INTEGRATING BIM WITH SYSTEM DYNAMICS AS A DECISION-MAKING FRAMEWORK FOR SUSTAINABLE BUILDING DESIGN AND OPERATION

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1 INTEGRATING BIM WITH SYSTEM DYNAMICS AS A DECISION-MAKING FRAMEWORK FOR SUSTAINABLE BUILDING DESIGN AND OPERATION Lawrence C. Bank 1, Michael McCarthy 2, Benjamin P. Thompson 2, Carol C. Menassa 2 1 The City College of New York, Department of Civil Engineering, 160 Convent Ave., New York, NY lbank2@ccny.cuny.edu (corresponding author) 2 University of Wisconsin Madison, Department of Civil and Environmental Engineering, 1415 Engineering Dr., Madison, WI ABSTRACT The objective of the research described in this paper is to integrate a decision-making framework for sustainable design of buildings with a Building Information Modeling (BIM) tool. Integration of a BIM model with a decisionmaking tool and sustainability metrics addresses the difficulties of making decisions earlier in the design/build process, and allows for specific sustainability trade-off analyses to be conducted, using the actual building conditions and characteristics. It is intended to improve the way data is utilized in a building throughout its life cycle, and to model the impact of design, maintenance, operations, and occupant behavior modification decisions made in an effort to improve the building s contribution to a sustainable infrastructure. Pertinent information contained within a BIM model is extracted, and utilized in decision-making related to operations, maintenance, and upgrades, and the development of what-if scenarios. Decision-makers will be able to evaluate options for improving their building s environmental sustainability performance. The research provides a new means for sharing data amongst various building modeling programs, and a new tool for making design decisions related to sustainable building design. KEYWORDS BIM, Buildings, Sustainability, Decision-making INTRODUCTION Buildings consume the majority of electric power and natural gas in the United States (Brass 2007), represent a significant portion of the nation s water use, are the cause of the majority of its waste output, and are responsible for over one third of its greenhouse gas emissions (USGBC 2009). The building industry also consumes approximately 40% of the world s raw materials (Hill and Bowen 1997; USGBC 2009), more than any industry but food production (Berge 2000). A drive to reduce these numbers is changing the way buildings are designed, built, operated, and maintained. However, it is difficult to assess the relative improvements in sustainability of one decision versus another (Anastas and Zimmerman 2003). Adding to this difficulty is the fact that many key decisions must be made in the design phase, when the ability to influence project cost is greatest, but when much information about the final design (and future actual performance) is unavailable (Barrie and Paulson Jr. 1984). "The challenge is thus finding a method to use detailed simulation tools even during the early stages of design when values for many of the variables for the building s technical sub-systems are not yet available" (Brahme et al. 2001), and to provide the designer with quantitative predictions of the building s future performance. Since building owners and developers do not have unlimited resources to design and construct their buildings, this creates a dilemma of how best to apply limited building budgets when designing for sustainability. More owners are now requiring or requesting LEED (Leadership in Energy and Environmental Design) or other 3 rd party certification of their projects. But, to maximize the true sustainability benefits of LEED certification, designers need to know which combination of credits provides the optimal choice of design variables for the building s sustainability, while keeping their project within its budgetary constraints.

