Toward generation of a Building Information Model of a deformed structure using laser scanning technology

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1 Toward generation of a Building Information Model of a deformed structure using laser scanning technology R Zeibak-Shini, R Sacks & S Filin Technion Israel Institute of Technology, Israel Earthquakes are among the most devastating natural disasters, striking with no warning and causing massive damage of buildings and infrastructure. Following such an event, the need arises to gather information regarding structural damage rapidly, especially in urban areas. The main benefits of rapid survey and damage assessment to constructed facilities are to determine possible locations of trapped survivors, to check the structural stability and durability of deformed buildings for their ability to provide shelter, and to detect other post event dangers. Following deformation, structural analysis is needed and a Building Information Model (BIM) of the deformed state of the structure can provide the semantic information required. The authors are developing a method for generating an information model of a deformed structure using an as-built information model and laser scanning technology. Unlike other research efforts that aim to extract semantically rich object models from point clouds and other data, this work tackles the problem of deformation, but benefits from utilizing prior information as a basis for reconstruction of the deformed structure. In this paper we outline the method and the research challenges it presents, with special focus on the problem of object extraction from the point cloud. Keywords: Terrestrial laser scanning, building information modeling, disaster recovery, damage assessment. 1 Introduction Civil engineering research is increasingly concerned with devising ways to improve disaster recovery capabilities. Disasters striking populated urban environments cause damage to buildings, turning them from shelters into serious threats to human life. Rapid assessment of structural damage is essential after disastrous events in order to provide guidance for rescue forces and other immediate relief efforts, as well as for structural analysis and the subsequent process of reconstruction and rehabilitation. Most recovery actions, such as data collection, damage identification and rescue guidance are manual, risky and slow. Due to difficulties in accessing the affected areas, remote sensing techniques for data collection and site documentation have been developed. Remote sensing techniques such as airborne and terrestrial laser scanning can provide valuable information for disaster management studies. Unlike airborne scanning, terrestrial laser scanning enables an accurate survey of vertical structures and so find its use in structural deformation measurement and change detection. Lindenbergh and Pfeifer (2005) studied deformation of a sea-lock via scans obtained from the same position. Girardeau- Montaut et al. (2005) detected changes on a construction site during the excavation phase using point clouds captured from almost the same pose (location and orientation) and organized in an Octree structure. These studies demonstrate the potential of detecting variations within physical scenes

2 without resorting to interpretation of radiometric content. It also shows that in most cases where terrestrial laser scanning is being applied, some constraints on the studied objects or on the scanner pose are being imposed. Since the assessment of the actual change and the ability to quantify and measure it (e.g., size, volume) offer great assets, Zeibak and Filin (2007) proposed a model for the detection of changes in cluttered scenes without the imposition of external constraints. The proposed model is efficient for the assessment of changes while being aware of the unique characteristics of terrestrial laser data. Thus although only a few studies have experimented with terrestrial laser scanning for deformation monitoring and modeling, terrestrial scanning provides a much more detailed view of a structure and so contributes to its model analysis. However, structural analysis of a deformed building using software requires a semantically meaningful building information model (BIM). BIM covers geometry, spatial relationships, geographic information, quantities and properties of building components and provides the data needed for a variety of functional analyses. The essential characteristics of a BIM are that it should use 3D constructive solid geometry (CSG), it should be parametric (Sacks et al. 2004) representing both geometric behavior and design intent, and carry semantically meaningful information about its objects in a machine-readable form (Eastman et al. 2011). BIM can be used to store the information of a building through the entire building life cycle, including the processes of construction and facility operation. Most significantly, structural BIM tools enable automated generation of stick and node and/or FEM representations for structural analysis. Therefore, a BIM of the deformed state of a structure will provide the information needed for structural analysis. Therefore, the heart of the problem is to generate a building model from the scanning data. Numerous researchers have attempted to automate recognition of meaningful building objects, and thus to compile BIM models, from scanning data and other data (Brilakis et al. 2010). In terms of asbuilt data collection on construction sites, data acquisition via terrestrial laser scanning has matured sufficiently to improve project quality control processes and modeling. Bosche and Haas (2008) proposed a new approach for robust automated retrieval of 3D CAD model objects in construction from range images. Tang et al. (2010) surveyed techniques developed in civil engineering and computer science that can be utilized to automate the process of creating as-built BIMs. The authors sub-divided the overall process into three core operations: geometric modeling, object recognition, and object relationship modeling. They surveyed the state-of-the-art methods for each operation and discussed their potential application to automated as-built BIM creation. They also outlined the main methods used by these algorithms for representing knowledge about shape, identity, and relationships. All of the aforementioned approaches for compiling BIM models based on scans and other data have been developed for construction management applications; none have been applied in the context of disaster recovery. 2 Proposed methodology The overall objective of this research is to develop the capability to rapidly generate a building information model of the exterior envelope of a structure that has undergone deformation. The method proposed uses a building information model of the structure before deformation (assumed to be available) and a laser scan of the building after deformation. Unlike conventional mapping techniques e.g., land surveying and photogrammetry, laser scanners provide rapid and direct description of 3D geometry independent of lighting conditions, and without the need for direct contact with the affected site or for manual data collection. The point-cloud provided by high-resolution scanners is both dense and accurate, providing a detailed description of objects irrespective of their shape complexity. It is, therefore, not surprising that laser-scanning technology is rapidly becoming the popular alternative for acquiring 3D scenes for site characterization, reverse engineering, and many other applications. However, the cloud of points

