- Open Access
Data completion in building information management: electrical lines from range scans and photographs
© The Author(s) 2017
- Received: 1 November 2016
- Accepted: 6 March 2017
- Published: 21 March 2017
The concept of building information management (BIM) is based on its holistic nature. This idea pays off, if all relevant information is fused into one consistent data set. As a consequence, the completeness of data is vital and the research question on how to complete data automatically remains open.
In this article we present a data completion technique based on knowledge management. We encode expert and domain knowledge in a generative system that represents norms and standards in a machine-readable manner. The implementation of this approach be used to automatically determine a hypothesis on the location of electrical lines within indoor range scans.
The generative paradigm can encode domain expert knowledge in a machine-readable way. In this article we demonstrate its usage to represent norms and standards.
The benefit of our method is the further completion of digital building information models – a necessary step to take full advantage of building information modeling.
- Data completion
- Formal language
- Shape grammar
- Knowledge management
Studies conducted on the productivity of the construction industries show that the industry could improve efficiency using a standardized workflow across its stakeholders, particularly in the field of electrical construction companies. According to these studies the implementation of building information modeling (BIM) is a solution (Bernstein and Jones 2012; Malleson 2015); its application would reduce conflicts and improve coordination. A survey conducted in 2015 showed that the implementation of BIM has not yet reached the majority of companies (Braun et al. 2015). The study by Hanna et al. (Hanna et al. 2014) points out the deficits of actual implementations of BIM in the electrical construction field. Other sectors of the construction industry have increased their productivity using BIM (Øbro 2015). The globally accepted Industry Foundation Classes (IFC) standard (ISO 2013) includes a complete set of electrical entities. The lack of BIM implementation in the field of building electricity is surprising, as shown by a survey conducted in the United States (Azhar 2009). This study shows a ranking of BIM features used, e.g. clash detections, visualization of electrical design or space utilization. Out of the partaking companies, 21% use BIM and report positive savings in time and cost. The remaining 79% respond that they are not using BIM due to not knowing about BIM, lack of technological experience, software incompatibility, and implementation costs. In addition, 59% of the companies which actually use BIM have only three or less years of experience with this technique. Nevertheless, these studies show that there is an interest in the utilization of BIM for electrical construction. There seems to be a hesitation of participants of the electrical construction sector to engage with BIM. This is at least partially grounded in the fact that BIM data is generally not available for projects that deal with the existing building stock which accounts for the biggest share of the building market, e.g. in the European Union for 75% (Atanasiu et al. 2011). Most of buildings date to a pre-digital area (Rapf et al. 2015). Project planning for such buildings requires extensive and expensive documentation of the as-built state. In Europe, 61% of the overall building stock consists of pre-1980 buildings. The need to upgrade these buildings in terms of energy efficiency (European Parliament 2002) demands investments in a range between 584 and 937 billion euro until 2050.
architects, engineers and interior designers can consider the existing electrical system in their design and reduce collisions of added elements and existing structures;
electrical planning companies are provided with a good base for planning of electrical systems,
electrical construction companies can reduce risks in their offers for building jobs,
building owners have an overview of the existing systems and a tool to control whether planned electrical systems collide with existing elements and systems in the building.
The general compliance of the electrical installation to the framework built by norms provides a relatively secure ground for professionals to estimate where wires could be routed, but assured data is usually not available. This situation provides the base scenario of this paper and other recent research: In the work of Petkova et al. (Petkova and Rüppel 2014), the location of sockets in 2D floorplans of buildings were used to estimate the amount of copper wire in the walls, a valuable resource and a real asset in the evaluation of a buildings value before demolition. This method integrates the prior knowledge of wiring placement directly into the algorithm, however, the norms differ between countries and been revised over time. In contrast, the method presented in this paper proposes to encode this knowledge using a knowledge management approach.
The main idea of knowledge management is to represent prior knowledge and expert knowledge in a self-contained, machine-readable manner, independent of the usage scenario. A commonly used approach to represent knowledge is based on machine learning. It “teaches” a machine to solve a problem using a training dataset; i.e. a set of input data with corresponding, labelled output data. Many computer vision problems, such as segmentation, detection, recognition, and matching, can be solved by machine learning techniques (Bishop 2007). Although many problems can be solved by machine learning, its application has some important limitations. Most machine learning techniques require a sufficiently large, labelled training dataset – a crucial precondition, which may be elaborate to meet. From the knowledge management point of view another problem of machine learning algorithms may arise: the “learned” knowledge can seldom be imported or exported in a human-readable way. As a consequence, prior knowledge, which may be available in form of specifications, standards and norms cannot be reused and the question what has been learned remains unanswered. Therefore, we chose a rule-based approach to represent knowledge (Schinko et al. 2010). The main advantage of procedural modeling techniques is the included expert knowledge within an object description; e.g. classification schemes used in architecture and civil engineering can be mapped to procedures. For a specific object only its type and its instantiation parameters have to be identified.
complex models become manageable through a few high-level parameters,
models are easier to store and to transmit, as only the process itself is described, not the processed data, i.e., the end result,
changeability and re-usability of existing solutions to modeling problems can be very much improved, and
the smaller parameter space can lead to much better results in model-based indexing and retrieval.
