This is partly attributed to the lack of existing databases of building designs, nonlinear structural models, and simulated seismic responses. This article introduces the development of a comprehensive database, which includes 621 special steel moment-resisting frames designed in accordance with modern codes and standards and their corresponding nonlinear structural models and seismic responses (i.e. peak story drifts, peak floor accelerations, and residual story drifts). The seismic responses for a subgroup of 100 steel moment-resisting frames subjected to three groups of site-specific ground motions (with 40 records each), at the service-level, design-based, and maximum considered earthquakes, are also included. The database has been utilized by the authors (in a separate study) to evaluate the performance of existing methods and develop data-driven and hybrid (combination of mechanics-based and data-driven) models for estimating seismic structural drift demands. The database can also be utilized in the development and implementation of a performance-based analytics-driven seismic design methodology.
Nonlinear response history analysis (NLRHA) is generally considered to be a reliable and robust method to assess the seismic performance of buildings under strong ground motions. While NLRHA is fairly straightforward to evaluate individual structures for a select set of ground motions at a specific building site, it becomes less practical for performing large numbers of analyses to evaluate either (1) multiple models of alternative design realizations with a site‐specific set of ground motions, or (2) individual archetype building models at multiple sites with multiple sets of ground motions. In this regard, surrogate models offer an alternative to running repeated NLRHAs for variable design realizations or ground motions. In this paper, a recently developed surrogate modeling technique, called probabilistic learning on manifolds (PLoM), is presented to estimate structural seismic response. Essentially, the PLoM method provides an efficient stochastic model to develop mappings between random variables, which can then be used to efficiently estimate the structural responses for systems with variations in design/modeling parameters or ground motion characteristics. The PLoM algorithm is introduced and then used in two case studies of 12‐story buildings for estimating probability distributions of structural responses. The first example focuses on the mapping between variable design parameters of a multidegree‐of‐freedom analysis model and its peak story drift and acceleration responses. The second example applies the PLoM technique to estimate structural responses for variations in site‐specific ground motion characteristics. In both examples, training data sets are generated for orthogonal input parameter grids, and test data sets are developed for input parameters with prescribed statistical distributions. Validation studies are performed to examine the accuracy and efficiency of the PLoM models. Overall, both examples show good agreement between the PLoM model estimates and verification data sets. Moreover, in contrast to other common surrogate modeling techniques, the PLoM model is able to preserve correlation structure between peak responses. Parametric studies are conducted to understand the influence of different PLoM tuning parameters on its prediction accuracy.
more » « less- Award ID(s):
- 2131111
- PAR ID:
- 10418905
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Earthquake Engineering & Structural Dynamics
- Volume:
- 52
- Issue:
- 8
- ISSN:
- 0098-8847
- Page Range / eLocation ID:
- p. 2407-2428
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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