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\begin{document}

\title{~~~~Integration of Key Lifecycle Indicators for Selecting Design
Strategies}



\author[1]{andrea.invidiata}%
\affil[1]{Dipartimento ABC. Politecnico di Milano}%


\vspace{-1em}



  
  \date{May 19, 2024}


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~~~~~~~~~~~~~~~~~~~\textbf{ABSTRACT~}

~~~~~~~~~~~~~~~~~~~~The building sector is one of the largest consumers
of natural resources and energy in the world. Design strategies to
improve the energy efficiency can be a solution to decrease impacts of a
building. To evaluate the correct way to choose design strategies in
buildings, they should be analysed with a multidisciplinary approach
based on sustainable development. The method presented herein is based
on a system of indicators that allows performing a comprehensive
evaluation of alternative design solutions. Through the Life Cycle
Assessment, Life Cycle Cost Analysis, indoor comfort conditions, future
effects of climate change and the Multi Criteria Decision Making
(COPRAS) it is possible to choose the most sustainable design strategies
in buildings. A multi-family social building, located in Milan, north of
Italy, was evaluated based on a 100-year lifespan. Six design solutions
with different structure and envelopes of the building were evaluated.
The results have shown that the design solution with the highest
sustainable behaviour degree is the reinforced concrete frame with
rectified bricks (100\%), followed by reinforced concrete frame with
cellular concrete blocks (75,4\%) and by X-Lam and wood fiber (20,79\%),
compared to the actual design building. This research pointed out the
need of using a multicriterial method to assure the right selection of
design strategies for obtaining more sustainable buildings.

\textbf{~~~~~~~~~~~~~~~~~~~~Keywords}

~~~~~~~~~~~~~~~~~~~~Sustainable buildings; Life cycle assessment; MCDM;
Energy efficiency.

~~~~~~~~~~~~~~~~~~~\textbf{INTRODUCTION}

~~~~~~~~~~~~~~~~~~~~The building construction sector is considered one
of the largest consumers of natural resources and energy. Buildings
consume 30-40\% of all primary energy and natural resources over their
lifespan (construction, operation, maintenance and demolition) and
respond for 30\% of the emission of greenhouse gases in the world
{[}1,2{]}. An appropriate choice of design strategies reduces the energy
demand of buildings {[}3-5{]}. Often, researches focus on the reduction
of energy consumption during the operational phase of buildings {[}6,
7{]}. The increase of energy efficiency in the buildings' sector is
reducing the energy consumption during the operational phase. But, the
improvements to design a more energy efficient building require more
materials and, consequently, the environmental impact of the building in
its construction, maintenance and demolition phases is greatly increased
{[}8-13{]}. Therefore, studies have addressed a more holistic approach,
which covers from the production process of materials involved in the
construction all the way to the demolition and recycling phases. Through
a methodology based on sustainable design of buildings, it is possible
to reduce their environmental impact and improve the quality of life of
the inhabitants. In recent years different methods have been developed.
One of these is the Life cycle assessment (LCA) use to analyse the
environmental impacts attributable to the life cycle of products and
services {[}14{]}. Through LCA is possible to analyse different
environmental impact categories arising from the construction, use,
maintenance and demolition of the buildings. Recent studies show
different approaches to apply the LCA in buildings. Stazi et al.
{[}15{]} studied 70 Italian residential buildings, of which five case
studies were monitored and one case underwent an in-depth environmental
evaluation. The authors used the Global Warming Potential, Ozone Layer
Depletion Potential and the Acidification Potential to analyse the
building using the LCA method. Motuziene et al. {[}11{]} analysed the
life cycle of a single-family house in Lithuania considering three
impact categories: Primary Energy Demand, Global Warming Potential and
Ozone Layer Depletion. The goal of the study was to improve the energy
efficiency of buildings, given the impact on the environment in the life
cycle. Atmaca and Atmaca {[}16{]} studied the life cycle of two
different residential buildings in Turkey through two impact categories:
the Primary Energy Demand and the Carbon Dioxide Emissions. These
studies show a different form to analyse the buildings through the LCA
method. The selection of the impact categories, like as the analyse of
the final results, are evaluate in an arbitrary manner. The impact
categories more used were the Global Warming Potential followed by the
Energy Demand {[}11, 15, 16{]}. ~This is because the energy efficiency
aspect is the most relevant parameter for the researchers, when evaluate
buildings through a LCA. Often, the LCA method is associated with the
Life-Cycle Cost Analysis (LCCA) to evaluate the cost of the building in
the life cycle {[}17-19{]}. Studies on LCCA have been developed to
analyse the best constructive choice in buildings and evaluate which
phase presents the highest cost {[}17-19{]}. Often, the use of the
methods LCA and LCCA in buildings does not help the decision-making
{[}11, 20, 21{]}. For this reason, Multi Criteria Decision Making (MCDM)
frequently appears like a complementary tool to LCA or LCCA {[}11,
21{]}. There are many ways to apply different multi-criteria analysis
methods. One of the most used is the analytic hierarchy process (AHP).
Proposed for the first time by Saaty {[}22{]} as a multi-criteria
decision-making tool, which attempts to determine weighting factors for
the criteria under consideration through pairwise comparisons. The MCDM
is a great tool to analyse different parameters in the same case study,
however, its application in the LCA is not common. Through the MCDM, LCA
and LCCA, Motuziene et al. {[}11{]} analysed three different types of
envelopes in a single family-house in Lithuania. The results show that
the MCDM method is an important tool to analyse different parameter in
buildings. To analyse and choose the correct design solutions in
buildings is of paramount importance bases its one decision not only in
one parameter.~~ The aim of this study is to propose a method for the
selection the design solution, that combines adaptive thermal comfort,
LCA, LCC and MCDA methods, and to illustrate its performance in a
multi-family social building located in Milan considering the effect of
climate change in buildings. The presented approach is based on a
complex system of criteria that enables comprehensive evaluation of the
alternative design strategies. The proposed method can be applied for
different types of buildings at different locations.

