Classification of detection states in construction progress monitoring

The research conducted in this publication focusses on automated progress monitoring. The recording of the current as-built state of a construction site is achieved by photogrammetric methods (e.g. UAVs) and compared to an as-planned (4D) BIM model. To visualize the detected elements and evaluate their respective detection rate, a schema for the classification of each individual element is presented. It compares the asbuilt ground truth with the actually detected elements and thus facilitates a quick and easy interpretation of the current construction state. Temporal data from construction schedules is added to further complete the provided information through visualization. Additionally, the classification helps to identify possible lacks of detection algorithms. New parameters for detection algorithms can be applied and the results are immediately visible with an easily understandable color scheme. There are several reasons, why some of those elements were not detected: One of the main reasons is occlusions. During construction, large amounts of temporary structures like scaffoldings, construction tools, and construction machinery obstruct the view on the element surfaces. Limited acquisition positions further reduce the visible surfaces and hence the overall quality of the generated point clouds. As introduced in (Huhnt, 2005), technological dependencies can help to formalize the schedule sequence. A precedence relationship graph (PRG) can hold this information and help to identify the described occluded elements (Braun et al. 2015b). Another reason for weak detection rates is building elements which are currently under construction. As those elements count towards the overall progress, they must not be missed and play a crucial role in defining the exact position in the current process. Challenging parts are in general all construction methods, whose temporary geometry differs largely from the final element geometry. This accounts e.g. for reinforcements or formwork. On the one hand, formwork may obstruct the view of the element and thus make it impossible to be detected. On the other hand, the plane surface of a formwork for a slab might be detected as the surface of the slab itself and thus lead to false positives (Turkan et al., 2014). Due to these challenges, detection algorithms need enhancements and require thorough testing. For the respective field studies, the ground truth is required to validate calculated results. Concerning construction monitoring, different construction sites are required and in addition, data recording should be tested under varying conditions to get different use cases for robust results. In the scope of this research, various case studies have been conducted for thorough testing results. Special focus lies on the points, that are counted as relevant for the individual surfaces of construction elements. Thus, thresholds need to be defined to filter unneeded and false points. Since these thresholds depend on various conditions like acquisition accuracy or construction methods, parameter studies are required for valid results. This necessitates classification schemes to categorize all possible detection states.