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ClimatCon
“Climate-resilient pathways for the development of concrete
infrastructure: adaptation, mitigation and sustainability”
Work Packages 3&4
Development of a probabilistic integrated
LCA-LCCA method and case study
Xiao-Hui Wang, Husham A. Salman and Dimitri V. Val
Institute for Infrastructure & Environment
Heriot-Watt University
Edinburgh, United Kingdom
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1. Introduction
About 5% of the global anthropogenic CO2 emissions come from cement production
(Pade and Guimaraes 2007). Approximately half of these emissions are generated by
calcination reaction, which is needed to produce Portland cement. Hence, CO2 emissions
associated with concrete production can be reduced by reducing the amount of Portland
cement used for this production. This is usually done by replacing Portland cement with
supplementary cementitious materials (SCMs), mostly industrial by-products such as fly ash,
blast furnace slag and silica fume (Meyer 2009); concretes containing such materials are often
referred to as ‘green’ concretes (Damtoft et al. 2008). The efficiency of measures for reducing
the environmental impact, in particular CO2 emissions, is typically evaluated by considering
the life cycle of concrete structures and/or their components (Tae et al. 2011) based on the life
cycle assessment (LCA) methodology presented in the international standards ISO 14040 and
ISO 14044 on environmental management. LCA calculates the environmental impact often
characterised by the Global Warming Potential (GWP). A review of the past environmental
impact studies of products made of traditional and ‘green’ concretes was presented by Van den
Heede and De Belie (2012). More recently, Habert et al. (2013) applied LCA to compare
different solutions for bridge rehabilitation and Van den Heede and De Belie (2014) compared
the environmental performance of Portland cement and high-volume fly ash concretes.
However, after completing the construction of a concrete structure the cement paste in the
concrete becomes exposed to the air. Carbonation, the reverse reaction to calcination, then
occurs and CO2 absorbed during this reaction partially offsets CO2 emitted in calcination
(Haselbach 2009). None of the studies mentioned above have accounted for that, while it has
been shown that not taking into account the CO2 uptake, especially during the end-of-life
phase of a concrete structure, leads to significant overestimation of CO2 emissions associated
with concrete infrastructure (Wu et al. 2014); e.g., Yang et al. (2014) estimated that after 40
years of the building service life and then 60 years of the use of recycled concrete the overall
CO2 uptake was 17% of the CO2 emissions from concrete production.
Although carbonation has a positive effect on the CO2 balance of concrete infrastructure,
it may also lead to deterioration of reinforced concrete (RC) structures due to corrosion of
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reinforcing steel, which starts when the carbonation front reaches the steel. The rate of
carbonation depends on atmospheric CO2 concentration, relative humidity (RH) and
temperature, which are changing over time due to climate change. As a result, the time to
initiation of carbonation-induced corrosion may decrease that will negatively affect the
long-term durability of RC structures. This problem was addressed by Yoon et al. (2007), who
proposed a model of carbonation which took into account increases in the ambient CO2
concentration and temperature. Stewart et al. (2012) using the same model but also
considering uncertainties associated with the model parameters estimated that the risk of
damage of RC structures in Australia due to carbonation-induced corrosion might increase by
16% by 2100. This research was further extended by Bastidas-Arteaga et al. (2013) and
Larrard et al. (2014); in the latter paper an advanced finite element model of carbonation was
developed. Talukdar et al. (2012a) proposed another numerical model of concrete carbonation
and showed that in Toronto and Vancouver the carbonation depth in concrete structures might
increase by up to 45% over 100 years due to climate change (Talukdar et al. 2012b). It should
be pointed out that none of the studies mentioned above considered the effect of loading,
including load-induced cracking, on carbonation although there is clear evidence that tensile
stresses lead to a major increase in the rate of carbonation (Castel et al. 1999). Since it has
been shown that climate change may accelerate deterioration of RC structures due to
carbonation adaptation actions are needed to maintain the durability of concrete structures.
Sustainability includes not only environmental issues but also economic and social ones.
