发表机构
Federal Highway Administration; Turner-Fairbank Highway Research Center（联邦公路管理局; 特纳-费尔班克公路研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究将物理信息神经网络（PINNs）与探地雷达（GPR）数据预测结合，构建含CNN、SFCA、ConvLSTM及TFFA的专用模型，提升了GPR数据预测精度，助力民用基础设施劣化评估。
AI 中文摘要
本研究探索将物理信息神经网络（Physics-Informed Neural Networks, PINNs）开创性地应用于探地雷达（Ground-Penetrating Radar, GPR）数据预测领域，提出了一种专用PINN模型的详细开发框架，该模型能像医学影像模型预测肿瘤行为一样解读和预测GPR数据。通过利用深度学习算法与地下结构物理规律（或医学领域的人体组织规律）的协同作用，模型将电磁波传播的物理规律有效嵌入其架构，确保预测结果既符合基础物理原理，又具备医学诊断中检测和监测肿瘤所需的精度。所提出的深度学习结构包含三个组件：卷积神经网络（CNN）、空间特征通道注意力（spatial feature channel attention, SFCA）机制、卷积长短期记忆网络（ConvLSTM），以及时间特征帧注意力（temporal feature frame attention, TFFA）模块。注意力机制通过自适应计算通道注意力和时间注意力权重，从而微调视觉和时间特征响应，以提取最相关、最重要的视觉和时间特征。通过将物理规律直接融入神经网络，本模型在预测GPR数据时展现出更高的准确性，这一提升对有效评估桥面状况及其他民用基础设施评估至关重要。物理信息神经网络（PINNs）的应用已展现出变革无损评估（Non-Destructive Evaluation, NDE）领域的潜力，可提升基础设施劣化预测的精度，还能通过基于物理的模型视角，为深入理解劣化的基本机制提供支持。
英文摘要
The research explores the pioneering integration of Physics-Informed Neural Networks (PINNs) into the domain of Ground-Penetrating Radar (GPR) data prediction. This research presents a detailed development framework for a specialized PINN model, proficient at interpreting and forecasting GPR data, much like how medical imaging models predict tumor behavior. By harnessing the synergy between deep learning algorithms and the physical laws governing subsurface structures or in medical terms, human tissues the model effectively embeds the physics of electromagnetic wave propagation into its architecture. This ensures that predictions not only align with fundamental physical principles but also mirror the precision needed in medical diagnostics for detecting and monitoring tumors. The suggested deep learning structure comprises three components: a CNN, a spatial feature channel attention (SFCA) mechanism, and ConvLSTM, along with temporal feature frame attention (TFFA) modules. The attention mechanism computes channel attention and temporal attention weights using self-adaptation, thereby fine tuning the visual and temporal feature responses to extract the most pertinent and significant visual and temporal features. By integrating physics directly into the neural network, our model has shown enhanced accuracy in forecasting GPR data. This improvement is vital for conducting effective assessments of bridge deck conditions and other evaluations related to civil infrastructure. The use of Physics Informed Neural Networks (PINNs) has demonstrated the potential to transform the field of Non-Destructive Evaluation (NDE) by enhancing the precision of infrastructure deterioration predictions. Moreover, it offers a deeper insight into the fundamental mechanisms of deterioration, viewed through the prism of physics-based models.
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