用于高效时变可靠性分析的双域融合长短期记忆模型
Dual-domain fused LSTM modeling for efficient time-dependent reliability analysis
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中文总结 AI 辅助
针对时变可靠性分析中传统方法不足，提出双域融合长短期记忆（DDF-LSTM）模型，通过新颖架构联合处理时变和非时变域信息，设计改进损失函数，有效捕捉相关依赖关系，经案例验证有更高计算效率和预测准确性。
中文摘要 AI 辅助
时变可靠性分析对于确保工程系统在不确定性下的长期安全和性能至关重要。传统代理模型方法难以纳入与时间无关的随机变量并捕捉其与时间相关随机过程的复杂相互作用。本文提出双域融合长短期记忆（DDF-LSTM）模型用于高效准确的时变可靠性分析。开发了一种新颖网络架构联合处理来自时变和非时变域的信息，将非时变变量嵌入初始隐藏状态，引入全连接层映射LSTM输出和非时变变量到最终输出空间。设计改进损失函数强调模型对最小响应的敏感性以提高失效概率估计精度。该方法有效捕捉随机变量、随机过程和极限状态函数时间行为之间的依赖关系。训练后的DDF-LSTM模型能以最小计算成本通过高效蒙特卡洛模拟估计时变失效概率。四个案例研究验证了该方法增强的计算效率和预测准确性。
英文摘要
Time-dependent reliability analysis is crucial for ensuring the long-term safety and performance of engineering systems under uncertainties. However, traditional surrogate model methods often struggle to incorporate time-independent random variables and capture their complex interactions with time-dependent stochastic processes. To overcome this limitation, this paper proposes a dual-domain fused long short-term memory (DDF-LSTM) model for efficient and accurate time-dependent reliability analysis. A novel network architecture is developed to jointly process information from both time-dependent and time-independent domains. Specifically, the time-independent variables are embedded into the initial hidden states, and a fully connected layer is introduced to map both LSTM outputs and time-independent variables into the final output space. Furthermore, an improved loss function is designed to emphasize the model's sensitivity to minimum responses, thereby improving the precision of failure probability estimation. The proposed method effectively captures the dependencies among random variables, stochastic processes, and the temporal behavior of limit state functions. Once trained, the DDF-LSTM model enables efficient Monte Carlo simulation to estimate time-dependent failure probabilities with minimal computational cost. Four case studies validate the proposed method's enhanced computational efficiency and predictive accuracy.