\textbf{~~~~~~~~~~~~~~~~~~~ METHOD}

~ ~ ~ ~ ~ ~ ~ ~ ~ ~ The method used the three pillars of the sustainable
development to evaluate the design strategies in buildings. The four
parameters analysed are: the comfort hours, the primary energy demand in
the life cycle, the carbon dioxide emissions in the life cycle and the
cost in the life cycle. The method is the combination of LCA, LCCA,
indoor comfort conditions, future effects of climate change and MCDM to
analyse the design strategies in buildings (Fig. 1).\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/1/1}
\caption{{Diagram of the method.
{\label{831047}}%
}}
\end{center}
\end{figure}

~~~~~~~~~~~~~~~~~~~ The method is divided in five main parts, i.e., (i)
evaluation of the indoor comfort conditions in the building the adaptive
comfort method; (ii) evaluation of the energy demand using LCA; (iii)
evaluation of the carbon dioxide emissions using LCA; (iv) evaluation of
the design strategies through the LCCA; (v) decision making process
through the MCDM.~~ In this method, only the design strategies are
analysed through the LCA and LCCA. Other materials that make up the
building are not evaluated. The recycling phase in LCA and LCCA was not
considered. In order to analyse the operational energy consumption and
the indoor comfort conditions of the building the EnergyPlus computer
programme was used.

\textbf{~~~~~~~~~~~~~~~~~~~~Comfort hours}

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The first parameter evaluate is
the indoor comfort hours in the building. Through the adaptive comfort
method {[}23{]} it was possible to determine the number of hours in
which there is internal thermal comfort in the building throughout the
year with and with no design solutions. The EnergyPlus computer
programme, version 8.4, was used. This analysis consists in verifying if
the use of design strategies allows for an improvement in the internal
thermal comfort. Eq. (1) shows how to estimate the upper limit.

(1)~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~ ~~~~Ulim=
0,31 x tpma + 21.3

where Ulim is the upper limit of the comfort zone (\selectlanguage{ngerman}°C), tpma is the mean
outdoor air temperature (°C).

Eq. (2) shows how to estimate the lower limit.

(2)~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~ Llim=
0,31 x tpma + 14.3

where Llim is the lower limit of the comfort zone (°C), tpma is the mean
outdoor air temperature (°C).

Through the limits of the comfort zone is possible to define if the
design strategies improved the thermal comfort conditions of the
building. Eq. (3) demonstrates how to obtain the number of hours in
which there is internal thermal comfort through the use of the design
strategy.

(3)~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Hcomfort = Ha~ - Hb~~ ~~

where Hcomfort is the increment in the number of hours in which there is
internal thermal comfort over one year when a design strategy is used
(h), Ha is the number of hours in which there is internal thermal
comfort over one year with the use of a design strategy (h), Hb is the
number of hours in which there is internal thermal comfort over one year
without the use of a design strategy (h).

\textbf{~~~~~~~~~~~~~~~~~~~~Primary Energy Demand~}

