Ethan Heil, Graduate Student in Civil & Environmental Engineering at Stanford University Sustainable Urban Systems Symposium Stanford University, June 2016

1. Promoting green, affordable housingEthan Heil // eheil@stanford.edu Civil + Environmental Engineering // Stanford University
ABSTRACT
LIFE CYCLE METRICS
ANALYZING THE DESIGN SPACE
The following variables were used to define the design space:
ACKNOWLEDGEMENTS
This research was inspired and enabled by the previous work of John Basbagill, PhD who has been a constant
source of advice and mentoring. Assistant Professor Michael Lepech, PhD has provided instrumental support and
feedback as the main advisor to this research. Bill Moffett, CM provided an invaluable link to professional practice
in the Architecture, Engineering and Construction industry and connected this research to the case study
presented. Kacey Callinan has also been an encouraging collaborator, sharing her unique experience and data.
Additional support was provided by the National Science Foundation Graduate Research Fellowship.
Each design decision (or combination of decisions) can be evaluated
based on potential contributions to life cycle costs and impacts. The
following comparisons illustrate how this analysis can inform decision-
making during the early, conceptual design stage
Construction costs
(material + labor costs)
Maintenance costs
Replacement costs
Utility costs
Construction material impacts
(embodied carbon emissions)
Replacement material impacts
(embodied carbon)
Utility-related emissions
Initial Phase Operational Phase
CostsImpacts
MODEL FRAMEWORK
This research aims to evaluate the long term cost and environmental
impacts of affordable, low-income housing. The motivation behind this
research is to develop a tool that will provide designers with feedback
regarding how design decisions affect life cycle costs and environmental
impacts.
As a means of providing this feedback, a model was developed to analyze
the potential design space available to architects during the early design
stage. This model leverages parametric design and life cycle assessment
techniques to systematically evaluate a design space of approximately
1024 unique designs. The results of this analysis allow potential design
choices to be assessed based on their life cycle costs and environmental
impacts. This enables designers to make more informed decisions at an
earlier point in the design process where this type of feedback has
historically been unavailable.
The evaluation of an affordable housing case study in San Francisco
highlights the tradeoffs that take place when designers seek to optimize
the disparate objectives of minimal life cycle cost and minimal life cycle
environmental impacts (as represented by total greenhouse gas
emissions). These results also provide examples of design alternatives
that could reduce both costs and impacts, leading to overall greener,
more affordable buildings.
Constant inputs remain unchanged
throughout the analysis and reflect
the site characteristics of a 6,200
square meter affordable housing
case study based in San Francisco,
CA. Parametric inputs are varied
using an automated design space
exploration routine. 100,000 unique
building designs were parametrically
defined and evaluated. The life cycle
environmental impacts and life cycle
costs of each of these designs are
presented in the graph to the left.
Although buildings with stone floors exhibit lower life cycle impacts than
buildings with carpet (left), they also reflect significantly higher life cycle
costs (right). A designer must therefore weigh the tradeoffs between
greener, less impactful materials and higher costs.
The figures above illustrate the distribution of life cycle impacts and costs
associated with cladding design choices. Stucco (red) exhibits significantly
lower impact (left) and slightly lower cost (right) profiles compared to
brick (blue). As such, stucco represents a greener design choice than brick
at no additional economic cost.
Constant inputs
Site variables
Parametric inputs
Geometric design variables
Parametric inputs
Component selection variables
Location (climate)
Service life
Program
Unit type
family or elderly
Unit size
Number of units
Substructure sizing
Utility metrics
Orientation
Building shape
Length
Width
Number of floors
Window-to-wall ratio (WWR)
Shading
Presence of overhangs
Overhang projection factor
Substructure system
Floor structure
Roof structure
Columns and beams
Cladding
Windows
Overhang material
Doors
Walls
Wall insulation
Wall finishes
Floor insulation
Floor finishes
ENABLING DESIGN DECISIONS
Stone floors
Carpet floors
Population average
A method for automated, parametric analysis of life cycle costs and
impacts was developed as illustrated below:
CladdingMaterialFlooringMaterial
Brick siding
Stucco siding
Population average