InfoGAN-MSF: a data augmentation approach for correlative bridge monitoring factors. (9th August 2021)

• Record Type: Journal Article
• Authors: Wan, Ping; He, Hongli; Guo, Ling; Yang, Jiancheng; Li, Jie
• Is Part Of: Measurement science & technology. Volume 32:Number 11(2021)
• Publication Date: 2021-08-09
• Subjects: generative adversarial nets (GAN) -- operation safety evaluation -- bridge factors -- data augmentation -- correlative parameters; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ac0744
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: Bridge health evaluation has been a challenging issue due to high assessment errors with imbalanced and insufficient monitoring data of correlative bridge monitoring factors. We propose a data augmentation model as the preprocessing of bridge health evaluation to expand existing monitoring data of bridge monitoring factors on the basis of generative adversarial nets (GAN), named as information-GAN (InfoGAN)-multi-scale-filtering. In this model, new data of bridge monitoring factors are produced by the InfoGAN-based model with regards to learning coupling relations among bridge monitoring factors. To resolve generalization issues from the parameter matrix in traditional InfoGAN, we improve the discriminator with spectral normalization to optimize the weight training process. To deal with the instability of InfoGAN performance, which creates defective samples, a multi-scale filtering scheme is designed to obtain effective samples from the InfoGAN-based model. The scheme picks credible samples from both quantitative and qualitative aspects with the multiple scale filtering procedure. Additionally, inherent properties of bridge monitoring factors (e.g. distributions) are discovered within the generation process. Finally, filtered data are mixed into raw monitoring data to train classifiers. Simulation results imply that the proposed model performs effectively in data generation of real-world bridge monitoring factors and improves the performance of bridge health evaluation.