CIFE Center for Integrated Facility Engineering •Stanford University
CIFE Seed Proposal Summary Page
2007-2008 Projects
Proposal Title: Strategic Integration and Automation of Conceptual
Design
Principal Investigator(s): John Haymaker, Kincho Law
Research Staff: Forest Flager, Alex Shkolnik, Victor Gane
Proposal Number: (Assigned by CIFE): 200706
Total Funds Requested: $125,000.00
First Submission?
Yes If extension, project URL:
Abstract (up to 150 words):
The life-cycle economic and environmental performance of buildings is substantially determined
in the early stages of the design process. Performance-based analysis methods supported by
product models have little opportunity to inform these early stage design decisions because
current tools and processes do not support the rapid generation and analysis of alternatives. The
goal of this research is to reduce the time required to complete such design iteration. We
anticipate that this will allow design teams to formally investigate the performance of many more
alternatives during the conceptual design phase leading to improved product performance. To
this end, we propose to (1) develop a framework to measure the effectiveness of multidisciplinary
design (MDD) methodologies using time as the unit of analysis; (2) implement methods and
technologies developed in the aerospace industry including parametric design, automated
discipline analysis and process integration; and (3) measure the effectiveness of these new
methodologies using the described framework.
Haymaker, Law
Strategic Integration and Automation of Conceptual Design
1
1
Observed Problem
Advancements in computer-based Building Information Modeling (BIM) and analysis methods
now allow architects and engineers to simulate building performance in a virtual environment. The
number of performance criteria which can be analyzed from product models now includes to
some extent architectural, structural, mechanical (energy), acoustical, lighting and an expanding
list of other concerns [Fischer 2006]. Consequently, performance-based design supported by
product models is becoming state-of-the-art practice [Hänninen 2006]. However, the potential of
this technology to inform the early stages of the design process has not been fully realized
because current tools and processes do not support the rapid generation and evaluation of
alternatives.
The amount of time required to generate and evaluate a design option using model-based
methods means that very few, if any, options can be adequately studied during the conceptual
design phase before a decision must be taken. Often engineers resort to only using model-based
methods to validate a chosen design option, rather than to rigorously explore alternatives. The
inability to quickly generate multiple options and to rigorously analyze them from the perspective
of multiple disciplines invariably leaves unexplored a broad area of the design space. The
unexplored regions of the design space - different building orientations, massing, internal layouts
and combinations of systems (i.e. structural and mechanical) - potentially may contain better
performing building solutions than anything previously considered [Shea et. al. 2005].
The goal of this research is to identify and test methods that reduce the time required for
architects and multidisciplinary engineers to complete a design iteration, which involves the
generation and evaluation of a given design option using model-based methods. The current
AEC process requires a significant amount of professionals’ time and the duplication of effort to
integrate, communicate, and coordinate information in order to complete design iteration.
Following is our diagnosis of the problem to be addressed by this research.
1.1 Benchmarking the Current AEC Process
Process Description
The conceptual design process can be characterized by four iterative steps (Fig. 1): (A) the
architect creates a design option based on perceived stakeholder requirements and, depending
on the project, engineering heuristics. (B) The architectural team represents the option in the form
of sketches, 2-D drawing and/or a 3-D CAD model to communicate with the project team. (C) The
engineering them then spends a significant amount of time integrating this information in order to
construct discipline-specific analysis models to simulate the behavior of a particular building
system. The analytic results are then used by the engineering team to complete the initial design
of their respective building systems which are each, in turn, communicated to the rest of the
design team in the form of sketches, 2-D architectural drawing and/or a 3-D CAD model. (D)
Finally, the design team conducts meetings to ensure that the building systems are coordinated
and are consistent with the architectural concept. The coordination process is also labor
intensive and typically focuses on resolving conflicts so as to reach a feasible design option
rather than optimizing the performance of the building as a whole.
Haymaker, Law
Strategic Integration and Automation of Conceptual Design
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Figure 1: Diagram of the current building design process
Process Metrics
To assess the effectiveness of the current process and provide a baseline for future research
using time as the unit of analysis, we conducted a survey of architects and multidisciplinary
engineers at a leading practice1. The goal of the survey was to determine (1) approximately how
many design iterations are possible within a standard project timeline and how long iteration
customarily takes as well as (2) the relative amount of time spent on key process tasks defined in
Figure 2. To provide a framework for analysis, these tasks were then classified into three basic
categories – strategy and specification, execution, and management.