Studies on the sustainability of RC structures have usually considered the whole life of a
structure (fib 2013) applying either life-cycle cost analysis (LCCA) (Val and Stewart 2003,
Narasimhan and Chew 2009), which concentrates on economic issues, or LCA (Lepech et al.
2014, Müller et al. 2014), which places emphasis on environmental ones. There have been a
few studies in which the two techniques, LCA and LCCA, have been integrated (Kendall et al.
2008). None of these studies have considered the effects of climate change and the CO2
uptake by concrete.
As can be seen from the above, there are gaps in the current approaches to the
sustainability evaluation of RC structures. In particular, none of the studies have addressed
adaptation and mitigation simultaneously although they are clearly interdependent. For
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example, if to partially replace Portland cement in the concrete mix with fly ash or blast
furnace slag the CO2 emissions from the concrete production will decrease but the rate of
carbonation will increase. In addition, many of the existing studies use deterministic
approaches although there are major uncertainties associated with environmental and
mechanical loads, material properties, models, etc.
In this report a probabilistic method for evaluating the whole life performance of RC
elements subjected to carbonation will be developed next within an integrated framework of
LCA-LCCA. The environmental impact in LCA will be evaluated in terms of GWP (i.e. mass
of CO2), which will account for both CO2 emissions and uptake. In LCCA the emphasis will
be on costs associated with the production, maintenance/repair and demolishing of RC
elements. Since the costs may be incurred at different times they will be discounted to a
present value to ensure that LCCA results are consistent. Deterioration of RC structures
during their service life due to carbonation-induced corrosion will be considered, including
the effect of climate change. This will enable to address adaptation measures in the analysis. It
will be assumed that a RC element needs to be repaired when cracking of the concrete cover
caused by corrosion becomes excessive (Val 2005); the time of excessive cracking will be
calculated using the previously developed carbonation model and a model for
corrosion-induced cracking (Chernin and Val 2011). The environmental impact of repairs as
well as the costs associated with them will be taken into account. To fully evaluate the effect
of the CO2 uptake the end-of-life stage will be considered. There are numerous uncertainties
affecting both LCA (e.g. Lloyd and Ries 2007) and LCCA (e.g. Val and Stewart 2003). To
apply probabilistic modelling these uncertainties need to be quantified. The quantification of
uncertainties associated with LCA and LCCA is based on the analysis of generic data
available in the literature, e.g. (Lepech et al. 2014, Eamon et al. 2012). Probabilistic analysis
will be performed via Monte Carlo simulation to obtain the distributions of GWP and costs.
An example illustrating the proposed methodology will be provided.
2. Concrete carbonation and its effects
2.1 Carbonation of concrete and corrosion initiation
Carbonation is a process involving two mechanisms – diffusion of carbon dioxide from
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the atmosphere into concrete and its reaction with the alkaline cement hydration products. The
reaction leads to reduction of the alkalinity, pH, of the concrete pore solution from 12 to 13 to
below 9, which destroys a thin passive oxide layer on the surface of steel reinforcement and
makes the steel susceptible to corrosion.
CO2 penetrates into concrete mainly due to gaseous diffusion through interconnected
air-filled pores; its diffusion through the water that fills pores and convection with the water
through the pores can be neglected. Relatively detailed numerical models, which account not
only for the CO2 diffusion but also for the transport of moisture and calcium ions as well for
the change in the concrete porosity (as the model presented in the WP2 report of this project),
are computationally intensive and by that reason difficult to use in probabilistic analysis. To
obtained simpler closed form solutions the diffusion of CO2 into concrete is usually described
by Fick’s first law of diffusion, which for one-dimensional diffusion is expressed as
x
CDJ
CO
COCO
2
22 (1)
where JCO2 is the flux of CO2, CCO2 the concentration of CO2 and DCO2 the diffusion
coefficient of CO2 in concrete. Taking into account that JCO2= dQCO2/Adt, Eq. (1) can be
written as
dtx
CADdQ
CO
COCO2
22
(2)
where QCO2 is the mass of diffusing CO2, A the surface area, ΔCCO2 the difference in the CO2
concentration in the atmosphere and at the carbonation front, and x the distance from the
concrete surface to the carbonation front.
