Geometric modelling from point cloud data is a fundamental step of the digital twinning process for rail infrastructure. Currently, this onerous procedure outweighs the anticipated benefits of the resulting model and expends 74% of the modellers’ effort on converting point cloud data to a model. This is particularly true for rail infrastructure because railway digital twin generation could be an efficient means for maintaining and retrofitting the fundamental mode of the nation’s transport. Recent statistics illustrate that the UK increased £5.4 billion of expenditure on railway maintenance per year. This expenditure could go up to £7.2 billion increments per year per incident for railway closures due to unplanned maintenance. Better documentation of current conditions enables tracking and detecting defects in existing railways, potentially avoiding irreversible harm without impeding the national economy. This explains why there is a huge market demand for less labour-intensive railway maintenance techniques that can efficiently boost railway operations and productivity. Railway geometric modelling in the literature has focused mostly on rail detection using laser scanner profile information, with state-of-the-art methods achieving a 99% F1 score for rail detection. However, the existing methods cannot offer large-scale digital twinning required over kilometres without forfeiting precision and manual cost. They only work well in much shorter track segments (on average 300 m) or simplified cases (without complex geometries such as bridges, tunnels, crossings). This is because the search space for railway elements is much longer in multi-kilometre segments and has thousands of element types that are relatively small and thin compared to the asset as a whole. Besides, the real-railway point clouds that stretch over kilometres on the ground often encounter huge challenges, such as occlusions caused by vegetation around the track and unevenly distributed points. Varying horizontal, vertical elevations and cross-sections define the railway geometries. These characteristics complicate the modelling, which is why none of the existing methods can manage them reliably. The aim of this PhD research is to devise, implement and benchmark a novel framework that can accurately generate individually labelled geometric objects of existing rail infrastructure comprising railway track structure and overhead catenary system elements in an established data format [i.e. Industry Foundation Classes (IFC)]. The research provided in this thesis first automatically and effectively segments labelled point clusters of railway elements in point cloud data. It then automatically reconstructs the 3D geometry of the segmented point clusters in IFC format to achieve this objective. The author formed five research questions to answer the segmentation task: (1) how to automatically remove vegetation and other noise surrounding railways without using any additional prior information such as neighbourhood structures, scanning geometries and intensities of the input data? (2) how to automatically segment masts in the form of point clusters by differentiating masts from other pole-like objects in imperfect railway Point Cloud Dataset (PCD)s where occlusions, data gaps and varying point densities exist? (3) how to automatically segment Overhead Line Equipment (OLE) elements in the form of point clusters from real-world railway PCDs with complex railway geometries while occlusions, data gaps and varying point densities are present? (4) how to automatically segment railway track structure elements in the form of point clusters from real-world railway PCDs with varying horizontal and vertical elevations and complex railway geometries?, and (5) how to automatically separate rails from other linear elements adjacent to the railway corridor without relying on prior knowledge such as scanning geometry? The research presented in this thesis exploits standard railway design guidelines to automatically reconstruct the 3D geometry of the segmented point clusters as 3D solid models. The author formed two further research questions to answer the 3D solid model generation task: (1) how to automatically reconstruct labelled point clusters into 3D IFC objects for the railway domain?, and (2) how to evaluate the accuracy of a railway GDT reconstructed from a PCD?. Railways are a linear asset type; their geometric relations stay roughly unchanged, often over very long distances. The proposed framework uses the knowledge of the highly regulated and standardised railway topology and railway design engineering knowledge to segment and model railway elements in point clouds. This framework directly segments railway track structure and overhead catenary system elements and then models them without generating low-level shape primitives and without using any prior information such as user inputs, intensities of input data and scanning geometries. Experiments reveal that the proposed framework can perform quickly and reliably with complex and incomplete real-world railway PCDs featuring occlusions, extreme vegetation around the track, and local variable densities of points. Experiments on 18 km railway PCDs yield an average segmentation F1 score of 88%, an average modelling accuracy below 6 cm Root Mean Square Error (RMSE). The proposed framework can realise an estimated time savings of 94% on average compared to the current manual geometric twinning practice. The proposed framework is the first of its kind to achieve such high and reliable performance of geometric digital twin generation of existing rail infrastructure. Contributions. This PhD research provides the unprecedented ability to rapidly and intelligently model geometric railway track structure and overhead catenary system elements based on quantitative measurements. This is a huge leap over the current practice and a significant step towards the automated generation of Railway Digital Twins.