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Creators/Authors contains: "Bai, Y."

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  1. Free, publicly-accessible full text available September 1, 2024
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  5. Submitted, under review. 
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  6. Abstract. In this paper, two different convolutional neural networks (CNNs) are applied on images for automated structural damage detection (SDD) in earthquake damaged structures and cracking localization (e.g., detection of cracks, their widths and distributions) at various scales, such as pixel level, object level, and structural level. The proposed method has two main steps: 1) diagnosis, and 2) localization of cracking or other damage. At first a residual CNN with transfer learning is employed to classify the damage in the structures and structural components. This step performs damage detection using two public datasets. The second step uses another CNN with U-Net structure to locate the cracking on low resolution images. The implementations using public and self-collected datasets show promising performance for a problem that had remained a challenge in the structure engineering field for a long time and indicate that the proposed approach can perform detection and localization of structural damage with an acceptable accuracy. 
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  7. Vision-based structural health monitoring (SHM) has become an important approach to recognize and evaluate structural damage after natural disasters. Deep convolutional neural networks (CNNs) have recently attained a breakthrough in computer vision field, in particular for image classification task. In this article, we adopted deep residual neural network (ResNet) whose residual representations and shortcut connections mechanism has gained significant performance in various computer vision tasks. In addition, we applied transfer learning due to a relatively small number of training images. To test our approach, we used the dataset from the 2018 PEER Hub ImageNet Challenge distributed by Pacific Earthquake Engineering Research Center. This challenge proposed eight structural damage detection tasks: scene classification, damage check, spalling condition, material type, collapse check, component type, damage level and damage type which can be categorized as binary and multi-class (3 or 4 classes) classification problems. Our experiments with eight different tasks showed that reliable classification can be obtained for some tasks. Corresponding above eight tasks, classification accuracy varied from 63.1% to 99.4%. Our approach has attained third place for overall tasks in this challenge. Through the individual observation of training dataset, it is found that there are a large number of confusing images. Therefore, it is believed that the accuracy will be improved after making a precise training data. 
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