Deep learning‐based structural damage detection methods overcome the limitation of inferior adaptability caused by extensively varying real‐world situations (e.g., lighting and shadow changes). However, most deep learning‐based methods detect structural damage at the image level and grid‐cell level. To provide pixel‐level detection of multiple damages, a Fully Convolutional Network (FCN)‐based multiple damages detection method for concrete structure is proposed. To realize this method, a database of 2,750 images (with 504 × 376 pixels) including crack, spalling, efflorescence, and hole images in concrete structure is built, and the four damages included in those images are labeled manually. Then, the architecture of the FCN is modified, trained, validated, and tested using this database. A strategy of model‐based transfer learning is used to initialize the parameters of the FCN during the training process. The results show 98.61% pixel accuracy (PA), 91.59% mean pixel accuracy (MPA), 84.53% mean intersection over union (MIoU), and 97.34% frequency weighted intersection over union (FWIoU). Subsequently, the robustness and adaptability of the trained FCN model is tested and the damage is extracted, where damage areas are provided according to a calibrated relation between the ratio (the pixel area and true area of the detected object) and the distance from the smartphone to the concrete surface using a laser range finder. A comparative study is conducted to examine the performance of the proposed FCN‐based approach using a SegNet‐based method. The results show that the proposed method substantiates quite better performance and can indeed detect multiple concrete damages at the pixel level in realistic situations.

Crack detection is important for the inspection and evaluation during the maintenance of concrete structures. However, conventional image-based methods need extract crack features using complex image preprocessing techniques, so it can lead to challenges when concrete surface contains various types of noise due to extensively varying real-world situations such as thin cracks, rough surface, shadows, etc. To overcome these challenges, this paper proposes an image-based crack detection method using a deep convolutional neural network (CNN). A CNN is designed through modifying AlexNet and then trained and validated using a built database with 60000 images. Through comparing validation accuracy under different base learning rates, 0.01 was chosen as the best base learning rate with the highest validation accuracy of 99.06%, and its training result is used in the following testing process. The robustness and adaptability of the trained CNN are tested on 205 images with 3120 × 4160 pixel resolutions which were not used for training and validation. The trained CNN is integrated into a smartphone application to mobile more public to detect cracks in practice. The results confirm that the proposed method can indeed detect cracks in images from real concrete surfaces.

Uniform mesoporous H-Nb2O5/rGO nanocomposites are developed for advanced lithium ion hybrid supercapacitors with remarkably high energy/power densities and excellent cycling stability.

Manual inspection (i.e., visual inspection and/or with professional equipment) is the most predominant approach for identifying and assessing superficial damage of masonry historic structures at present. However, this method is costly and at times difficult to apply to remote structures or components. Existing convolutional neural network (CNN)‐based damage detection methods have not been specifically designed for the multiple damage identification of masonry historic structures. To overcome these limits, a deep architecture of CNN damage classification techniques for masonry historic structures is proposed in this article using a sliding window‐based CNN method to identify and locate four categories of damage (intact, crack, efflorescence, and spall) with an accuracy of 94.3%. This is the first attempt to identify the multidamage of historic masonry structures based on CNN techniques and achieve excellent classification results. The data are only trained and tested from images of the Forbidden City Wall in China, and the pixel resolutions of stretcher brick images and header brick images are 480 × 105 and 210 × 105, respectively. Two CNNs (AlexNet and GoogLeNet) are both trained on a small dataset (2,000 images for training, 400 images for validation and testing) and a large dataset (20,000 images for training, 4,000 images for validation and testing). The performance of the trained model (94.3% accuracy) is examined on five new images with 1,860 × 1,260 pixel resolutions.

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The detection and measurement of crack at pixel level is a challenge to existing methods. To overcome this challenge, this paper proposes a convolutional encoder-decoder network (CedNet) to detect crack from image, and the maximum widths and orientations of cracks are measured using image postprocessing techniques. To realize this, a database including 1800 crack images (with 761×569 pixel resolution) taken from concrete structures is built. Then the CedNet is designed, trained and validated using the built database. The validating results show 98.90% accuracy, 93.58% precision, 94.73% recall, 93.18% F-measure, 87.23% intersection over union (IoU) of crack and 98.82% IoU of background. Subsequently, the robustness and adaptability of the trained model is tested. To measure true maximum widths and orientations of cracks, a laboratory experiment is carried out to calibrate a relation between ratio (pixel distance / real distance) and field of view (camera's view range on concrete surface included in image) and distance from the smartphone to concrete surface. In the post-processing techniques, the perspective transformation is employed to correct distorted images caused by the existence of the oblique angles between the smartphone and concrete surfaces. Then the maximum widths and orientations of cracks in predicted results are measured respectively using the Euclidean distance transformation and least squares principle. As comparison, two existing deep learning-based crack detection and measurement method are used to examine the performance of the proposed approach. The comparison results show that the proposed method substantiates quite good performance to detect cracks and measure maximum widths and orientations of cracks in our database.

To meet the increasing demands for high-performance energy storage devices, an advanced lithium-ion hybrid capacitor (LIHC) has been designed and fabricated, which delivers an ultrahigh energy density of 295.1 Wh kg −1 and a power density of 41 250 W kg −1 with superior cycling stability. The high-performance LIHC device is based on the uniform porous Nb 4 N 5 /rGO nanocomposite, which has an intimate interface between the firmly contacted Nb 4 N 5 and rGO through the Nb(Nb 4 N 5 )−O(rGO)−C(rGO) bonds, significantly improving the electron transport kinetics. Moreover, the introduction of rGO nanosheets can prevent the Nb 4 N 5 nanoparticles from agglomeration, not only resulting in a larger specific surface area to provide more active sites but also accommodating the strain during Li ion insertion/deinsertion. Therefore, the Nb 4 N 5 /rGO nanocomposite exhibits a higher reversible specific capacity and better rate and cycling performance than the Nb 4 N 5 nanoparticle. In view of the scalable preparation and superior electrochemical characteristics, the Nb 4 N 5 /rGO nanocomposite would have great potential practical applications in the future energy storage devices.

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