The following working definitions were given to those surveyed:
 Design option: A particular configuration of the following variables: building orientation,
massing and system types (e.g. structural – steel framing, mechanical – radiant floor system).
Changes to one of more of these variables constitute a distinct design option.
 Design iteration: The generation and analysis of a single design option using model-based
methods (Figure 1: steps A-D). The level of information required to demonstrate the
feasibility of an option was set to a common industry milestone known as “25% Design
Development (DD)”, which includes the preparation of architectural drawings, the selection of
building systems and the preliminary positioning and sizing of system component
The results of the survey are shown in Figure 3. These results suggest that architects and
engineers spend the majority of their time managing design information (58%) and relatively less
time executing (36%) and specifying (6%) this information.
1
Survey results were obtained in February, 2007 from 50 design professionals (5 architects, 45
multidisciplinary engineers) working at Ove Arup and Partners (www.arup.com) in San Francisco, USA
and London, England.
Haymaker, Law
Strategic Integration and Automation of Conceptual Design
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Strategy and
Specification
Multidisciplinary Design Tasks
Tasks
(Processes)
Establishing an overall design strategy by determining the
required tasks, deliverables and responsible personnel.
Schemas
(Information)
Establishing the overall implementation strategy by determining
what tools will be used to accomplish each task and the content
and format of information to be exchanged
Execute
Integrate
Management
Definition
Communicate
Coordinate
The activities directly pertaining to generating and analyzing
design options and performing analyses (e.g. creating new
geometric features, performing analyses and interpreting
results).
The activities pertaining to the exchange of information between
tasks (e.g. converting and ensuring accuracy of input and output
information to/from a specified format).
The activities pertaining to exchange of information between
persons responsible for the tasks (e.g. communicating the
nature of your task and local schema).
The activities pertaining to detection and resolution of conflicts
and management of changes (e.g. detecting space conflicts
between systems, propagating a local change throughout the
entire design).
Figure 2: Framework for measuring process effectiveness
Relative time spent on design tasks by category
Management
(8%)
6%
Specification
(6%)
Number and duration of design iterations
58%
Initial
Subsequent
(avg.)
7 weeks
5 weeks
Average Number
of Iterations Per
Project
36%
Management
Specification
Execution
Duration of Design
Iterations
2.8
Execution
(36%)
Figure 3: AEC design process metrics
Haymaker, Law
Strategic Integration and Automation of Conceptual Design
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1.2 Problem Summary and Intuition
Conceptual design decisions have a significant impact of the life-cycle economic and
environmental performance of buildings. Performance based analysis methods supported by
product models have little opportunity to influence these early stage design decisions due to
schedule limitations. According to our initial survey it takes architects and engineers over one
month to generate and analyze a design option using product models and, typically, less than
three such iterations are completed during the conceptual design phase.
This appears to be due to a collection of tool and process limitations. Part of the problem is that
designers’ tools are intended to generate static design options rather than help them define and
explore solution spaces. Another problem is that when information is produced, little
consideration is given as to how to represent that information to facilitate multidisciplinary
analysis. Many have written about the difficulties of tools used by different disciplines to share
data effectively [i.e., Gallaher 2004]. As a result, design professionals appear to be spending less
than half of their time doing work directly related to design and analysis. The majority of this time
is spent managing design information, including manually integrating and representing this
information in their task-specific format, and coordinating their solutions (Fig. 2). These
limitations prevent a more complete and systematic exploration of the design space based on
multidisciplinary model-based performance analysis.
The aerospace industry is in the process of overcoming a similar set of limitations by adopting a
suite of technologies and methodologies to support multidisciplinary analysis (MDA) using
product models, among them parametric geometry definition, integrated design schemas,
automated discipline analysis and multidisciplinary optimization leading to improved process and
product performance [Flager and Haymaker, 2007]. Our intuition is that these methods and
technologies can be adopted by AEC design teams to significantly reduce the time required to
generate and analyze a design option using model-based methods. Reducing the design
iteration time will allow architects and engineers to formally investigate the performance of many
more design alternatives within the current project timeline than is currently possible. This work
has the potential to improve building performance in terms of initial cost, energy performance and
overall quality.
