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The potential of graph theories to assess buildings’ disassembly and components’ reuse: How building information modelling (BIM) and social network analysis (SNA) metrics might help Design for Disassembly (DfD)?
F. Denis1&2, N. De Temmerman2, Y. Rammer1
1
Building, Architecture and Town Planning, Polytechnic School of Brussels, Université Libre de Bruxelles, Brussels, Belgium
2
Transform Research Group, Architectural Engineering Laboratory, Faculty of Engineering, Vrije Universiteit Brussel, Brussels, Belgium
Abstract
Nowadays, buildings consist in an assembly of several components and systems allowing them to fit the users’ technical, functional and comfort needs. In general, buildings’ systems and components are rather integrated into each-other and therefore, rather difficult to maintain, repair, update or dismantle separately, leading to precocious obsolescence and waste.
Design for Change (DfC) and Design for Disassembly (DfD) approaches aim at a reduction of waste by designing buildings enabling reuse of their components, elements and materials. To do so, one key aspect is the interface between buildings’ components. Indeed, depending on the connection type (reversible, non-reversible), their accessibility and assembly sequence, buildings’ ease of dismantling may differ drastically.
Today, approaches such as the relational pattern method, propose to map components’ interactions through nodes and edges representing respectively components and connections. Although a network is defined within the framework of this method, it appears that the networks are mainly used as visual support for the assessor, allowing him to qualify components’ interactions. This paper explores the potential of graph theories in general, and social network analysis to characterise buildings’ networks.
To do so, comparisons between DfD concepts and graph theory metrics will be investigated to show the main similarities, differences and opportunities. Furthermore, a discussion showing the specific interest of social network for DfD will be developed. Finally, the implementation and testing of those propositions into an automated Building Information Modelling (BIM) tool will prove the potential, limitations and opportunities of such approach.
In conclusion, this research proposes to use state-of-the art knowledge of other fields related to data management and network analytics to be able to characterize and assess disassembly and therefore, will allow designers to reduce waste and increase buildings’ reuse of components.
Keywords: BIM, Design for Change, Design for Disassembly, Design decision support, SNA.
Introduction
Network systems are present all over our life from the most evident and easily distinguishable such as social networks or communication networks (phones and internet) to traffic management or spatial layout (Space Syntax). However, today, even though graph theory, which is the mathematical background of network systems, is well defined and used widely,
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it appears that the construction industry or at least designers are not yet using it to analyse, understand or optimise buildings’ designs1.
Indeed, in this paper we propose to look at buildings using a different set of glasses. Instead of seeing buildings as one static and finished product, it is proposed to look at buildings considering them as assemblies of elements and components linked and attached together physically through different connection systems2.
In other words, buildings are complex systems composed of elements varying in shapes, life span, functions, environmental impact to cite only a few. Fortunately, graph theory proposes metrics and theories allowing to quantify and qualify such systems. First, we will discuss if buildings are networks or if network analysis could help to assess buildings. Second, the similarities between buildings’ assemblies and social networks will be discussed briefly and some key metrics will be presented. Finally, potential new metrics or concepts to investigate in the future will be discussed.
Are buildings networks?
Previously in the introduction we stated that we can consider buildings as networks of components linked together through connections. This vision comes mainly from our background in transformable architecture, design for change (DfC) and design for disassembly (DfD). Indeed, to improve buildings’ ease of disassembly it is often proposed to design reversible connections (also often referred as dry connections) [1, p. 37], [2, p. 4], [3]– [5]. Although, Elma Durmisevic [6] proposes a representation of the relations between functional, technical and physical domains [Fig. 12]. By comparing this representation with a rather classical representation of a network (in this case a social network) [Fig. 13] one may find similarities in the way elements are displayed. Although, an analogy is not a proof that both systems are equivalent, we decided to investigate further the link between graph theory and DfD.
1
As an exception, we might consider the use of space syntax to characterise space layout. 2
These elements might be divided into different types depending on the functions they fulfil within the whole system, the building level, or into sub-system, the component level.
Fig. 12 – representation of the relations between functional, technical and physical domains [6, p. 137] Fig. 13 - A NodeXL social media network diagram of relationships. Taken from [7, p. 33]
125 Buildings the new social networks?
Because of our highly connected society, social networks (SN) are being more and more useful in our everyday lives. They are also extensively studied in social sciences because they are of huge importance in understanding human behaviours and interactions. One could wonder why specifically social networks are interesting to compare to buildings components’ networks.
Social networks rely on graph theory which is a reliable and strong mathematical background. Moreover, there is a quite intriguing and potentially useful analogy between buildings components’ networks and social networks. Indeed, social networks map the relationships between people or their interaction. We can create a graph of the people knowing each other and their potential influence on each other. On the other hand, buildings components’ networks will represent the connection between components and their influence. Designers would benefit from a network allowing to analyse, calculate or characterise the influence of connections (e.g. reversible, non-reversible, contaminating or not) between elements and the total disassembly potential of the building3.
