Crack is the earlier indication of concrete structural severe damage; it plays an important role in structure health monitoring (SHM) of industrial civil infrastructures (such as buildings, bridges, roads, dams, etc.). Crack damage classification is the first and critical stage for concrete SHM. However, commonly used human visual classification is costly, labor-intensive, and unreliable, other machine learning based classification methods also have some drawbacks. To address these problems, this article proposes a cascade broad neural network architecture for concrete surface structural crack damage automated classification, which generates an effective and efficient framework with much less hyper-parameters than deep neural networks, and sufficiently explores the advantages of multilevel cascades of classifier ensemble. Experimental results on four challenging datasets demonstrate that its performance is quite more excellent than current mainstream classification methods (both in testing accuracy and training time).

中文翻译：

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用于混凝土结构裂纹损伤自动分类的级联广义神经网络。

裂缝是混凝土结构严重损坏的早期迹象。它在工业民用基础设施（例如建筑物，桥梁，道路，水坝等）的结构健康监测（SHM）中起着重要作用。裂缝损伤分类是混凝土SHM的第一个也是关键的阶段。但是，常用的人类视觉分类成本高，劳动强度大且不可靠，其他基于机器学习的分类方法也有一些缺点。为了解决这些问题，本文提出了一种用于混凝土表面结构裂缝损伤自动分类的级联广义神经网络体系结构，该体系生成了一种有效且高效的框架，其超参数比深层神经网络少得多，并且充分探索了多级级联神经网络的优势。分类器合奏。