Figure 5: Results of an MDO process in which Boeing optimized the shape of a hypersonic
vehicle. Each point in the graph represents a unique design. The desirable designs have a
relatively low take off gross weight (TOGW) ratio and a positive excess propellant fraction 2
2
Image courtesy of Geojoe Kuruvila, Associate Technical Fellow, The Boeing Company
Haymaker, Law
Strategic Integration and Automation of Conceptual Design
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2
Theoretical Points of Departure
In this section we first describe the fundamental points of departure and their limitations which we
propose to build upon with our research. We then describe some relationships of our research to
other emerging methodologies which this proposal will contribute to in the future.
2.1 Fundamental Points of Departure
Parametric computer-aided design (CAD) is a design methodology used to create and manage
geometric dependencies within a model. The use of parametric tools can enable designers to
shift from creators of single designs to designers of systems of inputs and outputs that generate
design spaces. Parametric tools are beginning to be actively used in academia and industry [i.e.,
Burry 2000]; however, the extent to which parametric tools have been used to support modelbased MDA processes for conceptual design has been limited by a lack of integration with
analysis processes. Further work is needed to determine if the appropriate geometric
dependencies can be identified and captured within a parametric model, and if the necessary
analysis representations can be defined in advance for a range of options.
BPEL, XML and IFC are task and schema specifications that provide standard ways to define
processes and information. Business Process Execution Language (BPEL) is an engine for
specifying processes, coordinating communication between services and parties, implementing
parallel processing of activities, manipulating data between partner interactions and providing
consistent exception handling [Cobban, M. 2004]. EXtensible Markup Language (XML), is a set of
rules for designing text formats to structure information. Several industry-specific sets of rules of
XML-based schemas are currently being developed (i.e. aecXML, gbXML, ecoXML) but none
have emerged to gain wide industry acceptance. Industry Foundation Classes (IFC) is an object
oriented data model used to describe the relationships, and properties of building specific objects.
To date its industry implementation is limited due to gaps in capturing the entire extent of AEC
information and the lack of software systems that support it. In summary, a wide variety of data
specific formats are available to enable interoperability which can be customized to process
specific needs, but more research is needed to establish how to apply these standards to
conceptual building design
Process Integration and Design Optimization (PIDO), an emerging line of software products
that aims to give users the ability to integrate processes that utilize multiple digital design and
analysis tools. These products allow software tools to be “wrapped” and published on a
computing network. This allows disciplines to keep ownership of their codes, maintain and
upgrade them, and serve them from their preferred computing platform. PIDO tools also provide
a graphical environment which permits users to select published components and graphically link
their inputs and outputs as required to create an integrated MDA model. Among limitations of
PIDO tools are the lack of support of various process components and a narrow problem focus
that does not explicitly address communication and coordination issues. Very little work has been
done to date to test the effectiveness of these frameworks in the AEC domain.
Narratives [Haymaker, J. et. al. 2004] provide a means to describe and communicate the design
process using an acyclic graph structure. Each node in the graph corresponds to a defined
reasoning process which operates on designated inputs and outputs. Narratives help AEC
professionals communicate multidisciplinary design processes and the information models used
in these processes. However, Narratives do not explicitly facilitate the exchange or coordination
of information for the described process.
Haymaker, Law
Strategic Integration and Automation of Conceptual Design
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2.2 Relationship to other Methodologies
Product Lifecycle Management (PLM) is an approach within systems engineering that takes a
product from early conceptual stages all the way through manufacturing, maintenance and
disposal. In industry PLM solutions allow collaborative creation, secure management and use of
product definition information, support customers and supply partners as well as integrate people,
processes, business systems and information [CIMdata, 2007]. The complicated nature of
product design, manufacturing and supply within an evolving global market has forced PLM
systems to develop technologies to help define, execute, measure, and manage key productrelated business processes and workflows. PLM has begun including Business Process
Management (BPM) solutions, [McClellan, M., Harrison-Broninski, K. 2006]. As the nature of PLM
is adapting to fast changing and dispersed product development practices its solutions come
closer to addressing problems that characterize the AEC industry.
Semantic Information Exchange. While BPM does offer solutions to organization and workflow
management, an alternate, semantic vision persists in academia and includes KIF, the knowledge
interchange format, PIF, the process interchange format and PSL, the process specification
language. These logic based approaches see machine interpretation and reasoning as being an
integral part of process control and management. One application of such an approach within
AEC is detailed in a PhD thesis entitled “A Distributed Problem Solving Approach to Collaborative
Facility Engineering Through Agent-Based Software Integration” [Khedro, T., 1994]. The
dissertation presents a framework for integration of facility design and engineering software
applications based on the Agent-Based Software Engineering approach [Genesereth, M. R.,
1992]. The framework offers a distributed and automated exchange of design information,
knowledge and constraints based on KIF and organizational structuring of designers with conflict
negotiation strategies. This approach, however, has not been validated in real world AEC
projects and was developed with mostly theoretical considerations in mind.
