An improved bidirectional generative adversarial network model for multivariate estimation of correlated and imbalanced tunnel construction parameters

Abstract

Estimation of construction parameters is crucial for optimizing tunnel construction schedule. Due to the influence of routine activities and occasional risk events, these parameters are usually correlated and imbalanced. To solve this issue, an improved bidirectional generative adversarial network (BiGAN) model with a joint discriminator structure and zero-centered gradient penalty (0-GP) is proposed. In this model, in order to improve the capability of original BiGAN in learning imbalanced parameters, the joint discriminator separately discriminates the routine activities and risk event durations to balance their influence weights. Then, the self-attention mechanism is embedded so that the discriminator can pay more attention to the imbalanced parameters. Finally, the 0-GP is adapted for the loss of the discriminator to improve its convergence and stability. A case study of a tunnel in China shows that the improved BiGAN can obtain parameter estimates consistent with the classical Gauss mixture model, without the need of tedious and complex correlation analysis. The proposed joint discriminator can increase the ability of BiGAN in estimating imbalanced construction parameters, and the 0-GP can ensure the stability and convergence of the model.

Keywords

• Multivariate parameters estimation
• Correlated and imbalanced parameters
• Bidirectional generative adversarial network (BiGAN)
• Joint discriminator
• Zero-centered gradient penalty (0-GP)