Deep learning is revolutionising the way that many industries operate, providing a powerful method to interpret large quantities of data automatically and relatively quickly. Deterioration is often multi-factorial and difficult to model deterministically due to limits in measurability, or unknown variables. Deploying deep learning tools to the field of materials degradation should be a natural fit. In this paper, we review the current research into deep learning for detection, modelling and planning for material deterioration. Driving such research are factors such as budget reductions, increasing safety and increasing detection reliability. Based on the available literature, researchers are making headway, but several challenges remain, not least of which is the development of large training data sets and the computational intensity of many of these deep learning models.

深度学习正在彻底改变许多行业的运作方式，提供了一种强大的方法来自动且相对快速地解释大量数据。恶化通常是多因素的，并且由于可测量性的限制或未知变量而难以确定性地建模。将深度学习工具部署到材料降解领域应该是很自然的选择。在本文中，我们回顾了有关深度学习的最新研究，以检测，建模和规划材料劣化。推动此类研究的因素包括预算减少，安全性提高和检测可靠性提高。根据现有文献，研究人员正在取得进展，但仍然存在一些挑战，