LIDAR-BASED AS-BUILT BIM RECONSTRUCTION FOR INFRASTRUCTURE

Abstract

The transportation infrastructure plays an important role in supporting a nation's economy, security, overall welfare, etc. Building Information Modeling (BIM) is widely adopted and harbors immense potential for enhancing transportation infrastructure. As BIM evolves, the transition from paper-based workflows to electronic/computerized workflows has given rise to BrIM, an adaptation of BIM for bridge projects, which enhances the precision of design drawings, and promotes better collaboration among project teams. Integration of technologies like laser scanning with BIM facilitates comprehensive as-built documentation of existing structures, thereby enhancing operational monitoring. BIM can be broadly classified into two primary forms: the as-designed one, which is developed during the initial design phase, and the as-built one, which represents the actual state of the construction at or following its completion. Scan-to-BIM is Currently utilized for architectural model reconstruction, but little research focuses on point cloud-aided bridge modeling. This study classifies components of a flyover based on observations of publicly accessible RC bridge point cloud data, identifying slab, beam, crosshead, pier, and foundation as sufficient for the case of flyovers in BIM bridge model creation. A universal workflow for the process of manual BIM bridge model creating based on 3D point cloud data is proposed, including 3 stages: Preparation (Data Collection and Component Classification), Data Processing (Format Standardization), and BIM Model Creating (Dimension Adjustment, Position Matching, Iteration and Repetition, and Connection Refinement). An example using an as-built flyover (Bridge A) covering all 5 types of components demonstrates the applicability of the workflow, creating Revit Families for all components, assembling them in BridgeA.rvt, and repairing all gaps caused by dissatisfied connection. Then a BIM bridge model for Bridge A is created along with this dissertation. Future directions based on this workflow include developing methods to verify BIM model accuracy compared to point cloud data, introducing quality assessment systems for bridge engineering projects, exploring AI-assisted automation, etc.