Investigation of Seismic Amplification on Soil–Structure Interaction Problems Based on a 3D DRM-RFEM Framework
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 10, Issue 1
Abstract
The spatial variability of soil properties is pervasive, and can affect the propagation of seismic waves and the dynamic responses of soil–structure interaction (SSI) systems. This uncertainty is likely to increase the damage state of a structure and its risk of collapse. Additionally, conducting multiscale simulations efficiently in the presence of uncertainties is a pressing concern that must be addressed. In this work, a 3D probabilistic analysis framework for an SSI system considering site effects and spatial variability of soil property (i.e., elastic modulus, ) has been proposed. This framework is based on the random finite element method (RFEM) and domain reduction method (DRM). A multiscale model of a five-story reinforced concrete (RC) frame structure was developed on an ideal 3D slope to verify the effectiveness of the proposed framework. The dynamic responses of the structure were analyzed, and the peak floor acceleration (PFA) and peak interstory drift ratio (PSDR) were selected to estimate the damage state of structures. It was found that the proposed method significantly improves computational efficiency approximately 20 times compared with the direct method. In the regional models, with the increase of the coefficient of variation (COV) of , the energy of seismic waves becomes more concentrated at the crest and the response spectrum value of medium and long periods increases. In the local SSI model, the soil variability increases the mean value of PSDR, resulting in a more severe damage state compared to the results from the deterministic analysis. Consequently, this study provides some suggestions for engineering practice, and the importance of probabilistic analysis considering spatially variable soils in the SSI problem is highlighted.
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Data Availability Statement
All data and models that support the findings of this study are available from the corresponding author upon reasonable request.
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 10 • Issue 1 • March 2024
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© 2024 American Society of Civil Engineers.
History
Received: Jul 11, 2023
Accepted: Oct 19, 2023
Published online: Jan 4, 2024
Published in print: Mar 1, 2024
Discussion open until: Jun 4, 2024
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