Efficient Bayesian metamodeling for fine-grained and robust fragility analysis of buildings at a regional scale
Efficient Bayesian metamodeling for fine-grained and robust fragility analysis of buildings at a regional scale
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DOI：
10.1016/j.strusafe.2023.102324
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发表时间：
2023-05
影响因子：
5.8
通讯作者：
Peiyang Su;Feng Xiong;Yang Lu;Qidan Hu;Bowen Zhang
中科院分区：
文献类型：
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作者：
Peiyang Su;Feng Xiong;Yang Lu;Qidan Hu;Bowen Zhang
考虑“土-结构-群相互作用”（SSCI）效应的细粒度区域建筑物地震易损性分析面临精度与效率之间的矛盾。在本研究中，贝叶斯神经网络（BNN）模型是用来解决这个问题。具体而言，传统的神经网络（NN）算法和贝叶斯推理集成到一个方法，其中NN预测的结构响应的建筑物和贝叶斯推理量化的认知不确定性的脆弱性估计所产生的有限的响应数据，由于高计算成本的结构分析。此外，高斯核函数为基础的数据增强（KDA）算法被提出来采样的模拟结构响应数据的BNN模型训练。以四川大学区域性建筑为例，对该框架进行了应用研究。结果表明，BNN能够准确、鲁棒地进行易损性估计，且建模效率高。
A fine-grained seismic fragility analysis of regional buildings considering ‘Soil-Structure-Cluster Interaction’ (SSCI) effect faces a dilemma between accuracy and efficiency. In the present study, a Bayesian Neural Network (BNN) model is adopted to address this problem. Specifically, the conventional neural network (NN) algorithm and a Bayesian inference are integrated into one approach, where the NN predicts the structural responses of buildings and the Bayesian inference quantifies the epistemic uncertainty of fragility estimations arising from limited response data due to the high computational cost of the structural analysis. Moreover, the Gaussian kernel function-based data augmentation (KDA) algorithm is proposed to sample the simulated structural response data for BNN model training. The proposed framework is implemented on the regional buildings of Sichuan University as a case study. The results show that the BNN can make accurate and robust fragility estimations with high modeling efficiency.