CO2 that diffuses into concrete reacts with the alkaline cement hydration products. This
reaction is called carbonation and it may simply be described by the reaction between
Ca(OH)2 and CO2 (e.g. Taylor 1997)
OHCaCOCOOHCa 23
OH
22
2
(3)
It should be noted that Eq. (3) represents the final result of the reaction between Ca(OH)2 and
CO2, which in reality involves a number of intermediate reactions not shown here for the sake
of simplicity. As a result of the reaction, practically insoluble CaCO3 and water are formed (as
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noted previously, these reaction results influence the carbonation process but are not taken
into account in simple carbonation models). The reaction of carbonation removes free CO2
from the mass balance that can be described by the following equation
AdxadQ COCO 22 (4)
where aCO2 is the CO2-binding capacity of concrete, which depends mainly on the
composition of the concrete.
If to assume that the reaction of carbonation, Eq. (3), occurs instantaneously withdrawing
CO2 from the concrete pores and preventing it from diffusing further, the mass balance
equation at the carbonation front can then be formulated using Eqs. (2) and (4) as
dtx
CADAdxa
CO
COCO2
22
(5)
Solution of this equation is
ta
CDtx
CO
COCO
c
2
222
)(
(6)
where xc is the depth of the carbonation front at time t.
The solution in Eq. (6) has provided a basis for many semi-empirical models of concrete
carbonation (e.g. Papadakis et al. 1991, Yoon et al. 2007, Yang et al. 2014, Ta et al. 2016). In
all the models ΔCCO2 is represented by the ambient CO2 concentration. However, the
estimation of DCO2 and aCO2 varies between different models. For example, the CO2-binding
capacity of concrete, aCO2, is often assessed as (e.g. CEB 1997, Pade and Guimaraes 2007,
Yoon et al. 2007)
CaO
CO
HCOM
MCaOCa 275.02 (7)
where C is the cement content in concrete, [CaO] the amount of CaO per weight of cement,
αH the degree of hydration and MCO2 and MCaO the molar masses of CO2 and CaO,
respectively. Eq. (7) is based on the assumption that the cement paste in concrete has been
completely carbonated. To account for different cement types, in particular possible addition
of SCMs, Ta et al. (2016) suggested to modify this formula by introducing the cement clinker
content, φcl:
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C a O
CO
HclCOM
MCaOCa 275.02 (8)
Other definitions of aCO2 have also been proposed; e.g. Papadakis (2000) suggested
aCO2=0.33[CH]+0.214[CSH], where [CH] and [CSH] are the contents of calcium hydroxide
and calcium-silicate-hydrate in concrete (in kg/m3). The diffusivity of CO2 depends on a
number of parameters, including concrete properties (e.g. porosity, composition), ambient
conditions (e.g. relative humidity, temperature) and technological factors (e.g. curing). To take
these parameters into account various functions of these parameters have been introduced to
modify DCO2. A brief overview of such functions as well as various formulas proposed for the
estimation of aCO2 can be found in (Ta et al. 2016).
Practically all simple semi-empirical carbonation models have been presented in a
deterministic formulation, i.e. uncertainties associated with the models and their parameters
have not been described and quantified. However, in order to use a model for probabilistic
analysis quantitative description of uncertainties associated with its parameters and the model
itself, or at least sufficient statistical data for doing that, is needed. One of semi-empirical
models that satisfies this requirement is presented in (fib 2006). The model was used in
DuraCrete (2000) and LIFECON (2003), where uncertainties associated with its parameters
were quantified. According to this model the depth of carbonation (in mm) at time t (in years)
is evaluated as
tWtCORkktxsNACcec 2
1
0,2 (9)
where ke is the environmental function which accounts for the moisture conditions, kc the
execution transfer parameter, R-1
NAC,0 the inverse carbonation resistance of concrete
determined in natural conditions in [(mm2/year)/(kg/m
3)], [CO2]s the concentration of CO2 in
the surrounding air in (kg/m3), and W(t) the weather function. Note that
2
1
0,2
CO
CO
NACa
DR (10)
Thus, it represents both the diffusivity and the binding capacity of concrete.
