In recent years, applying deep learning (DL) to assess structural damages has gained growing popularity in vision‐based structural health monitoring (SHM). However, both data deficiency and class imbalance hinder the wide adoption of DL in practical applications of SHM. Common mitigation strategies include transfer learning, oversampling, and undersampling, yet these ad hoc methods only provide limited performance boost that varies from one case to another. In this work, we introduce one variant of the generative adversarial network (GAN), named the balanced semisupervised GAN (BSS‐GAN). It adopts the semisupervised learning concept and applies balanced‐batch sampling in training to resolve low‐data and imbalanced‐class problems. A series of computer experiments on concrete cracking and spalling classification were conducted under the low‐data imbalanced‐class regime with limited computing power. The results show that the BSS‐GAN is able to achieve better damage detection in terms of recall and Fβ score than other conventional methods, indicating its state‐of‐the‐art performance.
In bridge health monitoring (BHM), crack identification and width measurement are two of the most important indices for evaluating the functionality of bridges. In order to reduce the labor cost in field detection, researchers have proposed a variety of deep learning (DL)-based detection techniques for crack recognition. However, some problems still exist in extending these techniques to practical applications, such as data annotation difficulty, limited model generalization ability, and inaccuracy of the DL identification of the actual crack width measurement. In this paper, an application-oriented multistage crack recognition framework is proposed, namely, Convolutional Active Learning Identification-Segmentation-Measurement (CAL-ISM). It includes four steps:(1) pretraining of the benchmark classification model, (2) retraining of the semisupervised active learning model, (3) pixel-level crack segmentation, and (4) crack width measurement. Beyond numerical experiments, the performance of the CAL-ISM is validated for practical applications: (i) bridge column test specimen and (ii) field BHM projects. In conclusion, the obtained results from these applications shed light on the high potential of CAL-ISM for BHM applications, which is recommended in future deployments for BHM. BACKGROUND AND MOTIVATIONSBy the end of 2020, there are 912,800 highway bridges in China, including 6444 long-span bridges and 119,935 normal bridges. The total mileage of the highway is about 5.19 million kilometers (km), of which the maintenance mileage of the highway is 5.14 million km, accounting for 99.0% of the total mileage (Ministry of transport of the People's Republic of China, 2020). The remaining 1% is for newly constructed highways with no need for immediate © 2022 Computer-Aided Civil and Infrastructure Engineering. maintenance. Similarly, there are more than 618,000 bridges in the United States, where nearly 36% of the bridges need repair work, and 7.3% of them are considered structurally deficient (American Road and Transportation Builders Association, 2021). Therefore, surging demands in the maintenance work of highway bridges in China, the United States, and many other countries are worldwide realities and it is a necessity to develop effective methods to evaluate the service functionalities of bridge structures. Because of cost-efficiency and easy-forming, reinforced
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.