Coarse-grained soils are fundamental to major infrastructures like embankments, roads, and bridges. Understanding their deformation characteristics is essential for ensuring structural stability. Traditional methods, such as triaxial compression tests and numerical simulations, face challenges like high costs, time consumption, and limited generalizability across different soils and conditions. To address these limitations, this study employs deep learning to predict the volumetric strain of coarse-grained soils as axial strain changes, aiming to obtain the axial strain (epsilon(a))-volumetric strain (epsilon(v)) curve, which helps derive key mechanical parameters like cohesion (c), and elastic modulus (E). However, the limited data from triaxial tests poses challenges for training deep learning models. We propose using a Time-series Generative Adversarial Network (TimeGAN) for data augmentation. Additionally, we apply feature importance analysis to assess the quality of the numerical augmented data, providing feedback for improving the TimeGAN model. To further enhance model performance, we introduce the pre-training strategy to reduce bias between augmented and real data. Experimental results demonstrate that our approach effectively predicts epsilon(a)-epsilon(v) curve, with the mean absolute error (MAE) of 0.2219 and the R-2 of 0.9155. The analysis aligns with established findings in soil mechanics, underscoring the potential of our method in engineering applications.

This study is focused on optimizing electromagnetic acoustic transducer (EMAT) sensors for enhanced ultrasonic guided wave signal generation in steel cables using CAD and modern manufacturing to enable contactless ultrasonic signal transmission and reception. A lab test rig with advanced measurement and data processing was set up to test the sensors' ability to detect cable damage, like wire breaks and abrasion, while also examining the effect of potential disruptors such as rope soiling. Machine learning algorithms were applied to improve the damage detection accuracy, leading to significant advancements in magnetostrictive measurement methods and providing a new standard for future development in this area. The use of the Vision Transformer Masked Autoencoder Architecture (ViTMAE) and generative pre-training has shown that reliable damage detection is possible despite the considerable signal fluctuations caused by rope movement.