In other words, social networks are specific networks used to consider relationships or exchange between people. Their mathematical background (graph theory) allow to determine and characterise the influence of an individual on the whole system, while SNA specific metrics and tools allow to process and interpret it. Buildings’ disassembly is highly dependent on the connections between the elements and today, we lack tools allowing to measure the influence of one element on the global ease of disassembly (e.g. the literature provides generally guidelines or generic qualitative assessment).
3
Moreover, current Social Network Analysis (SNA) systems allow to store, comment, and manage huge amount of data and today, the construction industry is moving towards digital solutions (e.g. BIM) all over Europe [9]–[11] generating similarly a tremendous amount of data which could be processes by such systems. The illustrations presented earlier in this paper already prove that current tools allow to generate such graphs and process them within a SNA system [Fig. 3 & Fig. 4].
Fig. 14. Revit (Autodesk) model
used as input to extract a components’ network.
Fig. 15. Network representing
the connectivity between the components of the sample model (NodeXL)
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Social networks analysis applied in buildings’ networks
Five different metrics will be discussed within this chapter, three at the level of the nodes (e.g. people or components) – degree, betweeness centrality and closeness centrality - and two at the level of the whole system (e.g. the social network or the building) – clustering coefficient and graph density. To do so, we will use the illustrations below which represent theoretical networks having the same structure.
The degree (1) of a vertex is a count of the number of unique edges that are connected to it (e.g. in the Fig. below Ike has a degree of 2 while Diane has a degree of 6). This rather simple value allows to define the node having an higher amount of connection. If we transpose this to a building, a bearing wall might have a very high degree. Coupled with a parameter defining the kind of connection would allow to assess the influence of the wall on all the elements it is connected to.
The betweenness centrality (2) allows to identify bridging elements. In terms of social networks it could be a person connecting two identified groups of people. In the example below we might consider Heather with a high betweenness centrality. Although he is only connected to three persons (degree=3), he is a key element of this network because he is bridging a gap between Diane’s group and Ike’s group. The interest of this parameter for buildings rely on the idea that building elements are fulfilling different functions (e.g. supporting, servicing, partitioning or finishing) with different life spans. Therefore, being able to identify elements connecting groups of elements will also allow us to identify key connections.
The closeness centrality (3) is the average shortest distance from each vertex to each other. This parameter is less intuitive but may present a huge potential in assessing the disassembly sequence and to compare different disassembly scenarios.
The clustering coefficient (4) is a metric allowing to characterise the number of groups/clusters within a network. This parameter might be very important for buildings’ networks because as stated in [6], [9], [10] it is important to separate elements having different functions (e.g. Site, structure, skin,…) because service life span differ from one function to another. Thus, the clustering coefficient will allow to consider if the graph is rather composed of sub-groups of the same function or not.
The Graph density (5) defines how interconnected the vertices are in the network. If we apply to buildings, it relates that the more interconnected elements are the more connections you should remove to disassemble. Furthermore, if there is a high graph density and a small clustering coefficient it will mean that a lot of elements with different functions and lifespan are interconnected to each other, leading to a potential weakness in terms of disassembly.
Fig. 16. A theoretical social network sharing the same structure as a building components’ network.
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Conclusion, new developments, and potential new metrics
In the previous parts of this paper, we tend to consider that buildings components’ networks were showing a good potential to assess buildings’ disassembly. The paper was therefore mainly showing the reasons to investigate it or the metrics already available to do so. Nevertheless, there are still some things to develop or adapt to use buildings components’ networks widely.
First, buildings are hierarchized and separated into different levels – Building level, Element Assembly or Systems and Element level- while social networks consist on the relations between nodes having the same characteristics.
Second, social networks are changing over time. People meet new contacts and forget others. While buildings are not changing that fast, they are also changing during their lifespan and evolve through renovations, demolition or deconstruction. Furthermore, the design process is a rather iterative process and using metrics used to compare the evolution of networks might be useful to compare design proposals.
Third, other metrics related to specific items from DfD should be considered an investigated: Assembly sequence – allowing to assess and measure the ease of disassembly in addition to parameters related to the kind of connection –, Assembly direction – useful to identify host and hosted elements and to check which elements relies on which –, Group distinction – allowing to differentiate elements not only regarding the clusters made by the network but regarding their function into the building.
If the three previous points and the future tools are developed in that perspective, DfD will be added to the field using graph theory to model complex interactions within a system. However, already today, because of the huge similarities between social networks and buildings, designers and researcher can already start developing simplified assessment based on tested methods. Hopefully, maybe in a near future, buildings components’ networks may be as usual as social networks.
Acknowledgements
The authors want to thank the IWT for the research grant funding this research. References
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[8] M. van Steen, Graph theory and complex networks: an introduction. Lexington: Maarten van Steen, 2010.
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