A modular automated modelling framework for cut-and-cover excavations in mixed ground conditions

Abstract

In recent years, the growing demand for underground infrastructure has driven expansion into larger and deeper regions. The excavation of structures under mixed ground conditions combines the dual complex challenges of soil and rock layers, such as the interaction between the depth of soil and rock layers and the depth of structural excavation, and the problem of spatial asymmetric three-dimensional beddings. While numerical simulations effectively represent ground characteristics during excavation and the interaction with support structures, the continual influx of project data frequently requires labour-intensive, repetitive design adjustments and model re-assessments. Compounded by platform interoperability issues across design, analysis, and decision-making stages. Thus, employing building information modelling (BIM) to facilitate seamless information exchange across diverse software systems can enhance workflow efficiency and improve the optimisation of engineering designs. This paper introduces a modular automated framework that combines parametric modelling and numerical simulation tools with digital platforms. By modularising and automating the design, analysis, and decision-making stages, it simplifies information exchange between digital models and numerical analysis, while enabling real-time, adaptive decision-making. Further, it integrates data from numerical simulations, historical observations, and monitoring data into digital platforms at the decision-making stage, providing dynamic criteria to adapt designs that accommodate long-term geotechnical uncertainties. Additionally, the framework emphasises the advantages of incorporating long-term local and satellite monitoring data, thereby enhancing both data management and decision-making processes. Illustrated through a workflow use case at a cut and cover excavation, sensitivity analysis identifies key parameters affecting stability under mixed ground conditions, demonstrating the framework’s capability to address complex challenges effectively. This framework ensures a continuous information flow from design through to decision-making, providing an advantage in managing ground-structure interactions.