The meeting of PLM and BPM worlds and the semantic vision of information exchange can prove
to be a foundation for emergence of new ideas which can be mapped to AEC practice. Our
research, which includes understanding current AEC practice and developing an analysis
framework to evaluate how methodologies impact multi-disciplinary design, serves as the first
step to establishing which of these ideas are applicable to AEC.
3
Research Questions





4
How to measure the effectiveness of conceptual MDA processes?
What is the current conceptual MDA process and how well does it perform?
How can model-based design and analysis information be constructed more effectively?
How can model-based design and analysis information be exchanged more effectively?
How can model-based design and analysis information be managed more effectively?
Research Methods
Our research methods can be broken down into two concurrent parts, one dealing with strategy
and problem exploration and the second dealing with implementation and testing. Part one
consists of three stages: (1a) evaluation of two current MDA processes, (1b) development of a
framework to measure methodology effectiveness and (1c) exploration of data schema
interoperability. Part two, the implementation and testing, consists of two stages: (2a)
incorporation of parametric modeling into current MDA processes and (2b) process integration
and automation. We also will measure the effectiveness of each of our proposed interventions to
Haymaker, Law
Strategic Integration and Automation of Conceptual Design
7
current practice and document detailed comparison studies. We describe all of the stages in
detail below.
1a. Evaluate current MDA processes: We will evaluate the MDA processes of two leading AE
firms: SOM and Arup. Each firm has multiple offices in North America, Europe and Asia. These
firms represent a reasonable diversity in terms of organization structure and domain of focus.
Arup is an Engineering firm that is usually a consultant for an architect, whereas SOM hires
engineering consultants, and sometimes uses their own multidisciplinary practice. At SOM, we
will document the San Francisco Trans Bay or similar high-rise project. At Arup, we will observe
the Stanford Graduate School of Business project. Our evaluation will be conducted through
direct interviews of project architects, engineers and managers.
1b. Development of our framework: Relying on work done within the areas of systems
engineering, workflow management and AEC we will continue to expand our framework to
measure methodology effectiveness (see Figure 2) to precisely characterize the challenges faced
by teams of multidisciplinary professionals on AEC projects. In addition to using this framework
to assess the methodologies we will implement as part of this proposal, Methodologies found
within AE and parallel industry and academia will be speculatively evaluated within our
framework. These speculative predictions will be used to propose future work and formulate
specific problems that address challenges faced within AE design.
1c. Explore data schema interoperability: The directed interviews described above will also be
used to identify the existing structure and format of model-based information that is exchanged on
the selected projects. This research will be coupled with literature review of data schemas
currently researched in the AEC and parallel industries. This information will be studied to
recommend an existing or infer a new data schema to facilitate interoperability. We will then
specify the format and structure of information to be exchanged for the selected project. This
process will resolve the information transfer protocols between parametric tools such as Digital
Project or CATIA and analysis tools (i.e. ETABS - structural, ECOTECT - Incident solar radiation,
Flovent - CFD, etc).
2a. Incorporate parametric modeling into MDA processes: We will formalize geometric
dependencies between design and analysis representations and automate this reasoning using
parametric tools. This model is intended to test if geometric dependencies can be defined to
generate a feasible solution space, and if the necessary analysis representations can be defined
in advance and the dependencies captured in the model such that engineering analysis geometry
can be created in parallel with the architectural model. We will implement the described
parametric models and compare the results to current practice.
2b. Process Integration and Automation: Two phases will be considered. Both will rely on our
study of data schema interoperability (see above) as we will select the data schema most
appropriate to our modeled process. The first phase entails automating the data extraction from
the created parametric models for use within analysis software and other tools. The second
involves using commercial PIDO software (Phoenix Integration, ESTECO, Engineous software) to
implement the modeling and analyses portions of the evaluated MDA processes. An exploration
of the design space will be conducted with resulting design performance improvements
documented.
