A systematic review of convolutional neural network-based structural condition assessment techniques

Sony, Sandeep; Dunphy, Kyle; Sadhu, Ayan; Capretz, Miriam A. M.

doi:10.1016/j.engstruct.2020.111347

Cited by 260 publications

(93 citation statements)

References 84 publications

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“…Recently, more research is being carried out in CNN for SHM systems. This is exhaustively covered in numerous review papers, 25,26,[32][33][34][35]41 as indicated before. Therefore, other deep NN algorithms are introduced here.…”

Section: Neural Network (Supervised/unsupervised)mentioning

confidence: 99%

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Malekloo

Özer

AlHamaydeh

et al. 2021

Structural Health Monitoring

270

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Conventional damage detection techniques are gradually being replaced by state-of-the-art smart monitoring and decision-making solutions. Near real-time and online damage assessment in structural health monitoring (SHM) systems is a promising transition toward bridging the gaps between the past’s applicative inefficiencies and the emerging technologies of the future. In the age of the smart city, Internet of Things (IoT), and big data analytics, the complex nature of data-driven civil infrastructures monitoring frameworks has not been fully matured. Machine learning (ML) algorithms are thus providing the necessary tools to augment the capabilities of SHM systems and provide intelligent solutions for the challenges of the past. This article aims to clarify and review the ML frontiers involved in modern SHM systems. A detailed analysis of the ML pipelines is provided, and the in-demand methods and algorithms are summarized in augmentative tables and figures. Connecting the ubiquitous sensing and big data processing of critical information in infrastructures through the IoT paradigm is the future of SHM systems. In line with these digital advancements, considering the next-generation SHM and ML combinations, recent breakthroughs in (1) mobile device-assisted, (2) unmanned aerial vehicles, (3) virtual/augmented reality, and (4) digital twins are discussed at length. Finally, the current and future challenges and open research issues in SHM-ML conjunction are examined. The roadmap of utilizing emerging technologies within ML-engaged SHM is still in its infancy; thus, the article offers an outlook on the future of monitoring systems in assessing civil infrastructure integrity.

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Section: Neural Network (Supervised/unsupervised)mentioning

confidence: 99%

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Malekloo

Özer

AlHamaydeh

et al. 2021

Structural Health Monitoring

270

View full text Add to dashboard Cite

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“…This method did not require calculating defect features [27]. Sony et al (2021) proposed a deep learning framework based on data fusion of CNN and Naive Bayes to detect cracked areas [28].…”

Section: Research Progress Of Weld Defect Detectionmentioning

confidence: 99%

Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed

Deng

Feng

et al. 2021

Symmetry

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Aiming at the problem of the poor robustness of existing methods to deal with diverse industrial weld image data, we collected a series of asymmetric laser weld images in the largest laser equipment workshop in Asia, and studied these data based on an industrial image processing algorithm and deep learning algorithm. The median filter was used to remove the noises in weld images. The image enhancement technique was adopted to increase the image contrast in different areas. The deep convolutional neural network (CNN) was employed for feature extraction; the activation function and the adaptive pooling approach were improved. Transfer Learning (TL) was introduced for defect detection and image classification on the dataset. Finally, a deep learning-based model was constructed for weld defect detection and image recognition. Specific instance datasets verified the model’s performance. The results demonstrate that this model can accurately identify weld defects and eliminate the complexity of manually extracting features, reaching a recognition accuracy of 98.75%. Hence, the reliability and automation of detection and recognition are improved significantly. The research results can provide a theoretical and practical reference for the defect detection of sheet metal laser welding and the development of the industrial laser manufacturing industry.

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“…Convolutional neural networks (CNNs) have achieved unprecedented results in image processing such as image classification [11], segmentation [12], and object detection [13], leading to broad applications in structural feature detection [14,15] and condition assessment [16,17]. In satellite-based assessments, CNNs have been used to classify hurricane-damaged roofs [18] and to estimate tree failure due to high winds [19,20].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Approaches for Tornado Damage Estimation with Unpiloted Aerial Systems

et al. 2021

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Tornado damage estimation is important for providing insights into tornado studies and assisting rapid disaster response. However, it is challenging to precisely estimate tornado damage because of the large volumes of perishable data. This study presents data-driven approaches to tornado damage estimation using imagery collected from Unpiloted Aerial Systems (UASs) following the 26 June 2018 Eureka Kansas tornado. High-resolution orthomosaics were generated from Structure from Motion (SfM). We applied deep neural networks (DNNs) on the orthomosaics to estimate tornado damage and assessed their performance in four scenarios: (1) object detection with binary categories, (2) object detection with multiple categories, (3) image classification with binary categories, and (4) image classification with multiple categories. Additionally, two types of tornado damage heatmaps were generated. By directly stitching the resulting image tiles from the DNN inference, we produced the first type of tornado damage heatmaps where damage estimates are accurately georeferenced. We also presented a Gaussian process (GP) regression model to build the second type of tornado damage heatmap (a spatially continuous tornado damage heatmap) by merging the first type of object detection and image classification heatmaps. The GP regression results were assessed with ground-truth annotations and National Weather Service (NWS) ground surveys. This detailed information can help NWS Weather Forecast Offices and emergency managers with their damage assessments and better inform disaster response and recovery.

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A systematic review of convolutional neural network-based structural condition assessment techniques

Cited by 260 publications

References 84 publications

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed

Data-Driven Approaches for Tornado Damage Estimation with Unpiloted Aerial Systems

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