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This work presents a balanced semisupervised GAN (BSS-GAN) that combines semi-supervised learning with balanced-batch sampling to address low-data imbalanced-class regimes. Experiments on concrete cracking and spalling classification show improved recall and F-score compared with conventional methods, demonstrating state-of-the-art performance.",{"@graph":14,"@context":71},[15,34,54],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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computer experiments on concrete cracking and spalling classification under low-data imbalanced-class conditions, achieving better recall and F-score than conventional methods.","https://schema.org",{"og:url":32,"og:type":73,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":75,"canonical":32},"index,follow",{"doc_id":77,"site_id":7},647581,1792789347,{"code":4,"msg":80,"data":81},"success",[82,86,90,94,99,104,109,113,118,121,125],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":83,"show_sort_weight":84,"slug":85},"Story & 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\nORIGINAL ARTICLE  \nBalanced Semi-Supervised Generative Adversarial Network for Damage Assessment from Low-Data Imbalanced-Class Regime  \nYuqing Gao 1,2 | Pengyuan Zhai 1,3 | Khalid M. Mosalam* 1,2,4  \n1Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA  \n2Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University, Shenzhen, China  \n3Department of Industrial Engineering and Operations Research, University of California, Berkeley, CA, USA  \n4Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, USA  \n*Correspondence  \nKhalid M. Mosalam, 723 Davis Hall, University of California, Berkeley, CA 94720-1710, USA. Email:  \n[mosalam@berkeley.edu](mosalam@berkeley.edu)  \nSummary  \nIn 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, over-sampling, and under-sampling, 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 semi-supervised learning concept and applies balanced-batch sampling in training to resolve low-data and imbalancedclass 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 F􀀌 score than other conventional methods, indicating its state-of-the-art performance.  \nKEYWORDS:  \nGAN, Imbalanced-class, Low-data, Semi-supervised learning, Structural damage assessment.  \n1  BACKGROUND & MOTIVATIONS  \nNowadays, machine learning (ML) and deep learning (DL) have been at the forefront of a plethora of research activities in many disciplines. Since 2017, there has been an increasing number of DL studies and applications in civil and structural engineering (Amezquita-Sanchez, Valtierra-Rodriguez,& Adeli, 2018; Gao & Mosalam, 2018; Mosalam, Muin,& Gao, 2019; Oh, Kim, Kim, Park, & Adeli, 2017; PerezRamirez et al., 2019; Rafiei & Adeli, 2017b; Yeum, Dyke, & Ramirez, 2018) . Particularly, for vision-based structural health monitoring (SHM), the convolutional neural network (CNN) is proven to be a promising approach with high practical potential (Cha, Choi, & Büyüköztürk, 2017; Deng, Lu, & Lee, 2020; Gao, Li, Mosalam, & Günay, 2018; Gao & Mosalam,  \n2018; Jiang & Zhang, 2019; Liang, 2019; Maeda, Sekimoto, & Seto, 2016; A. Zhang et al., 2017; C. Zhang, Chang,& Jamshidi, 2020) . However, the frameworks in past studies are facing three real-world challenges: (1) insufficient data,(2) imbalanced classes, and (3) high computing demands when computing power is limited.  \nMost of the current DL applications in vision-based SHM fall into the supervised learning category, e.g., image classification (Gao & Mosalam, 2018), damage localization (Cha, Choi, Suh, Mahmoudkhani, & Büyüköztürk, 2018; Xue & Li, 2018) and segmentation (Yang et al., 2018), which requires substantial amounts of labeled data to reach the desired performance level. Obtaining large-scale labeled structural image datasets is costly and labor-intensive. Compared with popular computer vision (CV) benchmark datasets such as ImageNet (14 million images) (Deng et al., 2009), MNIST (70,000 images) (LeCun, Bottou, Bengio, & Haffner, 1998) and CIFAR-10 (70,000 images) (Krizhevsky, 2009), current  \nvision-based SHM datasets, e.g., PEER Hub ImageNet (􀀞 -Net) with 36,413 images (Gao & Mosalam, 2020), are much s","cbCaioEWY9k4T8cu","https://ap.wps.com/l/cbCaioEWY9k4T8cu","pdf",4874407,"English","# Background & Motivations","[{\"question\":\"What problems does the paper target in vision-based structural health monitoring?\",\"answer\":\"It targets insufficient labeled data, class imbalance between damaged and undamaged states, and high computing demands when GPU resources are limited.\"},{\"question\":\"What is the main idea behind BSS-GAN?\",\"answer\":\"BSS-GAN adopts semi-supervised learning and introduces balanced-batch sampling during training to mitigate low-data and imbalanced-class issues.\"},{\"question\":\"How was the approach evaluated and what were the results?\",\"answer\":\"The method was tested via computer experiments on concrete cracking and spalling classification under low-data imbalanced-class conditions, achieving better recall and F-score than conventional methods.\"}]","Balanced Semi-Supervised Generative Adversarial Network for Damage Assessment from Low-Data Imbalanced-Class Regime | PDF"]