用图像+物理约束模型预测意大利面桥承重，提前预警结构失效。
Seeing Structural Failure Before it Happens: An Image-Based Physics-Informed Neural Network (PINN) for Spaghetti Bridge Load Prediction
- 结合物理定律与计算机视觉，构建图像驱动的结构预测模型。
- 在100组数据上实现96%决定系数，误差仅10.5单位。
- 适合轻量级桥梁设计早期风险评估，开源可复现。
物理信息神经网络（PINNs）因其能将物理规律嵌入深度学习模型而受到关注，尤其适用于数据有限的结构工程任务。本文探索使用PINNs预测小型意大利面桥的承重能力，以理解简化结构模型的载荷极限与潜在失效模式。所提框架引入基于物理的约束以提升性能，除标准PINN外，还提出新型架构物理信息柯尔莫哥洛夫-阿诺德网络（PIKAN），融合通用函数逼近理论与物理洞见。输入结构参数通过人工或计算机视觉方法获取。数据集包含15座真实桥梁，经扩充达100个样本，最优模型达成0.9603的决定系数（R²）和10.50单位的平均绝对误差（MAE）。从应用角度，还提供基于网页的参数输入与预测界面。结果表明，即便数据有限，PINNs仍可可靠估计结构承载力，有助于轻量化桥梁设计的早期失效分析。完整数据与代码已公开于https://github.com/OmerJauhar/PINNS-For-Spaghetti-Bridges。
原文摘要 · Abstract (English)
Physics Informed Neural Networks (PINNs) are gaining attention for their ability to embed physical laws into deep learning models, which is particularly useful in structural engineering tasks with limited data. This paper aims to explore the use of PINNs to predict the weight of small scale spaghetti bridges, a task relevant to understanding load limits and potential failure modes in simplified structural models. Our proposed framework incorporates physics-based constraints to the prediction model for improved performance. In addition to standard PINNs, we introduce a novel architecture named Physics Informed Kolmogorov Arnold Network (PIKAN), which blends universal function approximation theory with physical insights. The structural parameters provided as input to the model are collected either manually or through computer vision methods. Our dataset includes 15 real bridges, augmented to 100 samples, and our best model achieves an $R^2$ score of 0.9603 and a mean absolute error (MAE) of 10.50 units. From applied perspective, we also provide a web based interface for parameter entry and prediction. These results show that PINNs can offer reliable estimates of structural weight, even with limited data, and may help inform early stage failure analysis in lightweight bridge designs. The complete data and code are available at https://github.com/OmerJauhar/PINNS-For-Spaghetti-Bridges.
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