Abstract The condition monitoring of concrete surface plays a significant role in civil infrastructure management system. Crack is the main threat to concrete surface of buildings, bridges, roads and pavements. This issue has been researched for several decades, however, it is still a challenge to classify crack since there are many inferior factors, e.g., intense inhomogeneity, structure complexity and background noise of concrete surface. In this paper, a novel deep-width network (DWN) architecture is used for binary and multi-label concrete surface crack classification without handcraft feature extraction. It intelligently learns cracking structures from input raw images by linear and nonlinear mapping process, flexible dynamically updates new weights and efficiently constructs the network by adding new incremental samples. The presented crack distress classification method is tested on two concrete surface crack image datasets and compared with many popular classification methods like sparse autoencoder (SAE), convolution neural network (CNN), and broad learn system (BLS). Experimental results demonstrate that it obviously outperforms those methods both in accuracy and efficiency.

中文翻译：

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使用新型深宽网络架构从混凝土表面图像中自动进行裂缝灾害分类

摘要 混凝土表面状态监测在民用基础设施管理系统中占有重要地位。裂缝是建筑物、桥梁、道路和人行道混凝土表面的主要威胁。这个问题已经研究了几十年，然而，由于存在许多次要因素，例如混凝土表面的强烈不均匀性、结构复杂性和背景噪声，裂缝分类仍然是一个挑战。在本文中，一种新型的深宽网络（DWN）架构用于二元和多标签混凝土表面裂缝分类，无需手工特征提取。它通过线性和非线性映射过程智能地从输入的原始图像中学习破解结构，灵活地动态更新新的权重，并通过添加新的增量样本有效地构建网络。在两个混凝土表面裂纹图像数据集上测试了所提出的裂纹损伤分类方法，并与稀疏自编码器 (SAE)、卷积神经网络 (CNN) 和广泛学习系统 (BLS) 等许多流行的分类方法进行了比较。实验结果表明，它在准确性和效率上都明显优于那些方法。