2 BACKGROUND Sustainability Indicators The term sustainable construction means creating a healthy built environment using resource-efficient, ecologically-based principles" (Hill and Bowen 1997). However, measuring the sustainability of a building remains problematic. Numerous protocols are currently in use to assess sustainability, including, for example: (1) the Global Reporting Initiative, which uses indicators for material use, energy consumption, water use, emissions and waste, and many other environmental and social issues to develop their ecological footprint sustainability reports (GRI 2006); (2) Yale University s Office of Sustainability, which uses three main categories of sustainability metrics: (i) use of natural resources; (ii) systems and processes, which includes procurement, waste management, land use, food, transportation, and building design; and (iii) culture, which includes social justice issues on campus (Yale Office of Sustainability 2005); (3) the EU s sustainable development strategy, which includes an extensive list of items for measuring sustainability (European Commission 2001); (4) the World Economic Forum sustainability performance index, which deals with two main categories of indicators: (i) reducing environmental stresses on human health; and (ii) protecting ecosystem vitality (Esty et al. 2006); and (5) the American Society for Testing and Materials (ASTM) framework for sustainable design of buildings (ASTM 2005). Numerous certification and rating systems are available throughout the world for sustainable building, as well, including: (1) the United States Green Building Council s (USGBC) Leadership in Energy and Environmental Design (LEED) program (USGBC 2009); (2) the Living Building Challenge (McLennan 2006); (3) BREEAM (BRE Environmental Assessment Method) (BREEAM 2009); (4) Green Globes (Green Globes 2010); and (5) BOMA BESt (Building Environmental Standards) (BOMA 2009). While each of these systems requires different performance goals, the categories of environmentally sustainable design features addressed by LEED are illustrative of the general categories of most of these rating systems. LEED, run by the U.S. Green Building Council (USGBC) (a Non-Governmental Organization), awards points for various design features, including the major categories of: (1) Sustainable Sites; (2) Water Efficiency; (3) Energy and Atmosphere; (4) Materials and Resources; (5) Indoor Environmental Quality; and (6) Innovation and Design Process (USGBC 2009). The environmental sustainability of the building is then rated based on a threshold level of points achieved. Decision-Making and System Dynamics (SD) The premise of this research is that current methods of measuring, predicting, and optimizing the sustainable performance of a building can be improved through the use of new, integrated decision-making tools. The current systems for designing buildings rely on a number of disjointed analyses to determine whether discrete requirements have been met by various systems (e.g., HVAC (Heating, Ventilation and Air Conditioning), plumbing, lighting) or design features (e.g., landscaping, renewable energy generation, parking). The system-level building design method under consideration in this paper allows these currently un-connected analyses to be integrated and optimized in a systemic fashion, based on either the sustainability indicators or building rating systems discussed above. The key difficulty in determining the sustainable performance of a building is the fact that the term sustainability encompasses a wide range of concepts, and the three fundamental components that define sustainability (e.g., environmental quality, societal well-being, and economic stability) are often in conflict and very difficult to integrate into a single sustainability rating. This difficulty leads to different interpretations of the environmentally sustainable performance of a building (Anastas and Zimmerman 2003). Lacking any acceptable definition of environmentally sustainable performance, discussions of sustainability often deteriorate into discussions of economics, in terms of payback times, life cycle costs, and durability. While these are important considerations, they generally miss the point of LEED, or any other rating system, which is, ultimately, to provide a building that has as small a negative impact on the natural environment and human health and productivity as possible, while being economically viable. Thus, it is important to have a means of making decisions that, while considering costs, can also give an indication of the true level of environmentally sustainable performance of the building. From another perspective, taking the existing system of determining environmentally sustainable performance as a fixed point, it is important to understand the eco-effectiveness of design features, as well as their cost-effectiveness. In other words, since buildings are built on a finite budget, it would be extremely useful to be able to determine how,

3 given that fixed, finite budget, a designer could maximize a building s environmentally sustainable performance. This means using sustainability indicators, and the relationships among them and the building systems, to determine an optimal scheme of investing this limited budget in sustainable design features. Improving the sustainability performance of buildings is a difficult problem, because of the difficulty in assessing the relative improvements in sustainability of one decision versus another (Anastas and Zimmerman 2003), and the problem of trying to predict future building performance during the design phase, when the ability to influence project cost is greatest, but when much of the other information about the final design is unavailable (Barrie and Paulson Jr. 1984). Another challenge is that of providing the designer with early feedback on the building s future performance. The building