~~~~~~~~~~~~~~~~~~~~The primary energy demand was analysed through the
LCA. Thanks to this analysis, it was possible to identify the energy
balance, i.e., the energy saved over the building life cycle through the
use of the design strategies (Eq. (4)).~ ~

(4)~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ LCEb= OEs -
(EE+ME+DE) + ELR

where LCEb is the life cycle energy balance (kWh); OEs is the
operational energy saved due to the use of the design strategy (kWh); EE
is the embodied energy in the design strategy (kWh); ME is the
maintenance energy due to the design strategy (kWh); DE is the
demolition energy in the design strategy (kWh); ELR is the energy
life-cycle of the component in the base case that will be replaced by a
design strategy.

{For transportation of the design strategy from the manufacturer to the
retailer, three distance was adopted in the primary energy demand and
the carbon dioxide emissions parameters (50km, 250 km and 1000 km). For
transportation of the workers to the construction site, the distance of
50 km was adopted in the different LCA phases. The Fig. 6 shows the
lifespan of design strategies based on a literature review {[}16, 17,
41-46{]}. For transportation of the design strategy from the building to
the closest landfill site, the distance of 50 km was adopted. In the LCA
the re-use phase of the design strategies was not considered.}

\textbf{~~~~~~~~~~~~~~~~~~~~~~~~Carbon dioxide emissions}

~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ Following the evaluation of the design
strategies using the primary energy demand parameter, the design
strategies were analysed considering their life cycle carbon emissions.
Also in this parameter only the design strategies were analysed. The
carbon emissions balance is the CO\textsubscript{2}e ~saved over the
house life cycle through the use of the design strategies (Eq. (5)).

(5)~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ LCO\textsubscript{2}b=
OCO\textsubscript{2}s
-(ECO\textsubscript{2}+MCO\textsubscript{2}+DCO\textsubscript{2})+ECO\textsubscript{2}R

where LCO\textsubscript{2}b is the carbon emissions balance
(kgCO\textsubscript{2}e); OCO\textsubscript{2}s is the operational
carbon emissions saved due to the use of the design strategy
(kgCO\textsubscript{2}e); ECO\textsubscript{2} is the embodied carbon
emissions in the design strategy (kgCO\textsubscript{2}e);
MCO\textsubscript{2} is the maintenance energy due to the design
strategy (kgCO\textsubscript{2}e); DCO\textsubscript{2} is the
demolition carbon emissions in the design strategy
(kgCO\textsubscript{2}e); ECO\textsubscript{2}R is the life cycle carbon
emissions of the component in the base case that will be replaced by a
design strategy.