In order to reduce test time it is recommended to determine R-1
NAC,0 using an Accelerated
carbonation test (ACC-test). The ACC test is carried out using concrete specimens at the age
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of 28 days (7 day curing in tap water at T=+20oC and then 21 day storage at T=+20
oC and
RH=0.65). The specimens are then stored for 28 days in a carbonation chamber at T=+20oC,
RH=0.65, and the CO2 concentration of 2.0 vol.%. More detail description of the ACC test
can be found in DuraCrete (2000) or fib (2006). A relationship between R-1
NAC,0 and the
inverse carbonation resistance of concrete determined in the ACC-test, R-1
ACC,0, is found by a
linear regression analysis
tACCtNAC RkR 1
0,
1
0, (11)
where kt is the regression parameter and εt the error term.
R-1
ACC,0 can be described by a normal distribution with mean value estimated using the
ACC test result as
2
10,
c
R
X
ACC
[(m2/s)/(kg/m
3)] (12)
where Xc is the carbonation depth in [m] measured in the ACC test and τ=420 (s/kg/m3)0.5
the
“time constant”. If no test data is available, data from Table 1 can be used for orientation
purposes (fib 2006).
Since relative error in measuring Xc decreases with an increase in Xc and the mean value
of R-1
ACC,0 is directly proportional to Xc the coefficient of variation of R-1
ACC,0 depends on its
mean value and can be found as
22.01110689.0 10,
10,
ACCACC RRCOV (13)
Table 1. Mean values of R-1
ACC,0 for different types of cement.
Cement type
R-1
ACC,0 [10-11
(m2/s)(kg/m
3)]
w/ceqv1
0.35 0.40 0.45 0.50 0.55 0.60
CEM I 42.5 R n.d.2
3.1 5.2 6.8 9.8 13.4
CEM I 42.5 R + FA
(k=0.5)
n.d.2 0.3 1.9 2.4 6.5 8.3
CEM I 42.5 R + SF
(k=2.0)
3.5 5.5 n.d.2 n.d.
2 16.5 n.d.
2
CEM III/B 42.5 n.d.2 8.3 16.9 26.6 44.3 80.0
1equivalent water-cement ratio, considering fly ash (FA) or silica fume (SF) with the respective
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efficiency factors (k-values). The considered contents were: FA – 22% of weight of cement (22 wt%
cement), SF – 5 wt% cement. 2n.d. – data on R-1
ACC,0 for these cement types is not available.
The regression parameter, kt, is described by a normal distribution with mean of 1.25 and
COV=0.28. The error term, εt, is described by a normal distribution with mean of 315.5
(mm2/years)/(kg/m
3) and COV=0.152.
The environmental function, ke, takes into account the influence of the ambient relative
humidity on the diffusion coefficient of CO2 in concrete and, subsequently, on the carbonation
resistance of concrete. It is described by the following function
5.2
5
5
1
1
ref
real
eRH
RHk (14)
where RHreal is the relative air humidity obtained using data from the nearest weather station,
RHref the reference relative humidity at test conditions, RHref=0.65. To describe RHreal data
(daily mean values) from the nearest weather station are used. Since 0≤RHreal≤1 a distribution
function with a lower limit and an upper limit should be used for its probabilistic model. A
Beta-function is recommended for this purpose. Note that depending on the region the lower
limit of the distribution may be much higher than zero.
The execution transfer parameter, kc, takes into account the influence of curing. It is
described by the following formula
cb
c
c
tk
7 (15)
where tc is the period of curing in days, bc the exponent of regression. Tc is a deterministic
parameter, while bc is described by a normal distribution with mean of -0.567 and
COV=0.042.
The ambient concentration of CO2, [CO2]s, can be presented as the sum of the
concentration of CO2 in the atmosphere, [CO2]atm, and the additional CO2 concentration due
to emission sources (e.g., for road tunnels), [CO2]emi
emiCOatmCOsCO CCC ,,, 222 (16)
[CO2]atm is described by a normal distribution with mean of 8.2×10-4
kg/m3 and COV=0.122.
For usual structures [CO2]s = [CO2]atm.