3 provides a geometric description of the scanned scene but carries no semantic information regarding the objects within. Consequently, data interpretation is needed. On the other hand, a building information model (BIM) describes a building with respect to its geometric and its semantic properties. An information model of the original state of a building maintains a large amount of detail and semantic information about building parts and the spatial relationships between them. The method to be developed will need to bridge this gap and provide a mapping schema from the point cloud to the structural elements in the BIM. To do this, the topological and geometrical relationships between the scans and the BIMs must be established so that the new (damaged) locations and geometries of the objects in the original BIM can be identified, and the semantic relationships between the parts of any subdivided objects can be established. Shape diversity, cracking of structural elements, and the varying level of detail in which objects are represented due to the changing scanning resolution make their detection and analysis a challenging task. The proposed approach is illustrated in the flowchart shown in Figure 1. It consists of three main steps: 1. point cloud processing for extraction of primitives and topology, 2. data matching for object identification and damage detection, and 3. generation and modelling of the post-disaster BIM. Figure 1. A flow chart describing the proposed approach. Some of the expected challenges are: a) object and topology extraction from discrete laser scans that lack any semantic information; b) matching the data from two very different sources (the scan and BIM), c) overcoming mismatching hurdles in order to detect actual damage; d) damage

4 identification and quantification; e) compiling a BIM that represents damaged components and their relationships, which requires extension of existing building model object schema. 2.1 Object and topology extraction This stage will include two parallel processing schemes of both data sources (BIM and scan) that will eventually lead to a common representation of the geometric primitives and the topological relationships between them, extracted from each source. A complication in this regard is that the scanned point cloud describes the outer surfaces of the finishing layers of a building (such as stone cladding, glass curtain walls, plastered block walls in structural frames, etc.); it does not describe its structure. Therefore, this phase will mainly take place in the laser scan rather than in the BIM, in order to enable the extraction of semantic information regarding the discrete data provided by the scanner and therefore, identifying the scanned objects. An important property of terrestrial laser scanning is its varying resolution as a function of objectscanner distance. As angular spacing is fixed, the point density decreases as a function of the range and yields changes in level of description of objects in the scene. To accommodate the changing spacing of the points within the cloud, the proposed solution originates from the realization that the points themselves offer only a sample of the object surface, and that most of the scanned objects are solid by nature. Following this realization, 3D laser scans can be seen as range panoramas whose axes are the latitudinal and longitudinal scanning angles, and the ranges are the intensity values. This data representation establishes regularity and therefore defines topological neighbourhood adaptively to the changing point density and so, irrespective of the change in depth (Zeibak and Filin 2007). The volume of information within a laser point cloud and its 3D nature requires efficient means to analyse the data. Fitting parametric surfaces and CSG is time consuming considering the huge amount of data. Instead, the proposed solution focuses on regular buildings that are composed of planar surfaces. Regular buildings generally consist of elements with primitive geometric properties such as walls, columns, slabs, floors, windows, doors, etc. These are formed mostly by planar faces. Therefore, piecewise planar analysis is sought as a fast interpretation technique. Hence, local surface normals are efficiently computed at each point, as described in Zeibak and Filin (2009). A preliminary step to extract planar segments from the laser data is necessary. This process, known as segmentation, partitions the data into disjoint, salient, regions usually under the assumption that individual segments tend to represent individual objects or object parts. For primitive extraction, we propose to apply the mean-shift segmentation procedure (Comaniciu and Meer 2002) on the normal vectors image. We choose the mean-shift algorithm as it preserves discontinuities and is not computationally expensive. Once faces are identified, a topology tree can be compiled to represent the geometric relationships between adjacent faces. In a similar way, a topology tree can be derived from the BIM model by reducing the CSG representation to a boundary representation (BREP). The tree can be simplified by pruning it to exclude faces that are not visible from the scanning points (and that therefore will not exist in the tree derived from the point cloud). 2.2 Data matching Once primitives and topology are extracted, the next challenge will be recognizing the structural components and detecting changes caused to those components following deformation. So the next problem to be solved is matching between the point cloud and building information model of the structure before deformation. For the data matching step, the point cloud and the BIM model are assumed to be pre-registered in the national grid. This implies that some objects, such as ground floor columns, will remain close to their original location, and provide good starting points for seeding the matching of the two topology trees.