\textbf{~~~~~~~~~~~~~~~~~~~~~~~~Cost~}

~~~~~~~~~~~~~~~~~~~~~~~~The latest parameter is the cost of the design
strategy analysed through the LCCA.~ Through the ~economic balance it
became possible to define the cost for the use of the design strategy
during the life cycle of the building. The economy obtained through the
reduction on energy consumption during the lifespan of the house was
subtracted from the initial costs, maintenance and final demolition
costs (Eq. (6)).

(6)~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ LCCb= CS - (CI
+ CM + CD) + CE

where LCCb is the economic balance (\euro{}); CS is the savings on the
electricity bill during the operational phase due to the use of the
design strategy (\euro{}); CI is the initial cost of the design strategy
(\euro{}); CM is the maintenance cost of the design strategy (\euro{});
CD is the demolition cost of the design strategy (\euro{}).

The cost of each strategy was obtained through the pricelist of the
\emph{Camera di Commercio di Milano} {[}48{]}. The cost of the
maintenance material as well as replacement considered the average
inflation in the last ten years in Italy, which is 1,34\% a year
{[}49{]}.

\textbf{~~~~~~~~~~~~~~~~~~~~~~~~Multi-criteria decision making}

~~~~~~~~~~~~~~~~~~~~~~~~In order to find the best design solution in
building, the AHP and multiple criteria decision-making method COmplex
PRoportional Assessment (COPRAS) have been applied {[}24{]}. The method
COPRAS was first introduced in 1994 by Zavadskas and Kaklauskas
{[}25{]}. It assumes the direct and proportional dependence of the
significance and utility degree of the investigated versions on a system
of criteria, adequately describing the alternatives, values and weights
of the criteria~ For the assessment of the criteria weights 30 experts,
from three different areas (designer, researcher, Administrative and
technical) of the construction sector, were selected. The experts have
participated in the survey, in order to set the criteria weights and
determine their priority. Following the Saaty comparison scale of nine
levels {[}22{]} they have filled the pair-wise comparison matrix of the
following sub-criteria: Indoor comfort hours; Primary energy demand;
Cost. The scale of relative importance has the intensity from 1 to 9,
where the importance is from equal to extreme importance. In order to
ensure the consistence of the comparison matrix, the consistency ratio
(CR) must be evaluated and condition CR \textless{} 0.1 must be
satisfied.~ The first step to obtain the final decision by the method
COPRAS is the formation of the normalized decision-making matrix, where
the goal is to get the non dimensional weighted values out of the
comparative parameters (Eq.(7)).

(7)~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\(dij=\frac{xij\cdot qi}{\Sigma\ xij}\ \ i=1,m;\ j=1,n\)

where dij is the non dimensional weighted values; xij is the value, j``
of criteria,, i`` in decision value; m is the number of criteria; n is
the number of compared evaluations; qi is the significance of ,,i``
criteria.

Eq. (8) demonstrates how to obtain the sums of minimizing Sej and
maximizing Sþj normalized indicators.

(8)~~~~~~~~~~~~~~~\(S_+=\Sigma_{j=1}^nS_{+j}=\Sigma_{i=1}^n\Sigma_{j=1}^md_{+ij}\ \ \ \ S_-=\Sigma_{j=1}^nS_{+j}=\Sigma_{i=1}^n\Sigma_{j=1}^md_{-ij}\)

The relative importance Qj of each alternative aj is evaluated by the
following Eq. (9):

(9) ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~\(Q_j=S_{+j}+\frac{S_{-\min}\cdot\Sigma_{j=1}^mS_{-j}}{S_{-j}\cdot\Sigma_{j=1}^m\frac{S_{-\min}}{S_{-j}}}\ j=1,2,3,...m\)

The final stage is calculate the utility degree of each alternative
(Eq.(10)).

(10)~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\(N_{j^=}\frac{Q_j}{Q_{\max}}\cdot100\)\%

Through the normalization of the four parameters used in the study it
will possible to obtained the relative importance Qj of each design
solutions and the utility degree Nj of each alternative.