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The weather function, W(t), takes into account the micro-climate conditions of the
considered concrete surface and is described by the following formula
w
t
ttW
0 (17)
where t0 is the reference time, t the time of exposure, and w the weather exponent. The latter
is estimated as
2
wb
srToWpw (18)
where ToW is the time of wetness, psr the parameter representing the probability of driving
rain, and bw the exponent of regression. ToW represents the average number of rainy days per
year; a day is defined as rainy if the amount of precipitation water during this day ≥2.5 mm,
i.e.
365
mm 5.2rainfallyear with per days ToW (19)
ToW is a deterministic parameter which can be estimated using data from the nearest weather
station. Psr a deterministic parameter representing the average distribution of the wind
direction during raining: for vertical elements it should be evaluated from the nearest weather
station data; for horizontal elements psr=1; for interior elements psr=0. Finally, bw can be
described by a normal distribution with mean of 0.446 and COV=0.365.
The model of carbonation can be used to estimate the probability of corrosion initiation,
Pcorr, which occurs when the carbonation front reaches reinforcing steel, so that
0Pr txctP ccorr (20)
where c the thickness of the concrete cover.
2.2 Corrosion propagation
Carbonation by itself does not cause any damage to a RC structure. In fact, it leads to a
decrease in the concrete porosity and even increases the concrete strength. However, after
corrosion starts, it causes damage to the structure, including cracking of the concrete cover,
reduction and eventually loss of bond between concrete and corroding reinforcement, and
reduction of cross-sectional area of reinforcing steel (i.e., corrosion affects both strength and
serviceability of RC structures). It has been shown that corrosion initially affects the
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serviceability so that a RC element needs to be repaired when corrosion-induced cracking of
the concrete cover becomes excessive (Val 2005). Thus, in this section only models related to
the prediction of the time between the corrosion initiation and excessive cracking are
presented.
2.2.1 Corrosion rate
To predict the development of corrosion-induced deterioration with time the rate of
corrosion needs to be known. The latter is usually described in terms either of the corrosion
current density, icorr, or the corrosion penetration, Vcorr. According to DuraCrete (2000) and
LIFECON (2003), Vcorr can be estimated as
tcorr VwV [μm/year] (21)
where V is the corrosion rate, and wt the relative time of wetness. The corrosion rate is
expressed via the concrete resistivity, ρ(t)
t
mV
0 (22)
where m0=882 μm·Ωm/year. The concrete resistivity is time-variant and depends on a number
of factors
n
RHRTRt
tkkt
0
,,0 (23)
where ρ0 is the specific electrical resistivity of concrete at time t0, kR,T= exp[bR,T(1/Treal-1/T0)]
the temperature factor, bR,T the regression variable, T0=293 K, kR,RH the relative humidity
factor, t0=28 days, t the age of concrete (in analysis t≤1 year), and n the age factor for
resistivity. In analysis ρ0, bR,T, kR,RH and n are treated as random variables, whose statistical
properties are given in Table 2.
Table 2. Statistical parameters of random variables related to corrosion rate
Variable Mean COV Distribution
ρ0 fc’=30 MPa
fc’=40 MPa
fc’=50 MPa
116 Ωm
134 Ωm
155 Ωm
0.16
0.16
0.16
Normal
Normal
Normal
bR,T 3815 K 0.15 Lognormal
kR,RH in splash zone
on coast
1.0
1.07
-
0.13
Deterministic
Lognormal
n 0.23 0.174 Normal
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2.2.2 Cracking of concrete cover
Corrosion products occupy a larger volume than the consumed steel. As they form, they
exert pressure on the concrete surrounding a corroding reinforcing bar. This eventually leads
to cracking of the concrete cover, which poses a serious problem for serviceability of
reinforced concrete structures. There are two important parameters characterizing the
performance of reinforced concrete structures in relation to corrosion-induced cracking: (i) the
time of appearance of a corrosion-induced crack on the concrete surface, tcr1; and (ii) the time
of excessive cracking, tcr. In the following, probabilistic models for their estimation are
described.