Haymaker, Law
Strategic Integration and Automation of Conceptual Design
8
5
Research Impact
5.1 Contribution to Research
The proposed research will document the implementation of methodologies and technologies
developed in the aerospace industry to support multidisciplinary design processes on AEC
projects, among them parametric geometry definition, automated discipline analysis and
multidisciplinary optimization (MDO). We will provide a framework to scientifically assess the
proposed methodologies compared to current AEC practice using time as the unit of analysis.
We anticipate that this research will lead to an improved understanding of both the current
AEC design process as well as the potential benefits of the proposed methodologies to
AEC.
5.2 Contribution to Professional Practice
The goal of this research is to reduce the amount of time required to generate and evaluate a
design option using model-based methods. Reducing the simulation cycle time will allow
architects and engineers to formally investigate the performance of many more design
alternatives within the current project timeline than is currently possible. This work has the
potential to improve building performance in terms of initial cost, sustainability and overall
quality.
6
Funding, Schedule and Deliverables
6.1 Funding
We seek funding for 2 - 50 % Research Assistantships for 3 quarters and for one programmer for
this period of time for a total of $125,000. Forest Flager’s participation in this research is covered
by Stanford Graduate Fellowship (SGF) funding. We will use this work to formulate a proposal to
the National Science Foundation (NSF) and to seek additional funding from our industry partners,
Arup and SOM, who have agreed to dedicate their time and provide access to current projects
which will serve as case studies for our research.
6.2 Schedule and Deliverables
Quarter 1: (1) Evaluate current design AEC processes based on directed interviews with Arup
and SOM staff. Forest Flager has already worked for Arup (London and San Francisco) for
approximately three years, and Victor Gane has worked for SOM (Chicago and San Francisco)
for over two years. Both are ideally situated to collect data on ongoing projects. Alex Shkolnik will
formalize the framework categories along with task-specific metrics and provide evaluation
representations. The deliverables are:
 Narrative (process diagram): We will develop detailed Narratives to document the
people, tools, reasoning, information used, and information constructed at each step in
the process.
 Measure of current MDA practice: Documented initial framework together with data
representing our findings of challenges within current MDA practice.
(2) Explore data schema interoperability to identify and document the most compatible
methodology to exchange information between design generation and design analyses type
processes (tasks) which were identified in the evaluation of current MDA. Alex Shkolnik will
perform this study with input from Forest Flager and Victor Gane. The deliverables include a
survey of effective approaches for interoperability compatible with considered processes. The
survey will document advantages and disadvantages of the approaches reviewed.
Haymaker, Law
Strategic Integration and Automation of Conceptual Design
9
(3) Implement a design space exploration study using Process Integration and Design
Optimization (PIDO) software for a small subset of one of our AEC projects.
Quarter 2: (1) Complete all parametric modeling required for the SOM and Arup processes.
Victor Gane will lead this effort with Forest Flager and Alex Shkolnik participating. Each
contributor has an extensive parametric modeling background.
(2) Implement the data schema selected during Quarter 1 ensuring information flow at each node
(process) within the Narrative developed during Quarter 1. Forest Flager will head this effort with
technical aspects implemented by Alex Shkolnik. Victor Gane will also provide his expertise with
node (process) specific requirements. The deliverables for this step are two non-automated but
integrated processes simulating SOM and Arup design generation and analysis tasks with the
intervention of parametric technology and our chosen data schema approach.
(3) Use our framework to evaluate the impact of parametric techniques and our chosen data
schema. The deliverables are the documentation of the two evaluations and a discussion of the
advantages and disadvantages of both.
Quarter 3: (1) The final quarter will be spent implementing the two processes in a PIDO software.
The deliverables are two integrated and automated processes simulating SOM and Arup design
generation and analysis tasks. Forest Flager and Alex Shkolnik will head this effort with Forest
Flager providing engineering expertise and Alex Shkonik providing technical assistance and
optimization expertise.
(2) Arrive at a fully developed framework for evaluating methodology effectiveness and go
through an evaluation the impact of PIDO methodologies within MDA practice.
(3) During this quarter the final framework will be used to collect evaluation of methodologies from
AEC and parallel industry and academia (see Points of Departure: Relationship to other
Methodologies Section). Alex Shkonik will conduct this study delivering documented evaluations
and a discussion of the mapping of specific methodologies from AEC and parallel industry and
academia.
References
Burry, M.C. (2003) “Between Intuition and Process: Parametric Design and Rapid Prototyping”. In Branko Koarevic (Ed.).
