Investigation of Seismic Amplification on Soil–Structure Interaction Problems Based on a 3D DRM-RFEM Framework

空间变异性 概率逻辑 残余物 土-结构相互作用 有限元法 结构工程 帧(网络) 加速度 计算机科学 地质学 环境科学 数学 工程类 算法 统计 物理 电信 经典力学 人工智能
作者
Hongjie Fang,Zhichao Lai,Chuanxiang Qu
出处
期刊:ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering [American Society of Civil Engineers]
卷期号:10 (1) 被引量:8
标识
DOI:10.1061/ajrua6.rueng-1181
摘要

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, E) 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 E, 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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