Accelerated Large-Scale Seismic Damage Simulation With a Bimodal Sampling Approach
Accelerated Large-Scale Seismic Damage Simulation With a Bimodal Sampling Approach
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DOI：
10.3389/fbuil.2021.677560
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发表时间：
2021-05
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通讯作者：
M. Sheibani;Ge Ou
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作者：
M. Sheibani;Ge Ou
区域破坏模拟是一种很有前途的方法，可以帮助组织为可能发生的地震自然灾害的不可预见的影响做好准备。对某一区域内建筑物的有限元模型进行非线性时程分析，可以得到与实际建筑物的损伤和反应相似的结果。这种方法需要大规模的计算资源，为了提高效率，提出了并行处理和用集中质量模型表示建立有限元模型。然而，在没有高性能计算的情况下，计算复杂度仍然是深远的。一个地区的建筑物清单由许多类似的建筑物组成，但不同的结构数量有限。在本文中，我们提出了一种数据驱动的方法，运行NLTHA的独特的结构和推断的损坏和其他建筑物的反应，使用代理模型。考虑到建筑物在区域中的偏态分布，提出了一种新的信息样本选择方法，该方法设计用于输入域的双峰采样。我们使用高斯过程回归作为代理模型，并比较不同的样本选择方法的性能。所提出的方法是能够近似的区域损害模拟的结果，总的经济损失估计与98.99%的准确性，同时减少了计算需求，约1/7的模拟处理时间。
Regional damage simulation is a promising method to prepare organizations for the unforeseeable impact of a probable seismic natural hazard. Nonlinear time history analysis (NLTHA) of the finite element models (FEM) of the buildings in a region can provide resembling results to the actual buildings’ damages and responses. This approach requires large-scale computational resources, and to improve efficiency, parallel processing and representing building FEM models with lumped mass models are proposed. However, the computing complexity is still far-reaching when high-performance computing is not available. The building inventory of a region consists of numerous similar buildings with a limited number of distinct structures. In this paper, we propose a data-driven method that runs the NLTHA for the distinct structures exclusively and infers the damage and responses of other buildings using a surrogate model. Considering the skewed distribution of the buildings in a region, a novel informative sample selection method is proposed that is designed for bimodal sampling of the input domain. We use the Gaussian process regression as the surrogate model and compare the performance of different sample selection methods. The proposed method is able to approximate the results of the regional damage simulation regarding total economic loss estimation with 98.99% accuracy while reducing the computational demand to about 1/7th of the simulation processing time.