Architecture in the Digital Age, Spon Press, London.
Cobban, M. (2004). “What is BPEL and why is it so important to my business?”. SoftCare EC, Inc
CIMdata, Inc. provides worldwide strategic Product Lifecycle Management (PLM) consulting and program support, indepth research, and education for both industrial organizations and suppliers of technologies and services
seeking competitive advantage in the global economy.
Gallaher, M. P., et. al. (2004). “Cost Analysis of Inadequate Interoperability in the U.S. Capital Facilities Industry”.
Technical Report GRC 04-867, National Institute of Standards and Technology (NIST), Gaithersburg, MD.
Genesereth, M. R., (1992). “An Agent-Based Framework for Software Interoperability”. Proceedings of DARPA Software
Technology Conference, Pages 359-366.
Fischer, M. (2006). “Formalizing Construction Knowledge for Concurrent Performance-Based Design”. In Smith, I. (Ed.).
Intelligent Computing in Engineering and Architecture. Springer, New York, NY, pp. 186-205
Flager, F., Haymaker, J. (2007). “A Comparison of Multidisciplinary Design, Analysis and Optimization Processes in the
Building Construction and Aerospace Industries”. EG-ICE conference in Maribor, Slovenia, June 27-29.
Hänninen, R. (2006). “Building Lifecycle Performance Management and Integrated Design Processes: How to Benefit
from Building Information Models and Interoperability in Performance Management”. Invited presentation,
Watson Seminar Series, Stanford University.
Haymaker, J., et. al. (2004). “Engineering test cases to motivate the formalization of an AEC project model as a directed
acyclic graph of views and dependencies,” ITcon Vol. 9.
Khedro, T., (1994). “A Distributed Problem Solving Approach To Collaborative Facility Engineering Through Agent-Based
Software Integration”. PhD Thesis, Stanford University.
McClellan, M., Harrison-Broninski, K. (2006). “Product Meets Process”. CPM Media LLC.
Shea, K., Aish, R., and Gourtovaia, M. (2005). “Towards Integrated Performance-driven Generative Design Tools.” In
Automation in Construction 14(2), pp. 253-264
Haymaker, Law
Strategic Integration and Automation of Conceptual Design
10
6.3 Itemized Budget
Project: CEE-FY07-481 Assessment of MDA - CIFE proposal for 2007-2008
Department: Civil Engineering
Principal Investigator: HAYMAKER, JOHN (Asst Prof) - CE
Administrator: L. Unerdem
Period 1 All Periods
10/01/07 - 9/30/2008 10/01/07 - 09/30/08
%
Amount
Total Amount
Senior Personnel
Haymaker, John (Asst Prof)
Law, Kincho Prof)
Graduate Students
2007, RA - Post Quals, Grad (Res Asst)
2007, RA - Post Quals, Grad (Res Asst)
Contingent Staff
CEE-for Prof. Haymaker, Programmer (Programmer)
Total Salaries
Benefits
Faculty
Graduate
Contingent
Total Salaries and Benefits
Tuition
Tuition for 2 50% PhD Students (3 quarters)
Total Direct Costs
Modified Total Direct Costs
University IDC Costs
Total IDC Costs
Annual Amount Requested
acad
smmr
acad
smmr
1
1
1
1
1,082
360
2,000
677
1,082
360
2,000
677
acad
smmr
acad
smmr
50
0
50
0
22,248
0
22,248
0
22,248
0
22,248
0
99.7
40,000
85,938
40,000
85,938
428
1,691
3,360
91,417
428
1,691
3,360
91,417
30,906
122,323
91,417
30,906
122,323
91,417
125,000
125,000
cal
Rates Used in Budget Calculations
Benefit Rates
Faculty:
UFY08 29.70%; UFY09 29.70%
Graduate:
UFY08 03.80%; UFY09 03.80%
Contingent:
UFY08 08.40%; UFY09 08.40%
Indirect Cost Rate
Special Rate:
UFY08 00.00%; UFY09 00.00%
The budgeted salary amount is comprised of the direct effort for the project plus 8.65% vacation
accrual/disability sick leave (DSL) for exempt employees and 7.35% for non-exempt employees. These
amounts do not exceed total salary. The vacation accrual/DSL rates will be charged at the time of
the salary expenditure. No net salary will be charged when the employee is on vacation, disability
or worker’s compensation.
Haymaker, Law
Strategic Integration and Automation of Conceptual Design
11