Ever since the industrial revolution, the use of coal as a fuel for energy production and the
requirement of space for (heavy) industrial activity led to an increase of greenhouse gases into
the atmosphere. (American Institute of Physics, 2017) This activity also required a step
increase of delving raw materials, The exponential increase of all this producing, consuming
and disposal of goods and emission of greenhouse gases lead to global warming. A lot of focus
has been put on renewable energy to reduce emissions but our industrial economy has never
evolved from a linear take-make-dispose pattern since this is established during the industrial
revolution. (Ellen MacArthur Foundation, 2013)
The circular economy is a principle that synthesizes multiple schools of thought from
sustainability and industrial engineering principles. (Ellen MacArthur Foundation, 2013) there
are many different definitions but the principle consists of the biological cycle, the economic
model and the technical cycle and seven principles to guide the circular economy concept
(Ellen MacArthur Foundation, 2013; Schoolderman et al., 2014). Where the linear model stops
at disposal, the circular economy model defines feedback loops where products and material
circle back into the economy. (Ellen MacArthur Foundation, 2013) the circular economy does
not only attempt to solve the problems caused by the linear economy, but it is also estimated that
the economic potential is worth billions. Making it a problem solver but also a (potential)
money maker. This makes research towards the circular economy a relevant topic in today’s
The construction industry is the cause of 32.7% of the total waste generation (International
Energy Agency, 2015), it accounts for 31% of the total energy use (International Energy
Agency, 2015) and produces 9% of the total greenhouse gas emissions (European Union,
2016). Therefore it is important to apply circular principles to the built environment to reduce
“For the circular economy to become a success, a simple measure of achievement is necessary
as the first step towards fully integrated reporting. (Kok, Wurpel, & Ten Wolde, 2013) The
Building the Circularity Indicator (BCI) assessment (Verberne, 2016) model attempts to achieve
this. It assesses how well the principles of the Circular Economy are implemented in a building
project by translating them to Key Performance Indicators (KPI’s). Essentially the circular
potential is determined by two major KPI’s, the material in- an output and the disassembly
potential of products and materials.
The BCI is calculated in four steps. First, the Material Circularity Indicator (MCI) is calculated
for all products with the material in- and output and the technical lifecycle. Then the Product
circularity Indicator for all products is calculated with the MCI and Disassembly Determining
Factors (DDF’s) (Durmisevic, 2006). The next step is to categorize all products by shearing layer
of Brand (Brand, 1994) and use a normalizing factors like volume, weight, price, etc. to
calculate the System Circularity Indicator (SCI) of all layers. And the last step is to calculate the
Building Circularity Indicator with the SCI’s and the level of importance.
This research identified several limitations of calculating the disassembly potential is the BCI
assessment model. The goal of this research is to solve these limitations. For this the following
research question is formulated. “How can the disassembly potential of a building be
determined as an integral part of building circularity and what influence does this have on the
Building Circularity Indicator Assessment model?”
A literature study is conducted to understand what the role is of disassembly to enable the
circular economy and to identify influencing factors. The following aspects are considered:
▪ Disassembly and circular economy guiding principles
▪ Disassembly and the relation with the theory of building levels
▪ Disassembly as an integral aspect in the building development process
▪ The role of disassembly on material reutilization and reusability.
Furthermore, twenty-five factors categorized as technical, process-based on financial factors
(van Oppen, 2017) are identified that influence building disassembly from existing research.
Not only the built environment but also other sectors like industrial engineering and
automotive are considered. Adding all the factors in the BCI assessment model would make
the model too complex. Therefore the decision is made to only incorporate the most
important disassembly factors in the assessment model.
Two surveys are conducted with the Fuzzy Delphi Method (FDM) to validate and make a
selection of disassembly factors and to determine the relative weights of the disassembly
factors. A hypothesis is formulated that there is a difference between importance of the
disassembly factors. This can be covered with assigning weights for disassembly factors in the
BCI assessment model. From the results of the first survey twelve factors are selected and
from the results of the second survey the weights are determined. By validating the impact of
the weights no significant difference is found between the importance of disassembly factors.
This does not support the hypothesis and the decision is made to use equal weights for
disassembly factors in the BCI assessment model.
A new conceptual model for the BCI assessment model is developed to solve the limitations
of the BCI assessment model. Disassembly factors are implemented in the model as technical
requirements, preconditions and drivers. In the MCI step all products are classified according
to a proposed method to determine building levels. In the PCI step, relational patterns based
on detail drawings serve as framework to assess the Disassembly Potential of all products with
the new disassembly factors. According to reusability of products or a Disassembly Potential
threshold, systems are determined in the SCI step. These systems represent clusters of
products that can be assessed for Disassembly Potential as an entity. Finally in the BCI step,
all PCI’s and SCI’s are aggregated with a normalizing factor to determine one score to indicate
the circular potential of a building.
The new BCI assessment model is validated with a case study of a building designed for
disassembly. After a short validation session with the developers the resulting disassembly
potential reflects their experience with assembling and disassembling their model house.
Furthermore the results are compared with the old BCI assessment model. The difference
seems bigger on lower building levels than higher building levels, which is expected, but more
test cases are required to draw conclusions of the impact of the new calculation method on
the score. It was impossible to determine disassembly potential in the old BCI assessment
model without framework. Providing this is another big contribution of this research. By using
relational patterns and detail drawings the BCI assessment model became more transparent.
This method is also used to calculate the old BCI assessment model for the comparison.