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Finding an optimal design for a structural system subject to seismic actions to minimize failure probability, repair costs, and injuries to occupants, significantly contributes to the resilience of buildings in earthquake regions. This research presents a comprehensive framework for the performance-based design optimization of steel structures, incorporating the Performance-Based Earthquake Engineering (PBEE) methodology delineated in FEMA P-58 [1]. A selected set of ground motions, consistent with the seismic hazard intensity of interest, and a nonlinear finite element model, established using OpenSees, enable the assessment of the system's dynamic response. To address the computational complexity related to evaluating the probability of failure of the system during an optimization iteration when using the PBEE methodology for assessing performance, this study introduces metamodeling techniques as a substitute for the original high-fidelity nonlinear finite element model. In particular, Kriging is employed to approximate both the median and standard deviation of the Engineering Demand Parameters (EDPs) in the design domain. The parameters of the Kriging metamodels are derived from nonlinear dynamic analyses performed using the original high-fidelity model and an optimal sampling plan obtained through Latin Hypercube sampling. Under the assumption of a lognormal distribution, the metamodel is then used to generate a large number of simulated demand sets necessary for the Monte Carlo procedure adopted by FEMA P-58 to calculate the distribution of probable losses for any given value of the design variable vector. Additionally, the median and standard deviation of the fragility function modeling collapse are also approximated by a Kriging metamodel, in which the parameters are derived from an Incremental Dynamic Analysis (IDA) for any given value of the design variable vector. The scheme is illustrated in a full-scale case study consisting of the performance-based optimization of the buckling-restrained braces of a steel seismic force-resisting system in terms of expected losses and construction costs. The study demonstrates that the proposed risk-based optimization scheme effectively balances construction costs with expected financial losses from earthquakes, thus enhancing the seismic performance of the system.[1] Applied Technology Council, & National Earthquake Hazards Reduction Program (US). (2012). Seismic performance assessment of buildings. Federal Emergency Management Agency.
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Seismic fragility analysis using nonlinear autoregressive neural networks with exogenous input
Rapidly growing societal needs in urban areas are increasing the demand for tall buildings with complex structural systems. Many of these buildings are located in areas characterized by high seismicity. Quantifying the seismic resilience of these buildings requires comprehensive fragility assessment that integrates iterative nonlinear dynamic analysis (NDA). Under these circumstances, traditional finite element (FE) analysis may become impractical due to its high computational cost. Soft-computing methods can be applied in the domain of NDA to reduce the computational cost of seismic fragility analysis. This study presents a framework that employs nonlinear autoregressive neural networks with exogenous input (NARX) in fragility analysis of multi-story buildings. The framework uses structural health monitoring data to calibrate a nonlinear FE model. The model is employed to generate the training dataset for NARX neural networks with ground acceleration and displacement time histories as the input and output of the network, respectively. The trained NARX networks are then used to perform incremental dynamic analysis (IDA) for a suite of ground motions. Fragility analysis is next conducted based on the results of the IDA obtained from the trained NARX network. The framework is illustrated on a twelve-story reinforced concrete building located at Oklahoma State University, Stillwater campus.
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- Award ID(s):
- 1835371
- PAR ID:
- 10291931
- Date Published:
- Journal Name:
- Structure and Infrastructure Engineering
- ISSN:
- 1573-2479
- Page Range / eLocation ID:
- 1 to 15
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1080/15732479.2021.1894184
