Download Abstract: The purpose of this research is to display trends that exist

Abstract:
The purpose of this research is to display trends that exist between the cost per square
foot and the thermal conductivity of certain materials commonly used in building.
By plotting the costs and thermal conductivities of building materials, a correlation
between the data points was found to exist around 5 cents per square foot and .05 W/mK thermal
conduction by material.
Since more than 3/5 of the data points occur within a certain zone, it appears that building
materials that occur within this zone have an optimized value between cost and thermal
conductivity. Using this “optimized zone”, materials considered as viable in construction can be
considered more cost- and energy-efficient than others.
Background Research:
Data has been previously generated for the thermal conductivities of many building
materials, and building materials are common enough to gauge the average price of a sample.
Using this data, a relationship between the two properties should be able to be established.
The ratio of materials used in building houses also needed to be taken into account, as the
different thermal conductivities of the materials would cause different amounts of lost heat by
the amount that the materials were used in the building. Through analysis of several data sources,
a reasonable approximation could be created to fit the necessary data. Calculations regarding that
data also were required, and through these methods a sum of data was created to represent an
average household.
However, it must be noted that a certain amount of approximation and assumption
occurred during this stage, so all values may not be entirely accurate. This data proved ultimately
not substantially impactful on the result, but for precise results calculations of this nature are
needed.
Question:
What is the relation between the thermal conductivity and cost of common building materials?
Materials:
Data sourced from various sites on the internet, Microsoft Excel, Inventor.
Procedures:
Research on the cost and thermal properties of building materials provides the data
required to properly analyze the question, and research on the average composition of a house
provided the necessary information for the calculations performed.
Results:
The most common building materials not only were similar in thermal resistance, but
were also similar in price. Because of the data cluster that became evident, a range of optimal
values for the most thermal and cost efficient materials (which includes almost all building
materials analyzed) was established.
Hypothesis:
By comparing the ranges of thermal resistance with the prices of certain building materials, there
should exist a certain material set commonly used in the construction of houses that has the
highest ratio of energy efficiency to cost.
Conclusion:
The data, when plotted on a graph, seemed to contain a large number of sets within a
certain area of the graph. Because of this data cluster, which indicates not only thermal similarity
but also cost similarity, exists, it means that there should be a zone of optimization, where both
costs and thermal conductivity is low. It is understandable that the materials most used in
building over the course of many centuries would have a similar thermal conductivity. What is
slightly more surprising is that most of these materials have a similar cost value, despite having
widely varied procedures (between materials) to make said materials.
Because the data exhibits this “Optimized Zone” where cost to thermal conductivity is
the best, this range can also be applied to other materials that could be used in building. If that
material’s thermal conductivity and cost per square foot resides within the Optimized Zone, then
it can possibly be considered a viable building material, whether structural, insulative, or for
installation as windows.
In addition, using ratios and input values in Microsoft Excel allowed for the creation of a
moveable point of data that would represent the house in relation to three user-chosen materials
in a ratio also decided by the user. Using this function enables the discovery of certain house
types (using the given data) that fall within the optimized zone.
Future Directions:
Future projects based on this research could include a more specific data set, where all
values are accounted for by somewhat more reliable sources. This could also factor in for certain
changes in price from buying in bulk, and would display these findings for more accurate realworld prices.
In addition, future projects could add other viable materials, or new forms of old
materials (e.g. double-pane glass) to the list, expanding the scope of the field and possibly
altering or even refuting the “optimized zone” found in this study.
Finally, there could almost certainly be found a “best” set of materials to build a house
out of, and a “best” ratio between parts for maximum energy efficiency and cost. However, it is
much more difficult to judge this accurately, as both varying user preservation methods (which
would affect lifespan and effectiveness) and the addition of newer, possibly synthetic materials
to the market would change this result.