Such grammar systems were originally developed in formal language theory, and are commonly used in compiler construction. They are also a common tool used by generative modeling techniques (Thaller et al. 2013). For a review on generative modeling techniques we refer to (Krispel et al. 2015). The proposed grammar ruleset can be adapted to reflect changes in norms, or encode different norms, typically by a domain expert. The method is implemented as a pipeline that uses indoor 3D scans of buildings as a starting point. These scans consist of range scans and image information. From this data, a 3D representation of the scanned spaces is generated via a preprocessing step, and is further processed to determine the configuration of installation zones and a possible wiring hypothesis for the acquired data.
The proposed approach is divided into four main concepts: data acquisition, data preprocessing, the detection of visible endpoints of electrical installations (sockets and switches), and eventually the hypothesis of installation zones and a possible wiring inside the walls.
Although the final result of the pipeline is non-visual (i.e. a semantically enriched BIM model), all steps produce a visual output which is used for debugging the pipeline, for the verification of the results and for publication purposes. The point clouds have been rendered using CloudCompare1; the room geometry is exported as X3D2 and rendered with Blender3; the endpoint detection returns JPEG for visual inspection; the hypothesis modules generates SVG files for visualization.
The following sub-chapters describe each part of the pipeline in greater detail.
The input for the pipeline consists of range scan information (point clouds) in E57 format (Huber 2011), accompanied with spherical panoramas, i.e. equirectangular projections of the sphere around the scanner. Bigger datasets, e.g. a whole building floor require several scans and registration. The E57 file stores metadata for each scan; additionally, one panorama per scan has to be supplied. Our test datasets were acquired using a FARO Focus 3D scanner. In some cases the scanner-mounted camera yields too low resolutions for the details that should be detected in photographs, and some lighting conditions were challenging. In these cases, a high-resolution panoramic image was used that was taken with a Canon 500D DSLR and a Nodal Ninja 3 panoramic head. These panoramas were acquired at the scanning position, immediately after the scan.
Electrical line hypothesis
The hypothesis is carried out by two main steps: the installation zone generation and the final hypothesis extraction.
After grammar evaluation, a graph is built that encodes all possible routings. A graph consists of a set of elements (vertices or nodes) together with a binary relation that is defined on the set; a relation between two vertices is called edge (Wilson 1996). Crossing installation zones yield vertices, and detections, as shown by the blue and green rectangles in Fig. 9, are either placed as vertex on an existing edge, or connected by the shortest orthogonal path to nearby edge. Using the adjacency information of the floorplan, adjacent zones of adjacent walls are identified and connected. Furthermore, a power root node, see the orange rectangle in Fig. 9, has to be supplied; in many cases the location of the main fuse box of a floor is known and can be added to the data description, if no root is known one of the endpoints is chosen as root.
Finally, a subgraph (specifically a forest) is extracted from this graph using a discrete optimization approach. This subgraph connects all endpoints (detections) to a power root, under the assumption that the overall length of power lines, which corresponds to material costs, is minimized. Note that this is not a minimum spanning tree problem (Graham and Hell 1985), as not all vertices of the main graph might be included in the optimal solution.
Installation zone grammar
Installation zones are placed with respect to wall boundaries or “forbidden” zones, e.g. openings such as doors or windows, these objects are utilized as start symbols in the grammar.
The left side of a production rule contains exactly one nonterminal, and an optional constraint that evaluates to a boolean value, the rule will be matched only if the constraint evaluates to true. The right side can consist of any number of nonterminal and terminal symbols, together with attribute definitions in curly brackets. An attribute corresponds to a key-value pair. When a rule is evaluated, all symbols on the right hand side inherit all attributes from the left hand side, attribute definitions on the right hand side are carried out afterwards.
Given such a starting rule, the production rules are evaluated until only terminal symbols are left, and all nonterminal symbols are consumed. The terminals with a special meaning for the hypothesis extraction are hzone, vzone and root, other terminals are treated as an endpoint if it contains the attribute endpoint that is set to true. Using this mechanism, users can manually add points that should be contained in the routing hypothesis, e.g. if there are known positions of wirings that could not be detected by the automatic endpoint detection.
where the left hand side consists of pairs of nonterminals, written “ NT1:NT2”, with an optional constraint statement. Upon evaluation, the production evaluation system creates for these pair rules all possible pairings of concrete instances of nonterminals. These rules match if their condition statement evaluates to true. Pair rules are always prioritized until no pair of nonterminals matches, afterwards context free rules are processed. This allows us to formulate rules that group horizontal and vertical arrangements of endpoints together; groups that exceed a specific size will generate an additional installation zone.
