The cost and effort of modelling existing bridges from point clouds currently outweighs the perceived benefits of the resulting model. The time required for generating a geometric Bridge Information Model, a holistic data model which has recently become known as a "Digital Twin", of an existing bridge from Point Cloud Data is roughly ten times greater than laser scanning it. There is a pressing need to automate this process. This is particularly true for the highway infrastructure sector because Bridge Digital Twin Generation is an efficient means for documenting bridge condition data. Based on a two-year inspection cycle, there is a need for at least 315,000 bridge inspections per annum across the United States and the United Kingdom. This explains why there is a huge market demand for less labour-intensive bridge documentation techniques that can efficiently boost bridge management productivity. Previous research has achieved the automatic generation of surface primitives combined with rule-based classification to create labelled cuboids and cylinders from point clouds. While existing methods work well in synthetic datasets or simplified cases, they encounter huge challenges when dealing with real-world bridge point clouds, which are often unevenly distributed and suffer from occlusions. In addition, real bridge topology is much more complicated than idealized cases. Real bridge geometries are defined with curved horizontal alignments, and varying vertical elevations and cross-sections. These characteristics increase the modelling difficulties, which is why none of the existing methods can handle reliably. The objective of this PhD research is to devise, implement, and benchmark a novel framework that can reasonably generate labelled geometric object models of constructed bridges comprising concrete elements in an established data format (i.e. Industry Foundation Classes). This objective is achieved by answering the following research questions: (1) how to effectively detect reinforced concrete bridge components in Point Cloud Data? And (2) how to effectively fit 3D solid models in the format of Industry Foundation Classes to the detected point clusters? The proposed framework employs bridge engineering knowledge that mimics the intelligence of human modellers to detect and model reinforced concrete bridge objects in point clouds. This framework directly extracts structural bridge components and then models them without generating low-level shape primitives. Experimental results suggest that the proposed framework can perform quickly and reliably with complex and incomplete real-world bridge point clouds encounter occlusions and unevenly distributed points. The results of experiments on ten real-world bridge point clouds indicate that the framework achieves an overall micro-average detection F1-score of 98.4%, an average modelling accuracy of (C2C) ̅_Auto 7.05 cm, and the average modelling time of merely 37.8 seconds. Compared to the laborious and time-consuming manual practice, the proposed framework can realize a direct time-savings of 95.8%. This is the first framework of its kind to achieve such high and reliable performance of geometric digital twin generation of existing bridges. Contributions. This PhD research provides the unprecedented ability to rapidly model geometric bridge concrete elements, based on quantitative measurements. This is a huge leap over the current practice of Bridge Digital Twin Generation, which performs this operation manually. The presented research activities will create the foundations for generating meaningful digital twins of existing bridges that can be used over the whole lifecycle of a bridge. As a result, the knowledge created in this PhD research will enable the future development of novel, automated applications for real-time condition assessment and retrofit engineering.