  This paper introduces scour physics-informed neural networks (SPINNs), a
hybrid physics-data-driven framework for bridge scour prediction using deep
learning. SPINNs are developed based on historical scour monitoring data and
integrate physics-based empirical equations into neural networks as
supplementary loss components. We incorporated three architectures: LSTM, CNN,
and NLinear as the base data-driven model. Despite varying performance across
different base models and bridges, SPINNs overall outperformed pure data-driven
models. In some bridge cases, SPINN reduced forecasting errors by up to 50
percent. In this study, we also explored general models for bridge clusters,
trained by aggregating datasets across multiple bridges in a region. The pure
data-driven models mostly benefited from this approach, in particular bridges
with limited data. However, bridge-specific SPINNs provided more accurate
predictions than general SPINNs for almost all case studies. Also, the
time-dependent empirical equations derived from SPINNs showed reasonable
accuracy in estimating maximum scour depth, providing more accurate predictions
compared to HEC-18. Comparing both SPINNs and pure deep learning models with
traditional HEC-18 equation indicates substantial improvements in scour
prediction accuracy. This study can pave the way for hybrid physics-machine
learning methodologies to be implemented for bridge scour design and
maintenance.