\textbf{~~~~~~~~~~~~~~~~~~~~~ CASE STUDY}

\textbf{~~~~~~~~~~~~~~~~~}~~~~~~The method was applied in a multi-family
social building located in Milan (Fig. 2). A lifespan of 100 years was
used for the case study {[}26{]}. The \emph{Studio Rossi Prodi
Associati} realized \emph{Cenni di Cambiamento} in 2013. The building
was designed to be highly energy efficient and obtained the level A in
the Italian energy certification {[}27{]}. Four buildings compose the
entire project. In this study, only one building was evaluated. The
building has nine stories in contact with the ground. The basement was
not evaluated. On each floor there are four apartments of different
sizes.~~~~~~~~~~~\textbf{~ ~}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/12/12}
\caption{{Multi-family social building: `` \emph{Cenni di Cambiamento}''.
{\label{416482}}%
}}
\end{center}
\end{figure}

~~~~~~~~~~~~~~~~~~~ The internal and external walls are made of X-Lam
structure. The rock wool insulate was used in the envelope of the
building. The aluminium windows with triple glass and argon gas, with a
thermal transmittance of 1,1 W/m2K, were used. Shading devices in
aluminium were used to reduce the solar irradiation on the building.
Fig. 2 shows the thermal characteristics of the envelope of the
building.\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/3-08.47.28/3-08.47.28}
\caption{{Thermal characteristics for each component of the building.
{\label{797791}}%
}}
\end{center}
\end{figure}

~~~~~~~~~~~~~~~~~~~ Design Strategies~ To apply the proposed method in
the case study, five project solutions were analysed and compared with
the actual design of the building. The most recent projects of
multi-family social housing in Italy were analysed to identify the
design solutions {[}28-34{]}. Three different structure of the building
were evaluated: X-Lam, reinforced concrete frame and steel frame. A
simplified method to calculate the dimension of the structures was used.
The three structures were analysed with different insulations or
envelope materials. Eventually, six design solutions (Case) of the
building were evaluated: X-Lam and rock wall (actual case), X-Lam and
wood fiber, reinforced concrete frame with cellular concrete blocks,
reinforced concrete frame with rectified bricks, steel frame and drywall
with rock wool and steel frame and drywall with wood fiber. In the roof
of the building only the insulation was analysed. The different design
solutions maintain the same thermal transmittance of the actual
envelope. As mentioned above, only the design solutions were evaluated
by the LCA and LCCA. Fig. 4 shows the characteristics of the six design
solution evaluated.\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/13/13}
\caption{{Design solutions analysed in the building.
{\label{894200}}%
}}
\end{center}
\end{figure}

\textbf{~}~~~~~~~~~~~~~~~~~~~~Fig. 5 show the data-base used in the case
study. The embodied energy of the design strategies was obtained from
three different data-base: ICE database {[}35{]}, Ecoinvent 3 and the
EPD certification of building materials. The average value and the
standard deviation were used to analyse the design strategies.\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/5-08.47.28/5-08.47.28}
\caption{{Embodied energy and emissions in the three database used.
{\label{177530}}%
}}
\end{center}
\end{figure}

~~~~~~~~~~~~~~~~~~~ In the operational phase, the average generation
factor used to convert electricity consumption into carbon emissions is
0,3524 kgCO2e/kWh {[}36{]}. The Fig. 6 shows the lifespan of design
strategies based on a literature review {[}16, 17, 37-42{]}.\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Schermata-2017-06-01-alle-17.52.00/Schermata-2017-06-01-alle-17.52.00}
\caption{{Lifespan of building materials.
{\label{555204}}{\label{555204}}%
}}
\end{center}
\end{figure}

~~~~~~~~~~~~~~~~~~~ In the cost parameter, the cost of each strategy was
obtained through the pricelist of the Camera di Commercio di Milano
{[}44{]}. The cost of the maintenance material as well as replacement
considered the average inflation in the last ten years in Italy, which
is 1,34\% a year {[}44{]}.

~~~~~~~~~~~~~~~~~~\textbf{~~Computer simulation}

