Surrogate Models for Seismic Response of Structures
The seismic risks to a structure or a set of structures in a region are usually determined by generating fragility curves that provide the probability of a building responding in a certain manner for a given level of ground motion intensity. Developing fragility curves, however, is challenging as it involves the computationally expensive task of obtaining the maximum response of the selected structures to a suite of ground motions representing the seismic hazard of the region selected.
This study presents a methodology to develop surrogate models for the prediction of the maximum responses of buildings to ground motion excitation. Data-driven surrogate models using simple machine learning techniques and physics-based surrogate models using the space mapping technique to map the low-fidelity responses obtained using a multi-degree of freedom shear building model to the high-fidelity values are developed for the prediction of the maximum roof drift ratio and the maximum story drift ratio of a chosen 15-story steel moment-resisting frame building with varying structural properties in California. The predictions of each of these surrogate models are analyzed to assess and compare the performance, capabilities, and limitations of these models. Best practices for developing surrogate models for the prediction of maximum responses of structures to ground motion are recommended.
The results from the development of data-driven surrogate models show that the spectral displacement is the best intensity measure to condition the maximum roof drift ratio, and the spectral velocity is the best intensity measure to condition the maximum story drift ratio. Fragility analysis of the structure is thus conducted using maximum story drift as the engineering demand parameter and spectral velocity as the intensity measure. Monte Carlo simulation is conducted using the physics-based surrogate model to estimate the maximum story drifts for ground motions that are incrementally scaled to different intensity levels. Maximum likelihood estimates are used to obtain the parameters for a lognormal distribution and the 95% confidence intervals are obtained using the Wald confidence interval to plot the fragility curves.
Fragility curves are plotted both with and without variations in the structural properties of the building, and it is found that the effects of variability in ground motions on the fragility are far higher than the effects of the randomness of structural properties. Finally, it is found that about 65 ground motion records are needed for convergence of the parameters of the lognormal distribution for plotting fragility curves by using Monte Carlo simulation.
- Master of Science in Civil Engineering
- Civil Engineering
- West Lafayette