industry is also very fragmented (Chapman 1998), and the large number of systems and components that make up a building (e.g., structural, mechanical, electrical systems) and the complex interactions among these components make representation and simulation of a building in a single, integrated model very difficult (McGraw-Hill Construction 2007). Many of the sub-systems are designed, constructed, operated, and administered by separate entities that may or may not interact and share information (Howard et al. 1989; Singh and Dunn 2008). Interactions of building occupants with the various building systems add yet another layer of complexity to the building system (Fujii and Tanimoto 2004). Currently, most building models address individual systems (Hensen et al. 2002), including modeling programs that address a single aspect of building performance, such as Green Building Studio for energy analysis, Fluent for air flow modeling, CONTAM for indoor air quality, BEES for building product selection, SUNCAST for passive solar design, DAYSIM for daylighting analysis, ATHENA for environmental impact of structural materials, Watergy for water use and related energy use, and RETScreen for renewable energy and passive solar design. The U.S. Department of Energy lists 345 building software tools for energy analysis, alone 1. A major goal of the building simulation community is the integration of these types of models into a single building model, and the better incorporation of the results of these models into decision-making (Augenbroe and Hensen 2004), which is essential to designing a truly environmentally sustainable building. System dynamics is one tool with potential to make building sustainability decisions. It is a modeling method developed from systems thinking ideas (Forrester 1961). Systems thinking is a holistic approach to problem solving based on the General Systems Theory (von Bertalanffy 1968), a philosophy of science and engineering based on the idea of combining the knowledge gained through analysis and the understanding gained through synthesis to address root causes of problems (Caulfield and Maj 2001). The SD method applies and extends systems thinking concepts to construct computer simulation models, consisting of an interlocking set of differential algebraic equations developed from a broad spectrum of relevant measured and experiential data (Homer and Hirsch 2006) represented by a diagram, to examine system structure and the effects of altering key variables over time (SDS 2008). While systems thinking is a way of thinking about problems (Weinberg 1975), SD uses systems thinking principles to develop models to represent the problems. The system dynamics method has been used in a wide variety of applications, but its use in building design has been very limited (Thompson and Bank 2010). The SD modeling method is applicable to building system simulation because it is ideal for situations where the system to be modeled is extremely complex or highly dynamic (in time and/or in space) (Chritamara et al. 2002). Its focus is on the basic structure of the system, allowing for incorporation of soft factors that can help to capture human behavior of the building occupants (Caulfield and Maj 2001), and for other highly uncertain variables to be usefully included. Incorporation of these soft factors will be important in a building system model to address the occupants perceptions of and reactions to changes in a building s design and operation. These reactions will determine, to some extent, how building occupants behave (e.g., whether they keep the windows closed when the air conditioning is running). SD allows quantification of system behavior without necessarily requiring a high level of numerical accuracy in the model, as long as the model structure is well-defined (Forrester 1961). The SD method facilitates the search for leverage points through the use of sensitivity analyses (Randers 2000), and allows simulation experiments to be conducted on virtual buildings or retrofits (Chritamara et al. 2002). The main difficulties encountered in applying the SD method arise from difficulties in identifying truly dynamic feedback relationships within buildings systems. 1

4 Building Information Modeling (BIM) The Associated General Contractors of America (AGC) defines building information modeling, or BIM, as a datarich, object-oriented, intelligent and parametric digital representation of the facility, from which views and data appropriate to various users needs can be extracted and analyzed to generate information that can be used to make decisions and improve the process of delivering the facility (AGC 2006). BIM, however, is more than just the digital representation. It actually represents a shift in the traditional process of building delivery. This process shift is also known as Integrated Practice, or Integrated Project Delivery (AIA 2007), and is integral to the current industry trend towards fully integrated and automated project processes (Russell 2000). The National Building Information Modeling Standard (NBIMS) is being developed as a national standard for the use of BIM in building design and construction (NIBS 2007), the U.S. General Services Administration has begun to require BIM on its building projects (GSA 2006), and the AGC has developed The Contractor s Guide to Using BIM (AGC 2006). The states of Wisconsin and Texas now require BIM on most state projects (Blackwell 2009; Napier 2008) Efforts to standardize the practice of BIM are also underway within the U.S. armed forces, the European Commission, the Scandinavian countries, and Singapore (Fallon and Palmer 2007). The NBIMS discusses the role of interoperability, or seamless data exchange and sharing at the software level among diverse applications, each of which may have its own internal data structure as being essential in the building delivery process (NIBS 2007). The research described