Forecasting infrastructure deterioration with inverse GANs

Eric Bianchi; Matthew Hebdon

doi:10.1117/12.2595111

1 August 2021 Forecasting infrastructure deterioration with inverse GANs

Eric Bianchi, Matthew Hebdon

Proceedings Volume 11843, Applications of Machine Learning 2021; 1184304 (2021) https://doi.org/10.1117/12.2595111
Event: SPIE Optical Engineering + Applications, 2021, San Diego, California, United States

Abstract

Synthetic image generation using deep learning techniques like generative adversarial networks (GANS), has drastically improved since its inception. There has been significant research using human face datasets like FFHQ, and city-semantic datasets for self-driving car applications. Utilizing latent space distributions, researchers have been able to generate or edit images to exhibit specific traits in the resultant images, like face-aging. However, there has been little GAN research and datasets in the structural infrastructure domain. We propose an inverse-GAN application to embed real structural bridge detail images and incrementally edit them using learned semantic boundaries. Corrosion/non-corrosion and various steel paint colors were among the learned semantic boundaries discovered using the Interface-GAN methodology. The novel dataset used was procured from extracting hundreds of thousands of images from the Virginia Department of Transportation (VDOT) bridge inspection reports and was trained using the styleGAN2 generator. The trained model offers the ability to forecast deterioration incrementally, which is valuable to inspectors, engineers, and owners because it gives a window into the future on how and where damage may progress. As bridge inspectors typically review bridges every two years, this forecast could reinforce decisions for action or in-action.

Conference Presentation

Citation Download Citation

Eric Bianchi and Matthew Hebdon "Forecasting infrastructure deterioration with inverse GANs", Proc. SPIE 11843, Applications of Machine Learning 2021, 1184304 (1 August 2021); https://doi.org/10.1117/12.2595111

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