|Table of Contents|

Research Progress on Structural Damage Detection Based on Convolutional Neural Networks(PDF)

《建筑科学与工程学报》[ISSN:1673-2049/CN:61-1442/TU]

Issue:
2022年04期
Page:
38-57
Research Field:
Publishing date:

Info

Title:
Research Progress on Structural Damage Detection Based on Convolutional Neural Networks
Author(s):
YANG Shuo;  XU Qing-feng;  WANG Zhuo-lin
(Shanghai Key Laboratory of Engineering Structure Safety, Shanghai Research Institute of Building Sciences Co., Ltd., Shanghai 200032, China)
Keywords:
damage detection;  computer vision;  machine learning;  deep learning;  convolutional neural network
PACS:
TU375
DOI:
10.19815/j.jace.2022.02043
Abstract:
In order to systematically sort out the development and research status of the damage detection of engineering structures based on the convolutional neural networks(CNN), the method for structural damage detection were categorized, analyzed, and evaluated in the aspects of detection objects and applications in different types of structures. The basic architecture and assessing metrics of CNN were introduced, and the researches and applications of the CNN were reviewed. In terms of detection objects, for the classification, the location, and the segmentation of concrete structural damages, the methods based on different types of CNN for structural damage detection, namely the classification based, the regression based, and the pixel level image segmentation based methods, were described in details. The features of network architectures, the analysis procedures, the training methods, and the performance of damage detection were analyzed. In terms of the damage detection of different types of structures, the applications of CNN based methods in the damage detection for masonry structures, steel bridges, and wooden architectural heritages were also analyzed. Finally, the suggestions and expectations for CNN methods were proposed based on considering the advantages and disadvantages. The results show that the variety of training samples significantly influence the effects of the model for detection. The existing damage segmentation methods require numerous parameters and high computational cost. Using the data augmentation and transfer learning method can effectively avoid the over-fitting problems and improve the training effectiveness. The performance of such methods needs to be improved for the detection of small damages and different types of damages.

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Last Update: 2022-07-10