Seeing Structural Failure Before it Happens: An Image-Based Physics-Informed Neural Network (PINN) for Spaghetti Bridge Load Prediction

TL;DR

This paper demonstrates how physics-informed neural networks can accurately predict the load capacity of small-scale spaghetti bridges, aiding early failure detection with limited data through a novel architecture and computer vision inputs.

Contribution

It introduces a new PINN architecture called PIKAN that integrates physical insights with universal approximation, applied to structural load prediction in lightweight bridges.

Findings

Achieved an R^2 score of 0.9603 on bridge weight prediction.

Developed a web interface for parameter input and load prediction.

Enhanced model performance with limited data through physics-based constraints.

Abstract

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.