~~~~~~~~~~~~~~~~~~~~In order to assess the energy demand and the indoor
comfort conditions of the building the EnergyPlus computer programme,
version 8.4, was used. The building was evaluated considering the
constant use and occupation for the users. Details on occupation,
thermal load and use pattern were taken based on the Italian regulation
for energy efficiency in buildings {[}45{]}. Equipment and lighting
operated with 100\% load during the operational hours. The building was
evaluated in two different conditions: (i) while the naturally
ventilated and (ii) operating with an air-conditioning system. The
apartments while natural ventilated was analysed considering 20\selectlanguage{ngerman}°C as the
indoor temperature control of the opening or closing of windows. When
the house was operating with air-conditioning, the operation period of
artificial conditioning system was developed according to the indoor
occupation of the house (bedrooms and living room). When users are not
at home, the rooms are naturally ventilated and the indoor temperature
adopted to control the opening or closing of windows was 20°C. In order
to obtain the energy consumption for air-conditioning, the set-point
temperatures were taken as 20°C for heating and 26°C for cooling
{[}46{]}.~~ Climatic data and tool to assess climate change~~ The effect
of climate change in the case study was analysed. Recent studies show
the importance to evaluate the effect of climate change in buildings
{[}47, 48, 49{]}. For this reason, to analyse the design strategies
through the four parameters, the effects of climate change on the
building were studied. The tool Climate Change World Weather file
Generator {[}50{]} was used for the A2 (medium high) emissions scenario
for three future time slices, the 2020s, 2050s and 2080s {[}51{]}. The
building was evaluated under the current climate data and in the three
future climate data generated for the city of Milan using the weather
data file in Test Reference Year (TRY) format. Recent studies explain
with more details the operation of this tool {[}48, 49{]}.

~~~~~~~~~~~~~~~~~~~~~~~~Fig. 7 shows the annual air temperature,
relative humidity and global horizontal radiation in Milan in the four
climate data files: current, 2020, 2050 and 2080.\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/7/7}
\caption{{Milan Climate conditions in the four different periods.
{\label{472000}}%
}}
\end{center}
\end{figure}

The outdoor air temperature will increase over the years in Milan. Thus,
by 2080, the annual mean outdoor air temperature will increase 3.6 \selectlanguage{ngerman}°C
compared to the current temperature. Instead, the mean relative humidity
will decrease over the years. Thus, by 2080, the annual mean relative
humidity will decrease 5.7\% compared to the current climate conditions.
Finally, the mean global horizontal radiation will increase over the
years. Thus, by 2080, the annual mean global horizontal radiation will
increase 7.2 Wh/m2, compared to the current global horizontal radiation.
The analysis of the future climate condition in Milan shows the effect
of climate change in the future. These climatic changes directly affect
the energy and thermal performance of buildings, as demonstrated by
recent international studies {[}47--49{]}. For this reason is crucial to
consider the climate change effects when analysing the life cycle of
buildings.

\textbf{Results of the design strategies~}

Fig. 8 shows the six design strategies analysed, through the four
sustainable parameters, in the building.The Case 1 represent the design
strategy used in the actual project of the building. The results show a
different behaviour of the six design solutions. In the comfort
parameter, the Case 4, reinforced concrete frame with rectified bricks,
obtained the best result with more than 6000 indoor comfort hours, while
the Case 5, steel frame and drywall with rock wool, has obtained lass
than 5000 hours.~~ In the energy demand factor, the best design solution
is the Case 3, reinforced concrete frame with cellular concrete blocks,
with 1500 MWh, while the Case 2, X-Lam and wood fiber, obtained the
worst result with 3400 MWh in the life cycle. In all design strategies
the embodied energy represents the largest part of the energy consumed
in the life cycle. In the Case 4 the embodied energy represent 91\% of
the total energy. The maintenance phase is also a relevant stage in the
energy life cycle of the design strategies, accounting for 30\% of total
energy in the Case 5. In all design solutions, the demolition phase
represents less than 2\% of the energy consumed over the life cycle.
Besides, Fig. 8 shows that the use of different databases (indicated
through a line with the highest and lowest values) may influence the
results of the life cycle to 30\%. All this shows that the choice of the
correct data-base is one of the main issues in a LCA study, as
demonstrated by international studies {[}8, 10{]}.~ The results of the
carbon dioxide emissions show that the two design solution in X-Lam
frame are the best choices, with little more than 230
tCO\textsubscript{2}e. The Case 4, reinforced concrete frame with
rectified bricks, obtained the highest emissions with 230
tCO\textsubscript{2}e in the life cycle. The embodied emissions
represent the main part of the total emissions. The maintenance phase
characterizes 34\% of the life cycle emissions in the X-Lam structures,
between 20\% and 23\% in the steel frame design solutions and only 8\%
in the reinforced concrete frame cases. The deconstruction phase
represents around 3\% of the design solutions life cycle. Even in the
emission parameter, the variation of the results due to the three
data-bases used is significant. In the entire life cycle of the X-Lam
design solutions the result may vary by 200\%.~ In the cost factor, the
best design solution was the Case 4, reinforced concrete frame with
rectified bricks, with a cost of \euro{} 2,500,000, while the Case 2,
X-Lam and wood fiber, obtained the worst result with a cost of \euro{}
2,927,000 in the life cycle. In all design solutions, the initial phase
represents between 43\% and 49\% of the final cost, while the
maintenance phase represents between 46\% and 54\% of the life cycle
cost. The deconstruction phase represents between 1\% and 5\% of the
final cost. An interesting fact concerns the cost of labour, which,
unlike the energy and emission parameters, represents 40\% of the cost
of the life cycle.\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/8/8}
\caption{{Results of the design strategies in the four parameters.
{\label{954735}}%
}}
\end{center}
\end{figure}

\textbf{~~~~~~~~~~~~~~~~RESULTS AND DISCUSSION}

\textbf{~~~~~~~~~~~~~~~~Compare the results of the design strategies
applied in the building}

