The prediction of rail crack length propagation plays a crucial role in the
maintenance and safety assessment of materials and structures. Traditional
methods rely on physical models and empirical equations such as Paris law,
which often have limitations in capturing the complex nature of crack growth.
In recent years, machine learning techniques, particularly Recurrent Neural
Networks (RNNs), have emerged as promising methods for time series forecasting.
They allow to model time series data, and to incorporate exogenous variables
into the model. The proposed approach involves collecting real data on the
French rail network that includes historical crack length measurements, along
with relevant exogenous factors that may influence crack growth. First, a
pre-processing phase was performed to prepare a consistent data set for
learning. Then, a suitable Bayesian multi-horizons recurrent architecture was
designed to model the crack propagation phenomenon. Obtained results show that
the Multi-horizons model outperforms state-of-the-art models such as LSTM and
GRU.

铁路裂纹长度传播的预测在材料和结构的维护和安全评估中起着至关重要的作用。近年来，机器学习技术，特别是循环神经网络 (RNNs)，已经成为时间序列预测的有前途的方法。通过收集包括历史裂纹长度测量和可能影响裂纹生长的相关外生因素在内的法国铁路网络的真实数据，该方法首先进行了预处理，以准备一致的学习数据集。然后，设计了适当的贝叶斯多视角循环架构来建模裂纹传播现象。得到的结果表明，多视角模型优于 LSTM 和 GRU 等最先进模型。