ABSTRACT Non‑destructive testing (NDT) and structural health monitoring (SHM) are essential for ensuring the safety, reliability, and service life of engineering structures. Recent advances in sensing, multi‑physics modelling, and data analytics have accelerated the development of digital twins (DTs) for real‑time monitoring and predictive maintenance. Digital‑twin‑enabled NDT/SHM frameworks integrate physics‑based simulations with continuous sensing to support damage detection and structural diagnostics, and state‑of‑the‑art reviews show that digital twins significantly enhance monitoring accuracy and support real‑time model updating in civil infrastructure [1]. Parallel advancements in AI‑assisted NDT—such as automated ultrasonic and radiographic data interpretation—further strengthen DT pipelines by improving defect detection accuracy and reducing operator dependence [2]. Contributions are invited for following topics: (i) multi‑physics and data‑driven DT modelling; (ii) sensing and data‑fusion frameworks; (iii) feature extraction and damage indicators; and (iv) AI‑enabled NDT and DT‑driven diagnostics. Other relevant topics are also welcome. REFERENCES [1] Qiuting Wang et al., Current Status and Prospects of Digital Twin Approaches in Structural Health Monitoring, Buildings, 15(7), 1021, 2025 2 Amine el Mahdi Safhi et al., AI‑Driven Non‑Destructive Testing Insights, Encyclopedia MDPI, 4(4), 1760-1769, 2024