~~~~~~~~~~~~~~~~After analysing the design strategies in the four
parameters, the results of design solution applied in the building were
presented. The results of the energy simulation of the building with
different design strategies show a similar behaviour due to the same
thermal transmittance. Fig. 9 shows the results of the design solutions
compared with the actual project of the building. In the comfort factor,
only the two design strategies in steel frame obtained a negative
comfort hours compared to the actual design project of the building. The
Case 4, reinforced concrete frame with rectified bricks, obtained the
best result, increasing the number of hours in the life of the building
in which there is internal thermal comfort up to 60000 h (11\%) in
comparison to the actual design.~~ In the energy demand factor, the Case
2 obtained a negative energy balance, while the Case 3 resulted in the
greatest reduction of energy consumption, compared to the actual design
of the building. The design strategy in reinforced concrete frame with
cellular concrete blocks (Case 3) allowed for energy savings of
approximately 1300 MWh in the life cycle.~ In the carbon emissions
balance, only the Case 2 improve the performance of the building. The
other design solutions obtained a negative CO\textsubscript{2}e balance
compared the actual project.~ Finally, in the economic balance, all
design solution applied in the building obtained a positive balance. The
best design strategy was the reinforced concrete frame with rectified
bricks, with cost savings over \euro{} 290,000.~ The results show that
the optimal choice of project strategy is complex. In fact, no design
strategies obtained the best performances in the four parameters.\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/9/9}
\caption{{Results of design strategies applied in the building compared with the
actual design solution.
{\label{333814}}%
}}
\end{center}
\end{figure}

\textbf{~~~~~~~~~~~~~~~~~~~~~~~~Results of multi-criteria decision
making}

~~~~~~~~~~~~~~~~~~~~~~~~In order to find the best design solution in the
building, the AHP and multiple criteria decision-making method COPRAS
have been applied. First of all, the results of the survey were
analysed.~ Fig. 10 shows the results of parameters weights and their
priority. The results show that among the three groups of professionals
there is a different perception of the importance of the four
sustainability parameters. Among the designers, the parameter that most
influences their professional choices is the cost, followed by the
energetic, comfort and emissions parameter. Among the researchers, the
comfort parameter is considered the most important, followed by the
emissions, the cost and the energy factor. Finally, the cost parameter
is the most relevant for the administrative and technical professional,
followed by the energetic, emissions and comfort parameter. To ensure
the consistency of the comparison matrix, the consistency ratio (CR) has
been evaluated and has satisfied condition CR \textless{} 0.1. The
results show that the priorities of criteria (and weights) are as
follows: cost (0.306), energy demand (0.258), comfort (0.233), carbon
dioxide emissions (0.204).\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/10/10}
\caption{{Results of survey.
{\label{773188}}%
}}
\end{center}
\end{figure}

Fig. 11 shows that the design solution with the highest utility degree
is the Case 4, reinforced concrete frame with rectified bricks, (100\%),
followed by the Case 3, reinforced concrete frame with cellular concrete
blocks, (75.4\%), and Case 2, X-Lam and wood fiber, (20.8\%). The Case 6
and 5 obtained similar results of the actual design solution of the
building. The Case 5 obtained a negative utility degree (-7,1) despite
having a positive cost. Although this criterion is the most determinant,
it has not changed the priority of design strategies because in the
other parameters the Case 5 has not achieved a good performance.\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/11/11}
\caption{{Results of multi-criteria analysis.
{\label{960862}}%
}}
\end{center}
\end{figure}

\textbf{~~~~~~~~~~~~CONCLUSION}