Wire routing hypothesis
A wire routing in the proposed method corresponds to a set of connected straight wire segments beneath a wall surface, and is represented by a graph G W =(V,E), that contains vertices v∈V and edges e∈E. A vertex is associated to a position in wall coordinates, and an edge connects two vertices v 0 and v 1. Vertices can also be associated to an endpoint, such as sockets and switches, or a power root, expressed by attributes.
The graph of all possible wire routings is created from the list of terminal symbols after grammar evaluation. A line arrangement that is formed by the horizontal and vertical installation zones is built. Any edges that intersect forbidden zones, e.g. openings, are removed. Terminals that are marked as endpoints are connected to the graph, distinguished by three different cases: terminals near vertices are directly associated to this vertex, terminals near edges will split the edge and introduce an additional endpoint vertex on the edge, remaining terminals will create a new vertex and connected with either a horizontal or vertical edge to the nearest edge or vertex of the graph. Finally, graphs that correspond to adjacent walls are connected at neighbouring nodes using zero length edges.
with the penalty d being the euclidean distance.
This problem is also called the minimum Steiner tree problem in graphs, which is of practical importance in several areas, e.g. chip design or shortest-length connection of households to a power grid. This problem is also known to be NP-complete, as shown by Hwang and Richards (Hwang and Richards 1992), even within an approximation factor of ≈1.129 which was shown by Kaklamanis et al. (Kaklamanis et al. 2008). For small graphs, e.g. single or few-room datasets, this can be evaluated exhaustively to find the global optimum, for larger datasets a faster, approximative algorithm may be needed. Our current solution implements a local search algorithm that is similar to “A fast algorithm for steiner trees” from Kou et al. (Kou et al. 1981); this algorithm subsequently grows the final graph by sorting endpoints by 3D euclidean distance and connecting each endpoint to the graph.
This work presents an approach that synthesizes a hypothesis of electrical lines from range scans and photographs. It uses a new method that utilizes a formal grammar to represent the guidelines that govern the placement of electrical lines.
The main contribution of our new approach is the formalization of norms, standards and prior knowledge in a machine-readable manner. The presented application in the context of building information management shows the potential of the generative paradigm. Unfortunately, the machine-readable representation in form of a grammar system differs significantly from human-readable, descriptive norms. Future research will focus on a representation that closes the gap between machine-readable and human-readable knowledge representations.
Besides scientific contributions the new approach has several benefits: it enables the adaption of rules to different norms, up to specifics that concern only one building. The labeling of detected sockets with confidence values from the detection stage proved to be a viable approach in this project. Concerning future developments practitioners which evaluated our solution remarked that it would be necessary to show users that the generated data is an estimate and not the sole truth, to counter the blind believe, which stakeholders often demonstrate in the face of external data. The representation of an installation zone probability as a heat map would be a way to present this information. Practitioners also stated that this feature would be helpful in the design of new buildings. In later BIM based planning stages, the number and locations of sockets and switches per room are known, but no automated means to estimate the cable length and the attached costs are available.
For a productive usage the following issues have to be addressed. Just like any acquisition and reconstruction method, our approach also relies on the input data quality and completeness (a wall which has not been photographed cannot be analysed); the preprocessing step assumes a single floor with straight walls only; the endpoint detection can only detect what has been learned (in our examples we did not train high voltage sockets); the hypothesis cannot reproduce cases where the actual wiring does not follow the rules – a scenario often found in old buildings. Nevertheless, this proof of concept already shows the applicability of our approach.
Source code for the proposed pipeline for the modules orthophoto generation, electric endpoint detection and installation zone generation as well as wiring hypothesis is freely available at the GitHub repository 4 in Modules orthogen, elecdetect and wiregen.
Per-Kristian Hanson and Marcus August Frølich Innvær from CITA have been instrumental in the creation of the 3D scan dataset, panoramic images and ground truth for this project. Furthermore, the authors would like to thank Robert Viehauser for the implementation of the computer vision pipeline and Sebastian Ochmann for kindly providing Fig. 6.
UK is the main author of this manuscript. He is a PhD student at Graz University of Technology and Fraunhofer Austria Research GmbH. He developed the electrical line hypothesis method described in this document. HLE is an Architectural Design Researcher at The Royal Danish Academy of Fine Arts, Schools of Architecture, Design and Conservation CITA, and made key contributions to the data acquisition part for the evaluation of the pipeline. MT is an Associate Professor at CITA and took part in the pipeline design and the Introduction section of this manuscript. TU is Deputy Head of the business area Visual Computing of Fraunhofer Austria Research GmbH and made key contributions to the Knowledge Management section. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
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