in this paper seeks to implement an interoperable operation for sustainable design and the BIM process, by integrating decision making with BIM design, visualization, and analysis. INTEGRATION OF BIM WITH A SD/DECISION-MAKING TOOL BIM can be used to improve decision-making in building design. An elementary means of using BIM to improve decision-making is simply to reduce the amount of work involved in evaluating multiple options early in the design process. Currently, it is possible to export BIM models more-or-less directly (and more-or-less completely) to third party software programs such as Green Building Studio or Ecotect for specialized analysis of energy use or daylighting, or to various structural analysis software programs. Revit MEP and the IES Virtual Environment program are capable of a two-directional interaction (Malin 2007). These program links provide information for use in decision-making, but do not provide any framework for actually making the decisions or for optimizing the design features used in these independent third party analyses. These programs also do not make the BIM model dynamic. When information is passed from the BIM model, the data is used to construct a model in the analysis software, run the analysis, and return results of the modeled performance of the design. If any optimization takes place in of these analysis programs, any modifications are not translated automatically into the BIM model. For example, Green Building Studio has a design advisor function that automatically makes suggestions to improve the design, but these are not dynamically integrated into the BIM model (Malin 2007). Other ways making a BIM model dynamic is a straightforward process using the BIM program API (application programming interface) for transferring data in two directions from and to a BIM model. This technique is used successfully to couple REVIT Structure with structural analysis and design software programs such as ETABS, RAM and RISA, where beam sizes can be updated, for example. Creating a two-way dynamic BIM-SD link could lead to more sustainable and costeffective buildings, and more streamlined, efficient, and thus cost-effective building design processes. These benefits can be achieved by creating a framework to make optimal decisions regarding sustainable design features early in the design process. The methodology employed in this study was to move data between a SD decision-making software model and a BIM software model. Once data has been transferred from the BIM model to the SD model, it can be used for making decisions regarding the building s design. Using appropriate sustainability indicators, these decisions may be optimized within the decision-making software to provide for a sustainable building. These decisions may then be actualized by returning the optimized building data to the BIM model, and modifying BIM components as appropriate. Specifically, this study addresses the creation of a direct link between the AnyLogic (XJ Technologies 2010) SD modeling program and AutoDesk s Revit Architecture (Autodesk Building Solutions 2008) BIM modeling software. This link would be used to automatically populate variables in the AnyLogic model with data from the Revit model, and to then update the Revit model by using results from the SD simulation to modify objects in the BIM model directly.

5 The first step was to identify the required inputs to the SD program that are based on the building geometry, or other building data (e.g., room occupant load, component cost, airflow, lighting specifications). This process began by identifying the highest level sustainability metrics (energy, water and material use) and then determining which building components (HVAC system, plumbing system, façade) contributed to each metric. Once inputs were identified, the next step was to incorporate these variables as parameters in the BIM model. The prototype link between the BIM and SD programs was accomplished through Java and Visual Basic applications written to take advantage of the programs APIs, as illustrated in Figure 1. In the figure, the square shapes represent commercial software, the white rectangles with rounded corners represent existing means of interfacing with these models, and the parallelograms represent the interim steps in moving data from one model to the other. VB/C# Translator Visual Basic/ C# External Application Revit API/ ODBC BIM Model Decision Model Java Applets IFC, CS/2, gbxml Figure 1. Schematic Relationship of BIM, API, Java, and Decision-Making (SD) Model The system described here is designed to allow data to flow in both directions. First, data may move from the BIM model to the SD decision-making model, to specify initial conditions in the SD model, and provide a realistic basis for decision-making. Second, the data may move from the SD model back to the BIM model, allowing objects in the BIM model to be updated automatically, based on decisions made using the SD model. In moving from the BIM model to the SD model, one of two procedures may be followed. In the first procedure, data may be output from the BIM model, via its API into a Visual Basic (VB) or C# external application. The next step, using a freely available software module, is to translate this VB or C# data into a Java format. Java applets are run as plug-ins to the AnyLogic software program, and the data may be incorporated into the SD model through these applets. In the second procedure, data may be exported from the BIM model in some standardized format, such as IFC (Industry Foundation Class) files, CIS/2 (CimSteel Integration Standards) files, or gbxml (green building extensible Markup Language) files. These files may then be directly translated to Java files using the translator module, which may be linked via the Java capabilities of AnyLogic to the decision-making model. Once BIM data has been integrated into the decision-making SD