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Deconstruction Project Planning of Existing Buildings Based on Automated Acquisition and Reconstruction of Building Information

: Volk, Rebekka; Luu, Thu Huong; Mueller-Roemer, Johannes; Sevilmis, Neyir; Schultmann, Frank


Automation in Construction 91 (2018), S.226-245
ISSN: 0926-5805
Fraunhofer IGD ()
Object detection; 3D Reconstruction; Information acquisition; Building information models (BIM); Digitized Work; Computer vision (CV); building auditing and acquisition; automated 3D building reconstruction; building inventory generation,; optimized project planning; optimized dismantling; optimized deconstruction

During their lifecycles, buildings are changed and adapted to the requirements of generations of users, residents and proprietaries over several decades. At the end of their life time, buildings undergo either retrofit or deconstruction (and replacement) processes. And, modifications and deviations of the original building structure, equipment and fittings as well as the deterioration and contamination of buildings are often not well documented or only available in an outdated and unstructured way. Thus, in many existing buildings, incomplete, obsolete or fragmented building information is predominating and hampering retrofit and deconstruction project planning. To plan change or deconstruction measures in existing buildings, buildings are audited manually or with stationary laser scans which requires great effort of skilled staff and expensive equipment. Furthermore, current building information models or deconstruction planning systems are often not able to deal with incomplete building information as it occurs in existing buildings. We develop a combined system named ResourceApp of a hardware sensor with software modules for building information acquisition, 3D reconstruction, object detection, building inventory generation and optimized project planning. The mobile and wearable system enables planner, experts or decision makers to inspect a building and at the same time record, analyze, reconstruct and store the building digitally. For this purpose, a Kinect sensor acquires point clouds and developed algorithms analyze them in real-time to detect construction elements. From this information, a 3D building model and building inventory is automatically derived. Then, the generated building reconstruction information is used for optimized project planning with a solution algorithm of the multi-mode resource-constrained project scheduling problem (MRCPSP) at hand. In contrast to existing approaches, the system allows mobile building recording during building walkthrough, real-time reconstruction and object detection. And, based on the automatically captured and processed building conditions by sensor data, the system performs an integrated project planning of the building deconstruction with available resources and the required decontamination and deconstruction activities. Furthermore, it optimizes time and cost considering secondary raw material recovery, usage of renewable resources, staff qualification, onsite logistics, material storage and recycling options. Results from field tests on acquisition, reconstruction and deconstruction planning are presented and discussed in an extensive non-residential case study. The case study shows that the building inventory masses are quite well approximated and project planning works well based on the chosen methods. Nevertheless, future testing and parameter adjustment for the automated data processing is needed and will further improve the systems' quality, effectiveness and accuracy. Future research and application areas are seen in the quantification and analysis of the effects of missing data, the integration of material classification and sampling sensors into the system, the system connection to Building Information Modelling (BIM) software via a respective interface and the transfer and extension to retrofit project planning.