Shuozhi Wang

Reusable launch vehicles (RLVs) undergo severe thermal cycling, and cryogenic storage conditions introduce strong, nonlinear distortions in guided ultrasonic wave (GUW) signals used for structural health monitoring (SHM). These temperature-induced effects obscure damage-sensitive features and limit the robustness of both conventional compensation techniques and purely data-driven learning models.This paper proposes a physics-informed machine learning framework, termed the Guided Wave Physics-Informed Generative Adversarial Network (GW-PIGAN), to compensate cryogenic GUW distortions in a physically consistent manner. The model adopts a residual generative formulation in the Hilbert-envelope domain and integrates adversarial sequence learning with wave-physics priors through curvature-based regularization and peak-morphology constraints. These physics-informed components explicitly suppress nonphysical temporal artifacts and preserve the modal structure of guided-wave responses, enabling stable compensation beyond the training temperature domain.Experiments conducted on an aluminum-alloy plate under liquid-nitrogen cooling–heating cycles at 330\,kHz demonstrate that GW-PIGAN achieves high-fidelity reconstruction ($R^2 = 0.9552$) while maintaining robust performance across varying reference temperatures. Compared with purely data-driven baselines, the proposed physics-informed formulation exhibits improved stability, physical plausibility, and generalization under extreme thermal conditions.By embedding guided-wave physics into generative learning, GW-PIGAN provides a practical and interpretable PIML solution for temperature compensation in cryogenic SHM, supporting real-time monitoring of reusable aerospace structures under coupled environmental and structural variability.

Carbon emissions present a pressing challenge to the traditional construction industry, urging a fundamental shift towards more sustainable practices and materials. Recent advances in sensors, data fusion techniques, and artificial intelligence have enabled integrated digital technologies (e.g., digital twins) as a promising trend to achieve emission reduction and net-zero. While digital twins in the construction sector have shown rapid growth in recent years, most applications focus on the improvement of productivity, safety and management. There is a lack of critical review and discussion of state-of-the-art digital twins to improve sustainability in this sector, particularly in reducing carbon emissions. This paper reviews the existing research where digital twins have been directly used to enhance sustainability throughout the entire life cycle of a building (including design, construction, operation and maintenance, renovation, and demolition). Additionally, we introduce a conceptual framework for this industry, which involves the elements of the entire digital twin implementation process, and discuss the challenges faced during deployment, along with potential research opportunities. A proof-of-concept example is also presented to demonstrate the validity of the proposed conceptual framework and potential of digital twins for enhanced sustainability. This study aims to inspire more forward-thinking research and innovation to fully exploit digital twin technologies and transform the traditional construction industry into a more sustainable sector.

The measurement accuracy and reliability of thermography is largely limited by a relatively low spatial-resolution of infrared (IR) cameras in comparison to digital cameras. Using a high-end IR camera to achieve high spatial-resolution can be costly or sometimes infeasible due to the high sample rate required. Therefore, there is a strong demand to improve the quality of IR images, particularly on edges, without upgrading the hardware in the context of surveillance and industrial inspection systems. This paper proposes a novel Conditional Generative Adversarial Networks (CGAN)-based framework to enhance IR edges by learning high-frequency features from corresponding visual images. A dual-discriminator, focusing on edge and content/background, is introduced to guide the cross imaging modality learning procedure of the U-Net generator in high and low frequencies respectively. Results demonstrate that the proposed framework can effectively enhance barely visible edges in IR images without introducing artefacts, meanwhile the content information is well preserved. Different from most similar studies, this method only requires IR images for testing, which will increase the applicability of some scenarios where only one imaging modality is available, such as active thermography.