model, it may be used to populate the SD model with appropriate building data. This data is then combined with the structured decision model. Appropriate building sustainability indicators are then used as metrics to determine optimal combinations of building characteristics through sensitivity analyses completed within the SD software program. Other decision-making software and models may be used, but the SD modeling technique carries the added benefit of allowing users to choose sustainability indicators in a fashion important to the user. This flexibility could be used to choose indicators that simulate a LEED rating system, or any other rating system the designer chooses to investigate (e.g., BREEAM, Green Globes, Living Building Challenge). In addition, the various indicators may be weighted by the user to accommodate the priorities of the designer or owner, or to reflect the local environmental and climatic conditions in which the building is to be sited. Once an optimal arrangement of the project variables has been reached within the decision-making model, these decisions can be implemented in the BIM model by following the data-transfer steps in reverse order. Data representing the building characteristics may be exported from the SD model, via its Java applets. This data may

6 then be translated via the third-party Java-VB translator module into a VB file, which is then read in through the BIM program s API, allowing the changes made to building components in the decision-making model during the optimization process to be reflected in the geometry and parameters of the objects within the BIM model. Examples External decision-making models can be used to weigh any number of tradeoff scenarios with respect to different performance metrics in building design. Many parameters of a building can influence several different sustainable aspects of a building such as the energy consumed, water efficiency, material used etc. For example, the amount of concrete used in the structure will be involved in a tradeoff between the embodied energy used to construct the building and energy required to heat and cool the building in operation. More concrete in the structure will result in more material that will need to be extracted, manufactured and transported to the site but will also reduce the amount of space that needs to be heated and cooled as well as provide thermal mass and some insulation. The decision-making model can then optimize the thickness of the walls or floors to get optimal performance from the concrete with respect to the building sustainability metric, embodied versus operational energy use in this example, as determined by the designer. Figure 2 shows how the thickness of an exterior wall can be changed based on a decision-making model outside of the BIM program. On the left the exterior walls are highlighted and their properties are displayed, including the thickness of each component. The code sample in the center of the figure is used by the API to change the wall thickness based on external input from a decision-making model. After the change is made the properties of the wall are shown on the right of the figure, highlighting the increased thickness of the wall. The BIM model then updates its database based on the change. Figure 2. Example: Changing Wall Thickness Based on an External Decision-making Process Much of the data that would be used by a decision-making model to aid in design of a building is also data that is required to be calculated in order to apply for LEED credits. While LEED has its shortcomings, discussed below, one of its greatest contributions has been to encourage designers to consider and put more focus on the sustainability of the building and its different systems. LEED identifies the most important factors for designers to consider with respect to different sustainability metrics of a building and therefore the information needed in any decision-making model could also be used to calculate LEED points for a building during the design. One immediate benefit from this would be to reduce the time effort needed to calculate and apply for LEED credits which can be a time consuming activity for any design firm. When designers reuse building elements from project to project they could add parameters to these elements in their BIM models to store the data about that product that would be needed to perform LEED point calculations. This would make the LEED accreditation process more efficient for designers. A much more important long term benefit from this would be to change the design approach such that the

7 sustainability of the building in considered earlier in the design process, in much the same way that BIM has begun to change the building industry towards considering potential problems that were previously not addressed until well after the design was compete and construction had begun (for example, clash analysis). If the designer can see the LEED credits being accumulated during design then it can be used to drive decisions as well as other aspects of the building such as constructability or cost. This will allow designers to actually design the building to meet sustainability goals, whether specified by LEED or not. The current LEED points are not weighted perfectly based on the overall importance each point has with respect to the buildings sustainability, but as LEED evolves this will improve. LEED specifies many benchmarks that are required to achieve certain points, and although a designer may strive to achieve the specific point they often cannot be sure how close they are until the design is complete. This may result in several points being missed narrowly which may have been achieved with minor design changes, or several requirements being greatly exceeded to the point that the added benefit from the design contributes minimally to the overall building sustainability. This will become more useful as LEED