Lateral stiffness of structural components, such as reinforced concrete (RC) columns, plays an important role in resisting the lateral earthquake loads. The lateral stiffness relates the lateral force to the lateral deformation, having a critical effect on the accuracy of the lateral seismic response predictions. The classical methods (e.g. fiber beam–column model) to estimate the lateral stiffness require calculations from section, element, and structural levels, which is time-consuming. Moreover, the shear deformation and bond-slip effect may also need to be included to more accurately calculate the lateral stiffness, which further increases the modeling difficulties and the computational cost. To reduce the computational time and enhance the accuracy of the predictions, this article proposes a novel data-driven method to predict the laterally seismic response based on the estimated lateral stiffness. The proposed method integrates the machine learning (ML) approach with the hysteretic model, where ML is used to compute the parameters that govern the nonlinear properties of the lateral response of target structural components directly from a training set composed of experimental data (i.e. data-driven procedure) and the hysteretic model is used to directly output the lateral stiffness based on the computed parameters and then to perform the seismic analysis. We apply the proposed method to predict the lateral seismic response of various types of RC columns subjected to cyclic loading and ground motions. We present the detailed model formulation for the application, including the developments of a modified hysteretic model, a hybrid optimization algorithm, and two data-driven seismic response solvers. The results predicted by the proposed method are compared with those obtained by classical methods with the experimental data serving as the ground truth, showing that the proposed method significantly outperforms the classical methods in both generalized prediction capabilities and computational efficiency.
The 2018 Mw7.1 Anchorage, Alaska, earthquake is one of the largest earthquakes to strike near a major US city since the 1994 Northridge earthquake. The significance of this event motivated reconnaissance efforts to thoroughly document damage to the built environment. This article presents the spatial variability of ground motion intensity and its correlation with subsurface conditions in Anchorage, the identification of liquefaction triggering in the absence of surficial manifestations (such as sand boils or sediment ejecta), cyclic softening failure in organic soils, and the poor performance of anthropogenic fills subjected to cyclic loading. In addition to lessons from observed ground deformation and geotechnical effects on structures, this article provides case studies documenting the satisfactory behavior of improved ground subjected to cyclic loading and the appropriateness of current design procedures for the estimation of seismically induced sliding displacements of mechanically stabilized earth walls.
more » « less- NSF-PAR ID:
- 10229876
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Earthquake Spectra
- Volume:
- 37
- Issue:
- 4
- ISSN:
- 8755-2930
- Page Range / eLocation ID:
- p. 2372-2399
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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