A new deep learning-based approach for concrete crack identification and damage assessment

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Fuyan Guo, Qi Cui, Hongwei Zhang, Yue Wang, Huidong Zhang, Xinqun Zhu, Jiao Chen
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引用次数: 0

Abstract

Concrete building structures are prone to cracking as they are subjected to environmental temperatures, freeze-thaw cycles, and other operational environmental factors. Failure to detect cracks in the key building structure at the early stage can result in serious accidents and associated economic losses. A new method using the SE-U-Net model based on a conditional generative adversarial network (CGAN) has been developed to identify small cracks in concrete structures in this paper. This proposed method was a pixel-level U-Net model based on a generative network, that was integrated the original convolutional layer with an attention mechanism, and an SE module in the jump connection section was added to improve the identifiability of the model. The discriminative network compared the generated images with real images using the PatchGAN model. Through the adversarial training of generator and discriminator, the performance of generator in crack image segmentation task is improved, and the trained generation network is used to segment cracks. In damage assessments, the crack skeleton was represented by the individual pixel width and recognized using the binary morphological crack skeleton method, in which the final length, area, and average width of the crack could be determined through the geometric correction index. The results showed that compared with other methods, the proposed method could better identify subtle pixel-level cracks, and the identification accuracy is 98.48%. These methods are of great significance for the identification of cracks and the damage assessment of concrete structures in practice.
基于深度学习的混凝土裂缝识别和损伤评估新方法
混凝土建筑结构在受到环境温度、冻融循环和其他运行环境因素的影响时,很容易出现裂缝。如果不能在早期阶段检测到关键建筑结构的裂缝,就会导致严重的事故和相关的经济损失。本文开发了一种基于条件生成对抗网络(CGAN)的 SE-U-Net 模型新方法,用于识别混凝土结构中的细小裂缝。该方法是一种基于生成网络的像素级 U-Net 模型,它将原有的卷积层与注意力机制整合在一起,并在跳转连接部分添加了 SE 模块,以提高模型的可识别性。判别网络使用 PatchGAN 模型将生成的图像与真实图像进行比较。通过对生成器和判别器进行对抗训练,提高了生成器在裂纹图像分割任务中的性能,并将训练后的生成网络用于裂纹分割。在损伤评估中,裂纹骨架由单个像素宽度表示,并使用二元形态学裂纹骨架方法进行识别,其中通过几何校正指数可确定裂纹的最终长度、面积和平均宽度。结果表明,与其他方法相比,所提出的方法能更好地识别细微的像素级裂缝,识别准确率达到 98.48%。这些方法对混凝土结构的裂缝识别和损伤评估具有重要的实际意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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