5 Once faces are matched, a change detection process between the pre- and post-event datasets is essential. A process will be sought to track discontinuities of some key properties specifying the original entities and their parts, to detect changes in their geometry and interrelations. The goal of this stage is to determine the relationships between segments of parts in the damaged structure and the original parts to which they belonged. 2.3 Compiling the post-damage BIM The final challenge will be to develop a knowledge-based modeling scheme to interpret the meaning of the changes identified. This stage is devoted to modeling the semantic information extracted regarding the new state of the structure. Such information includes each object's new identity, new location and new relationships with its neighbors. This may be hard to interpret because objects once whole and linear are likely to have become tilted, cracked or displaced. This stage will also require extension of existing building model object schema, such as the IFC model (Vanlande et al. 2008), to include segment, crack and other entities and relationships. This aspect is expected to be straightforward. 3 Preliminary results Some initial experiments have been carried out to explore the challenges associated with the matching process between the laser scan and BIM. Figure 2 illustrates the challenges in implementing the scan and BIM matching process on an irregular building façade that consists of many openings, yet hasn't undergone any damage. An exterior scan of the façade (Figure 2a) successfully captures the façade exterior with all its details. However, unlike its existing BIM (Figure 2b), the laser scan lacks any notion of the identities of the building elements, their functions and their interconnections. a) b) Figure 2. a) Laser scan of a building facade exterior. b) Screen shot of a BIM model of the same building façade.

6 The first step towards object extraction is to segment the laser scan based on planarity properties. Figure 3 shows the mean-shift segmentation results on surface normal vectors of the building façade point cloud. The results are presented as an image using a polar representation of the scan. Different colours represent different planar segments. Notice that the result reveals all the faces of the building entities. Figure 3. Mean-shift segmentation results. Zooming into the results exposes manifest challenges in the data matching process, when attempting to recognize the segments identities. Such challenges are due to the fact that scanning the building exterior will only capture the façade finishing layer's geometry, whereas the BIM consists of rich information regarding the building element identities and relationships. Figure 4a shows that the outer planar facade formed by columns and beams appears as one segment, making their association with the multiple faces of the BIM columns non-trivial. Figure 4b illustrates another difficulty in the object recognition process. Perpendicular planar faces belonging to the same building element (the column shown in Figure 4b) appear as separate segments in the scan, as they form two different planar faces in space. They two faces derived from the scan must be merged into an object with a single identity. a) b) BIM Scan Figure 4. Challenges in the data matching phase (no-damage case). BIM Scan The aforementioned challenges exist even without occurrence of any structural damage. Therefore, in the case of a damaged building, the task is expected to be more complex. Additional scanning experiments were carried out, but this time for a structure that has undergone some structural damage. Figure 5a shows a damaged structure - a relatively simple frame with a masonry infill wall. The laser scan of the damaged structure shown in Figure 5b demonstrates the ability of the laser scanning technique to accurately capture fine details. Although the scan maps the deformed state of the structure precisely, it is very different from the BIM that represents the original state of the structure prior to damage (see Figure 5c). But the BIM can offer all the necessary properties regarding the original object and so assists in detecting damaged and undamaged building elements. Some of those properties are geometric (size, area, volume, angle between neighboring facets) and others are topological.