~~~~~~~~~~~~This paper has demonstrated the importance of evaluating
design strategies applied in buildings, not exclusively in the
operational phase but during the entire life cycle through different
parameters. The method used, aims to analyse the design solution in
buildings with a sustainable approach. The article presents a new
approach based on the complex system of criteria that allows
comprehensive evaluation of the design strategies in buildings combining
the indoor comfort conditions, the life cycle assessment (LCA), life
cycle cost analysis (LCCA) and the multi-criteria decision analysis
(MCDA). Through the method proposed, it was possible to identify the
design solutions that reduce the environmental impact, improved the
indoor comfort conditions and reduce the cost. The design solutions,
applied in the multi-family social building, show different performances
in the four parameters. In the comfort parameters, the best solutions
were the traditional reinforced concrete frame, as with the financial
cost parameter. The constant and centennial use of reinforced concrete
in civil engineering allows to a significantly cost reduction, compared
to more advanced techniques. In the energy demand parameter the concrete
and steel frame solutions obtained a better behaviour compared to the
actual design project. Instead, the X-Lam design solutions obtained
better results in the carbon dioxide emissions factor. The study shows
that significant emission savings have been achieved by the use of wood
products in the building. From the first analysis of the results, it is
not clear which are the best design solutions in the building. Through
the MCDM was possible to identify the best solution for the case study.
The reinforced concrete frame with rectified bricks is the best choice
of the building.~ The different databases used can completely change the
design strategies' behaviour in the energy and emissions life cycle.
That is one of the problems of the international studies in the field of
LCA. The climate change directly affects the future thermal and energy
performance of the building. This is another important aspect to
investigate in a life cycle analysis. Only two impact categories of LCA
were evaluated. Some studies showed that the energy demand and the
Global Warming Potential are not the categories with the greatest
impact. The selection of other impact categories can assure a more
comprehensive and accurate assessment. This choice may be made or by
identifying the impact categories more used in the literature studies or
by selecting the impact categories more significant after normalisation
or by considering the impacts categories more relevant in the opinion of
experts applying a survey.~ The method shown herein may become a useful
tool for designers when choosing the most suitable design strategies,
reducing the environmental impact and improving the quality of life of
users.

\textbf{Limitation}

This paper shows a small part of a research. For this reason, it has
some limitations which will be solved in future works:~ -- In the
demolition phase, the re-use of the design strategies material was not
considered. Through recycling, the performance of the design solution in
terms of energy and emissions could be improved.

\begin{itemize}
\tightlist
\item
  Only one of the six emission scenarios published by the IPCC {[}28{]}
  was considered. Other emission scenarios will be analysed in future
  studies;
\item
  Only two impact categories of the LCA method were analysed. Through
  the study of other impact categories is possible to improve the
  analyse of the environmental impact in the building.
\item
  The future improvements in energy efficiency of air-conditioning
  systems were not considered;
\item
  Only 30 experts have participated in the survey to obtained the
  parameters weights and determine their priority.
\end{itemize}

~~~~~~~~~~~~~~~~~~~\textbf{~ACKNOWLEDGMENTS}

The authors would like to thank CAPES, a Brazilian agency for
post-graduation education, for the PhD scholarship granted to Andrea
Invidiata for the development of this research project.

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