The increasing demand for sustainable and resilient geo-structures, as well as the repurposing and reuse of existing ones, presents significant challenges for geo-engineers and geo-scientists. They are tasked with designing complex projects while optimising available resources. Computational modelling and data-driven approaches have become fundamental tools in the design and back-analysis of geotechnical structures such as tunnels, deep basements, slopes, dams, retaining walls, and foundations. Recent advancements in numerical methods and data-driven techniques have revolutionised traditional approaches, enhancing the accuracy, efficiency, and reliability of ground engineering-related projects, and extending their application beyond experts to a broader range of engineers and geo-scientists. This minisymposia (MS) aims to collect advanced and stabilised/robust numerical and data-driven models dealing with geotechnical and ground engineering problems. This MS will cover a range of cutting-edge subtopics, including but not limited to: • Application of physics-enhanced machine learning in ground engineering problems • Model- and data- driven techniques including model update, inverse problems, fusion of models and data and virtual control for ground engineering problems • Advanced numerical methods: boundary-fitted, i.e., mesh-based and boundary-unfitted discretisation technique, i.e., CutFEM, including meshless methods; Material Point Method (MPM); Isogeometric analysis (IGA) • Robust constitutive modelling techniques, e.g. sub-stepping, for tunnel and foundation engineering • Advanced numerical methods and data-driven models for elastic and acoustic wave propagation problems • Advanced sensing and monitoring technologies for geotechnical applications: data- and physics- driven interpretations and predictive analytics REFERENCES [1] Ninić, J. and Meschke, G., 2015. Model update and real-time steering of TBMs using simulation-based metamodels. Tunnelling and Underground Space Tech, 45, pp.138-152. [2] Bui, H.G., Ninić, J., et al., 2024. Integrated BIM-based modeling & simulation of segmental tunnel lining by means of IGA. Finite Elements in Analysis and Design, 229. [3] Liravi, H., Bui, H.G., Kaewunruen, S., Colaço, A., Ninić, J., 2025. Bayesian optimisation of underground railway tunnels using a surrogate model. Data-Centric Engineering 6, e32. [4] Zhu, H. M., Huang, M. Q., and Zhang, Q. B., 2024. TunGPR: Enhancing data-driven maintenance for tunnel linings