Department of Civil and Environmental Engineering, Western University, London, ON N6A 3K7, Canada.
Department of Electrical and Computer Engineering, Western University, London, ON N6A 3K7, Canada.
Sensors (Basel). 2022 Aug 18;22(16):6193. doi: 10.3390/s22166193.
The deterioration of infrastructure's health has become more predominant on a global scale during the 21st century. Aging infrastructure as well as those structures damaged by natural disasters have prompted the research community to improve state-of-the-art methodologies for conducting Structural Health Monitoring (SHM). The necessity for efficient SHM arises from the hazards damaged infrastructure imposes, often resulting in structural collapse, leading to economic loss and human fatalities. Furthermore, day-to-day operations in these affected areas are limited until an inspection is performed to assess the level of damage experienced by the structure and the required rehabilitation determined. However, human-based inspections are often labor-intensive, inefficient, subjective, and restricted to accessible site locations, which ultimately negatively impact our ability to collect large amounts of data from inspection sites. Though Deep-Learning (DL) methods have been heavily explored in the past decade to rectify the limitations of traditional methods and automate structural inspection, data scarcity continues to remain prevalent within the field of SHM. The absence of sufficiently large, balanced, and generalized databases to train DL-based models often results in inaccurate and biased damage predictions. Recently, Generative Adversarial Networks (GANs) have received attention from the SHM community as a data augmentation tool by which a training dataset can be expanded to improve the damage classification. However, there are no existing studies within the SHM field which investigate the performance of DL-based multiclass damage identification using synthetic data generated from GANs. Therefore, this paper investigates the performance of a convolutional neural network architecture using synthetic images generated from a GAN for multiclass damage detection of concrete surfaces. Through this study, it was determined the average classification performance of the proposed CNN on hybrid datasets decreased by 10.6% and 7.4% for validation and testing datasets when compared to the same model trained entirely on real samples. Moreover, each model's performance decreased on average by 1.6% when comparing a singular model trained with real samples and the same model trained with both real and synthetic samples for a given training configuration. The correlation between classification accuracy and the amount and diversity of synthetic data used for data augmentation is quantified and the effect of using limited data to train existing GAN architectures is investigated. It was observed that the diversity of the samples decreases and correlation increases with the increase in the number of synthetic samples.
基础设施健康状况的恶化在 21 世纪在全球范围内变得更加突出。随着基础设施的老化以及自然灾害造成的结构损坏，促使研究界改进用于进行结构健康监测 (SHM) 的最先进方法。对高效 SHM 的需求源于受损基础设施带来的危害，这些危害常常导致结构倒塌，造成经济损失和人员伤亡。此外，在对这些受影响地区进行日常运营之前，必须进行检查以评估结构所经历的损坏程度以及确定所需的修复。然而，基于人工的检查通常劳动强度大、效率低、主观性强且仅限于可访问的现场位置，这最终会影响我们从检查现场收集大量数据的能力。尽管深度学习 (DL) 方法在过去十年中得到了广泛的探索，以纠正传统方法的局限性并实现结构检查的自动化，但数据匮乏在 SHM 领域仍然很普遍。缺乏足够大、平衡和通用的数据库来训练基于 DL 的模型，这常常导致不准确和有偏差的损坏预测。最近，生成对抗网络 (GAN) 受到 SHM 社区的关注，成为一种数据扩充工具，通过该工具可以扩展训练数据集以改善损伤分类。然而，在 SHM 领域，没有现有的研究调查使用 GAN 生成的合成数据进行基于 DL 的多类损伤识别的性能。因此，本文研究了使用 GAN 生成的合成图像的卷积神经网络架构在混凝土表面的多类损伤检测中的性能。通过这项研究，当与在完全真实样本上训练的相同模型相比时，确定了所提出的 CNN 在混合数据集上的平均分类性能下降了 10.6%和 7.4%，用于验证数据集和测试数据集。此外，当比较在给定训练配置下仅使用真实样本训练的单个模型和使用真实样本和合成样本训练的相同模型时，每个模型的性能平均下降了 1.6%。量化了分类准确性与用于数据扩充的合成数据的数量和多样性之间的相关性，并研究了使用有限数据训练现有 GAN 架构的效果。观察到随着合成样本数量的增加，样本的多样性减小并且相关性增加。
