Automatic execution of workflows on laser-scanned data for extracting bridge surveying goals

With the capability of capturing detailed geometry of bridges in minutes, laser scanning technology has attracted the interests of bridge inspectors and researchers in the domain of bridge management. A challenge of effectively utilizing laser scanned point clouds for bridge inspection is that inspectors need to manually extract and measure large numbers of geometric features (e.g., points) for deriving geometric information items (e.g., the minimum underclearance) of bridges, named as bridge surveying goals in this research. Tedious manual data processing impedes inspectors from quantitatively understanding how various data processing options (e.g., algorithms, parameter values) influence the data processing time and the reliabilities of the surveying goal results. This paper shows the needs of automatic workflow executions for extracting surveying goals from laser scanned point clouds, and presents a computational framework for addressing these needs. This computational framework is composed of formal representations of workflows and mechanisms for constructing and executing workflows. Using a prototype system implemented based on this framework, we constructed and quantitatively characterized three workflows for extracting three representative bridge surveying goals, using three metrics of workflow performance defined in this research: exhaustiveness of measurement sampling, reliability of surveying goal results, and time efficiency.