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A Cepstrum-Informed neural network for Vibration-Based structural damage assessment
Computers & Structures ( IF 4.4 ) Pub Date : 2024-11-25 , DOI: 10.1016/j.compstruc.2024.107592
Lechen Li, Adrian Brügger, Raimondo Betti, Zhenzhong Shen, Lei Gan, Hao Gu
Computers & Structures ( IF 4.4 ) Pub Date : 2024-11-25 , DOI: 10.1016/j.compstruc.2024.107592
Lechen Li, Adrian Brügger, Raimondo Betti, Zhenzhong Shen, Lei Gan, Hao Gu
Data-driven methods for vibration-based Structural Health Monitoring (SHM) have gained significant popularity for their straightforward modeling process and real-time tracking capabilities. However, developing complex models such as deep neural networks can pose challenges, including limited interpretability and substantial computational demands, due to the large number of parameters and deep layer stacking. This study introduces a novel Cepstrum-Informed Attention-Based Network (CIABN) developed to model power cepstral coefficients of structural acceleration responses, guided by cepstrum-based physical properties to facilitate efficient structural damage assessment. The CIABN integrates three key components: a unique input–output mapping based on weighted cepstral coefficients, a novel cepstral positional encoding mechanism, and a multi-head self-attention mechanism. The unique input–output mapping enables appreciable model generalization in overall structural characteristics, with the weighted cepstral coefficients serving as informative and compact data for efficient neural network modeling. The developed cepstral positional encoding scientifically guides the model to capture the coefficient indices, and the underlying trend of cepstral coefficients primarily governed by overall structural characteristics. The multi-head attention mechanism enables computationally efficient parallel analysis of interdependencies among coefficients, facilitating the development of a lightweight network. The effectiveness and superiority of the method have been validated using both simulated and experimental structural data.
中文翻译:
用于基于振动的结构损伤评估的 Cepstrum-Informed 神经网络
基于振动的结构健康监测 (SHM) 的数据驱动方法因其简单的建模过程和实时跟踪功能而广受欢迎。然而,由于参数数量众多和深层堆叠,开发深度神经网络等复杂模型可能会带来挑战,包括有限的可解释性和大量的计算需求。本研究介绍了一种新的 Cepstrum-Informed Attention-Based Network (CIABN),该网络用于模拟结构加速反应的幂倒谱系数,由基于 Cepsrum 的物理特性指导,以促进有效的结构损伤评估。CIABN 集成了三个关键组件:基于加权倒谱系数的独特输入-输出映射、新颖的倒谱位置编码机制和多头自注意力机制。独特的输入-输出映射使模型在整体结构特征中实现了明显的泛化,加权倒谱系数作为信息丰富且紧凑的数据,可实现高效的神经网络建模。开发的倒谱位置编码科学地指导模型捕获系数指数,以及主要由整体结构特征控制的倒谱系数的潜在趋势。多头注意力机制能够对系数之间的相互依赖关系进行高效的计算并行分析,从而促进轻量级网络的开发。该方法的有效性和优越性已使用模拟和实验结构数据进行了验证。
更新日期:2024-11-25
中文翻译:
用于基于振动的结构损伤评估的 Cepstrum-Informed 神经网络
基于振动的结构健康监测 (SHM) 的数据驱动方法因其简单的建模过程和实时跟踪功能而广受欢迎。然而,由于参数数量众多和深层堆叠,开发深度神经网络等复杂模型可能会带来挑战,包括有限的可解释性和大量的计算需求。本研究介绍了一种新的 Cepstrum-Informed Attention-Based Network (CIABN),该网络用于模拟结构加速反应的幂倒谱系数,由基于 Cepsrum 的物理特性指导,以促进有效的结构损伤评估。CIABN 集成了三个关键组件:基于加权倒谱系数的独特输入-输出映射、新颖的倒谱位置编码机制和多头自注意力机制。独特的输入-输出映射使模型在整体结构特征中实现了明显的泛化,加权倒谱系数作为信息丰富且紧凑的数据,可实现高效的神经网络建模。开发的倒谱位置编码科学地指导模型捕获系数指数,以及主要由整体结构特征控制的倒谱系数的潜在趋势。多头注意力机制能够对系数之间的相互依赖关系进行高效的计算并行分析,从而促进轻量级网络的开发。该方法的有效性和优越性已使用模拟和实验结构数据进行了验证。