7 a) b) c) Figure 5. a) An image of a damaged structure. b) A laser scan of the damaged structure. c) BIM of the same structure before occurrence of any damage. In the majority of cases, structural damage is reflected in the geometry of a building. Damage such as cracks is manifested as discontinuities in the faces of objects. Computation of the normal vectors in a point cloud (as shown in Figure 6a) is an effective means to identify discontinuities, leading to identification of damaged components. The different colours in the figure represent different normal vector directions. a) b) Figure 6. a) Normal vectors map. b) Segmentation results. Figure 6b shows the segmentation results. As can be seen, the full range of damage has been highlighted, as well as discontinuities in the original object. When the object extraction step is completed successfully, the remaining step is to distinguish between design features in the original object s geometry, such as reveals or steps in a concrete element, and cracks that have resulted from structural damage. Where discontinuities result from structural damage, changes in the relative location and orientation of adjacent face segments will be apparent, and therefore, will be used to derive the cracks. 4 Summary Given the high cost of acquiring the detailed information needed to support renovation and maintenance of the large stock of buildings in developed cities, the challenge to extract semanticallyrich building information models from laser scans and other remotely-sensed data is the focus of many research groups. In this research, we propose to tackle a more difficult problem to compile an information model of a damaged structure but with the simplifying assumption that a BIM of a

8 building s original state is available. The motivation is that when earthquakes and other disasters damage and deform structures, a BIM model of the deformed state can provide engineers with the information necessary to analyze the structural state of a deformed building and make decisions regarding its stability and durability. The method proposed has three steps: expression of the two information sources (point cloud and as-built BIM model) in a common format, matching the sources to locate structural objects in the point cloud, and identification of visible damage to structural objects. Calculation of surface normals and segmentation are the basis for developing topology graphs from the point cloud, while similar topology trees can be derived from boundary representations of the exposed surfaces of the structural objects from the BIM model. Graph matching between these two representations will be augmented by using known starting points and other data, such as material textures. The approach is limited to the exposed surface elements of the building. Experiments performed to date have shown that the surface normal and segmentation process is a promising approach to developing a useful topology graph from the point clouds, and that segmentation can identify cracks in damaged structural components. Future work will seek to implement the matching process, develop algorithms to identify damaged components, and compile an object model schema for BIM representation of damaged structural components. References BOSCHE, F., AND C. T. HAAS Automated retrieval of 3D CAD model objects in construction range images. Automation in Construction 17 (4): BRILAKIS, I., M. LOURAKIS, R. SACKS, S. SAVARESE, S. CHRISTODOULOU, J. TEIZER, AND A. MAKHMALBAF Toward automated generation of parametric BIMs based on hybrid video and laser scanning data. Advanced Engineering Informatics 24 (4): COMANICIU, D., AND P. MEER Mean shift: a robust approach toward feature space analysis. Pattern Analysis and Machine Intelligence, IEEE Transactions on 24 (5): EASTMAN, C. M., P. TEICHOLZ, R. SACKS, AND K. LISTON BIM Handbook: A Guide to Building Information Modeling of Owners, Managers, Designers, Engineers and Contractors. Second Edition ed. Hoboken, NJ: John Wiley and Sons. GIRARDEAU-MONTAUT, D., M. ROUX, R. MARC, AND G. THIBAULT Change detection on points cloud data acquired with a ground laser scanner. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 36 (part 3):W19. LINDENBERGH, R., AND N. PFEIFER A statistical deformation analysis of two epochs of terrestrial laser data of a lock. Proc. of Optical 3D Measurement Techniques 2: SACKS, R., C. M. EASTMAN, AND G. LEE, Parametric 3D modeling in building construction with examples from precast concrete. Automation in Construction 13 (3): TANG, P., D. HURBER, B. AKINCI, R. LIPMAN, AND A. LYTLE Automatic reconstruction of as-built building information models from laser-scanned point clouds: A review of related techniques. Automation in Construction 19 (7): VANLANDE, R., C. NICOLLE, AND C. CRUZ IFC and building lifecycle management. Automation in Construction 18 (1): ZEIBAK, R., AND S. FILIN Change detection via terrestrial laser scanning. International Archives of Photogrammetry and Remote Sensing 36 (3/W52): ZEIBAK, R., FILIN, S., Object Extraction from Terrestrial Laser Scanning Data. In Proceeding of FIG Working Week, Eilat, Israel, May 3-8, 2009, published on CD-ROM.