Pal Ashish, Meng Wei, Nagarajaiah Satish
Department of Civil and Environmental Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA.
Department of Mechanical Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA.
Sensors (Basel). 2023 Aug 26;23(17):7445. doi: 10.3390/s23177445.
Structures in their service life are often damaged as a result of aging or extreme events such as earthquakes or storms. It is essential to detect damage in a timely fashion to ensure the safe operation of the structure. If left unchecked, subsurface damage (SSD) can cause significant internal damage and may result in premature structural failure. In this study, a Convolutional Neural Network (CNN) has been developed for SSD detection using surface strain measurements. The adopted network architecture is capable of pixel-level image segmentation, that is, it classifies each location of strain measurement as damaged or undamaged. The CNN which is fed full-field strain measurements as an input image of size 256 × 256 projects the SSD onto an output image of the same size. The data for network training is generated by numerical simulation of aluminum bars with different damage scenarios, including single damage and double damage cases at a random location, direction, length, and thickness. The trained network achieves an Intersection over Union (IoU) score of 0.790 for the validation set and 0.794 for the testing set. To check the applicability of the trained network on materials other than aluminum, testing is performed on a numerically generated steel dataset. The IoU score is 0.793, the same as the aluminum dataset, affirming the network's capability to apply to materials exhibiting a similar stress-strain relationship. To check the generalization potential of the network, it is tested on triple damage cases; the IoU score is found to be 0.764, suggesting that the network works well for unseen damage patterns as well. The network was also found to provide accurate predictions for real experimental data obtained from Strain Sensing Smart Skin (S). This proves the efficacy of the network to work in real-life scenarios utilizing the full potential of the novel full-field strain sensing methods such as S. The performance of the proposed network affirms that it can be used as a non-destructive testing method for subsurface crack detection and localization.
结构在其使用寿命期间，常常会因老化或地震、风暴等极端事件而受损。及时检测到损伤对于确保结构的安全运行至关重要。如果不加以检查，地下损伤（SSD）可能会导致严重的内部损伤，并可能导致结构过早失效。在本研究中，开发了一种卷积神经网络（CNN），用于利用表面应变测量检测SSD。所采用的网络架构能够进行像素级图像分割，也就是说，它将应变测量的每个位置分类为受损或未受损。将全场应变测量作为大小为256×256的输入图像输入的CNN，将SSD投影到相同大小的输出图像上。网络训练数据通过对具有不同损伤情况的铝棒进行数值模拟生成，包括在随机位置、方向、长度和厚度处的单损伤和双损伤情况。训练后的网络在验证集上的交并比（IoU）分数为0.790，在测试集上为0.794。为了检验训练后的网络对铝以外材料的适用性，对数值生成的钢数据集进行了测试。IoU分数为0.793，与铝数据集相同，证实了该网络适用于表现出相似应力-应变关系的材料。为了检验网络的泛化潜力，对三重损伤情况进行了测试；发现IoU分数为0.764，这表明该网络对未见过的损伤模式也能很好地工作。还发现该网络能对从应变传感智能蒙皮（S）获得的实际实验数据提供准确预测。这证明了该网络利用S等新型全场应变传感方法的全部潜力在实际场景中工作的有效性。所提出网络的性能证实了它可作为一种用于地下裂纹检测和定位的无损检测方法。