evolves a point scale that better reflects the importance of each metric, possibly to the extent that it can become the driving decision-making model. Figure 3 shows an example of the parameters required to calculate the LEED credit for regionally produced materials. The credit requires that the distance from the project that the material was harvested and manufactured is known as well as the percentage of the material that was harvested and manufactured within a 500 mile distance. On the left is an API code segment that is used to add parameters to different components in a BIM model. This can be done for all families (walls, doors, windows, etc) in a model. On the right the properties of a door are displayed showing the newly added parameters under Green Building Properties required to calculate the credit. Figure 3. Example of LEED Parameters Added to a BIM Model CONCLUSIONS While unquestionably having a positive impact in the building industry, LEED (and similarly many of the other rating systems) faces three important challenges at this time. First, while the theory would have building owners and designers addressing LEED points that represent the greatest environmental impact or the most importance to them, the reality of the situation may be that many designers address the lowest-cost points first, in an attempt to achieve LEED certification for the lowest total cost, with less regard for actual environmental benefit (Navarro 2009). Second, further difficulties arise from the fact that LEED addresses a large number of different and conflicting aspects of the sustainability performance of buildings, creating the possibility that one aspect will be

8 neglected in favor of others, while a LEED rating can imply to those not involved in the certification process a high level of performance in all aspects of sustainability performance (Nature 2009). Finally, LEED has historically based its ratings on predicted performance, often resulting in a gap between the predicted (rated) performance and the actual performance of the constructed building (Navarro 2009). Improvements in these three challenge areas lie in the development of decision-making tools for sustainable building design. Decision tools developed using appropriate sustainability indicators will allow LEED points to be chosen on a basis other than lowest cost, and will assist in integrating incommensurable performance indicators. Basing the LEED analysis during design directly on an accurate BIM will lead to predictions that should more closely approximate actual performance, and will minimize inconsistencies that can occur when creating multiple models of the same building. Incorporation of LEED into a BIM model will not only help to facilitate these improvements, but will also allow design professionals to improve their processes, which may help to reduce the costs of sustainable, LEED-certified projects. Reduced costs for sustainable buildings will encourage more owners to build such buildings, while at the same time the decision-model linked to the BIM model will make these buildings even more optimally sustainable than current LEED projects. The software application described above for LEED credits within a BIM model is a model for developing LEED calculations within the BIM model, which will be the basis for alternative evaluation and optimization within the decision-making tool. As the decision model evolves, it will be possible to incorporate more and more advanced sustainability criteria in the decision model, moving first to more stringent rating systems, such as the Living Building Challenge, and eventually to develop a deeper sustainability rating system. These deeper sustainability decision criteria will be available to the BIM model through the same interactive process described above. ACKNOWLEDGEMENTS Support from the University of Wisconsin Industrial and Economic Development Research (IEDR) program is gratefully acknowledged. Bret Tushaus, Director of Information Technology at Eppstein Uhen Architects, Inc. (EUA) and colleagues are thanked for access to their BIM models and LEED certification documentation as well as for stimulating and interesting discussions. REFERENCES AGC. (2006). The Contractor's Guide to BIM, Associated General Contractors of America, Arlington, VA. AIA. (2007). "Integrated Project Delivery: A Working Definition." American Institute of Architects California Council, Sacramento, CA. Anastas, P. T., and Zimmerman, J. B. (2003). "Design Through the 12 Principles of Green Engineering." Environmental Science and Technology (Mar. 1), ASTM. (2005). ASTM E : Standard Guide for General Principles of Sustainability Relative to Buildings, ASTM International, West Conshohocken, PA. Augenbroe, G., and Hensen, J. (2004). "Simulation for Better Building Design." Building and Environment, 39(8), Autodesk Building Solutions. (2008). "Revit Architecture." Autodesk, San Rafael, CA. Barrie, D. S., and Paulson Jr., B. C. (1984). Professional Construction Management, McGraw-Hill Book Company, New York. Berge, B. (2000). The Ecology of Building Materials, F. Henley, translator, Architectural Press, Burlington, MA. Blackwell, M. (2009). "Texas Adopts Building Information Modeling (BIM) capability for State Design and Construction Projects." Texas Facilities Commission, Austin, TX. BOMA. (2009). "BOMA BESt." < (accessed Jul. 1, 2010). Building Owners and Managers Association, Toronto, CA. Brahme, R., Mahdavi, A., Lam, K. P., and Gupta, S. (2001). "Complex Building Performance Analysis in the Early Stages of Design." Seventh International IBPSA Conference, Rio de Janeiro, Brazil, Brass, L. (2007). "A Glimpse of the Energy Future." Oak Ridge National Laboratory Review, 40(2), 2-7. BREEAM. (2009). "BREEAM: the Environmental Assessment Method for Buildings Around The World." < (accessed Jul. 1, 2010). BRE Global, Garston, UK.

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