Augmentation of 3D virtual aggregate database using deep convolutional Wasserstein generative adversarial networks

作者：Dong Feng, Jinchao Guan, Chaoliang Fu, Yuanyuan Hu, Frédéric Otto, Alvaro García Hernandez, Pengfei Liu · 发表于：Advanced Engineering Informatics · 年份：2025 · DOI：10.1016/j.aei.2025.103994 · 被引用次数：18 · 研究领域：Generative Adversarial Networks and Image Synthesis、Anomaly Detection Techniques and Applications、Image Processing and 3D Reconstruction

• An original 3D virtual aggregate database was established using stereo vision. • Six DC-WGANs were used to augment the original 3D virtual aggregate database. • A multi-criteria evaluation approach was proposed to assess the augmented database. • The feasibility of applying lightweight processing to the database was studied. The generalization capability of asphalt mixture numerical models incorporating virtual aggregates is often constrained by the limited morphological diversity of the virtual aggregate database, which reduces the ability of the model to reliably predict the mechanical response of asphalt pavements. To address this, a novel framework based on deep convolutional Wasserstein generative adversarial networks (DC-WGANs) is proposed in this study to augment the 3D virtual aggregate database. The original 3D virtual aggregate database was created using stereo vision and multi-view matching techniques. Six DC-WGAN variants were subsequently employed for data augmentation, and a multi-criteria evaluation approach was introduced to comprehensively assess both model performance and the augmented databases. In addition, aggregate packing and Superpave Gyratory Compaction (SGC) simulations were performed to investigate the effect of data augmentation on the prediction of mechanical response. Finally, the feasibility of applying lightweight processing to the database augmented by the best-performing DC-WGAN variant was investigated. Results show that the DC-WGAN-div mo...