An energy-based framework is proposed for the dynamic stability assessment of structures subjected to earthquake-fire scenarios and verified through earthquake-fire hybrid simulation. In this framework, the wavelet packet Long Short-Term Memory (LSTM) model is used to separate the noise and residue caused by earthquake and fire within the structural response signals, guided by wavelet packet energy and power spectral density of signals. Moreover, a comparative analysis is performed with 4 previous signal processing models (empirical mode decomposition, variational mode decomposition, wavelet packet transform, and LSTM). Additionally, Latin hypercube sampling is employed to account for uncertainties in structural characteristics and hazards for dataset establishment and fragility analysis. The experimental findings suggest a decline in the structural dominant frequency with the emergence of high-frequency noise and residuals in structural responses due to the multi-hazard effect. The wavelet packet transform eliminates the high-frequency noise in the signal and avoids the oscillation of the LSTM prediction results. Post-earthquake fire increases the structural collapse possibility even under a moderate earthquake excitation. The proposed framework proves to be rational and has the potential to be further applied to other multi-hazard scenarios.

中文翻译：

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地震-火灾情景下基于能量的结构倒塌评估的小波包深度学习模型：框架和混合模拟


提出了一种基于能量的框架，用于地震-火灾场景下结构的动态稳定性评估，并通过地震-火灾混合模拟进行验证。在此框架中，小波包长短期记忆（LSTM）模型用于在小波包能量和信号功率谱密度的指导下，分离结构响应信号中由地震和火灾引起的噪声和残留。此外，还与之前的4种信号处理模型（经验模态分解、变分模态分解、小波包变换和LSTM）进行了比较分析。此外，采用拉丁超立方抽样来解释结构特征的不确定性以及数据集建立和脆弱性分析的危险。实验结果表明，由于多灾害效应，结构主频率随着高频噪声和结构响应残差的出现而下降。小波包变换消除了信号中的高频噪声，避免了LSTM预测结果的振荡。即使在中等地震激发下，震后火灾也会增加结构倒塌的可能性。所提出的框架被证明是合理的，并且有潜力进一步应用于其他多种灾害场景。