Time to crack initiation, tcr1
The time to crack initiation equals
11 cricr ttt (24)
where ti is the time to corrosion initiation and Δtcr1 the time between corrosion initiation and
cover cracking. The latter is estimated using the so-called thick-walled cylinder model (e.g.,
El Maaddawy and Soudki 2007). In this model the concrete around a corroding reinforcing
bar is represented by a hollow thick-walled cylinder with the wall thickness equal to the
thickness of the concrete cover, c, and the internal diameter equal to the diameter of the
reinforcing bar, d. Expansion of the corrosion products is represented by uniform pressure
applied to the inner surface of the cylinder (see Figure 1).
Figure 1. Thick-walled cylinder model.
A relationship between the expansion of corrosion products Δd (i.e., increase of the bar
diameter) and the pressure, P, caused by it is given by the following formula
Pdcc
d
E
dd c
efc
21
2
,
(25)
c
d
c2
13
where νc is the Poisson's ratio of concrete, Ec,ef=Ec/(1+φ) the effective modulus of elasticity of
concrete, Ec the modulus of elasticity of the concrete at age of 28 days, and φ the concrete
creep coefficient.
The concrete cover is fully cracked when the average tensile stress in it becomes equal to
the tensile strength of concrete, fct. The average tensile stress is estimated as the average
tangential stress in the cylinder wall so that the internal pressure causing the concrete cover
cracking, Pcr, equals
d
cfP ct
cr
2 (26)
The expansion of corrosion products can be estimated as (e.g., Chernin and Val 2011)
tVd corrv 12 [μm] (27)
where v is the volumetric expansion ratio of corrosion products, Vcorr is given by Eq. (21)
and Δt the time since corrosion initiation in years.
However, not all corrosion products contribute to the pressure exerted on the surrounding
concrete since a part of them diffuses into concrete pores and microcracks. Denote the relative
fraction of the corrosion products diffused into concrete as η, then the actual expansion of the
corrosion products around a corroding reinforcing bar is Δd(1-η) (Chernin and Val 2011).
Substituting the last result into Eq. (25) and using Eqs. (26) and (27), the time between
corrosion initiation and cover cracking is found as
dcc
d
VE
cft c
correfcv
ctcr
21
11
2
,
1
[years] (28)
The diameter of a reinforcing bar, d, is deterministic and statistical properties of αv and η are
given in Table 3.
Table 3. Statistical parameters of random variables related to cover cracking
Variable Mean COV Distribution
αv 3.0 0.30 Beta on [2.,6.4]
Η 0.70 0.30 Beta on [0.,0.9]
The model does not take into account the actual location of a reinforcing bar within a
structural element and does not distinguish between cracking and delamination. As a result, it
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may lead to significant errors. Based on the comparison with results of more accurate
nonlinear finite element analysis, it has been established that the model should not be used for
predicting the time to crack initiation around internal reinforcing bars in reinforced concrete
slabs when (see Figure 2)
1.5 ≤ c1/d ≤ 4 or 1.5 ≤ c2/c1 ≤ 3.5
In such cases crack initiation can be predicted, e.g., by nonlinear finite element analysis.
(a) (b)
Figure 2. Fragments of reinforced concrete cross-section representing different locations of a
reinforcing bar: (a) corner bar; (b) internal bar.
Time to excessive cracking, tcr
The time of excessive cracking is defined as the time when the width of
corrosion-induced cracks on the surface of a RC element reaches its critical value of 0.4 mm
(i.e. wlim=0.4 mm). The time is estimated using the empirical model proposed by Mullard and
Stewart (2009)
corrcrrcrc
RcrcrirMEk
wktt
0114.005.0lim1 [years] (29)
where icorr is the corrosion current density (in µA/cm2; note that icorr=Vcorr/11.6), kR is the rate
of loading correction factor (introduced because experimental data for the model were
obtained for icorr,exp=100 µA/cm2), kc the confinement factor that accounts for faster crack
growth near external reinforcing bars due the lack of concrete confinement, rcr the rate of
crack growth and MErcr the model error.
pcrcrr ,7.1exp0008.0 (30)
ct
pcrdf
c, 11.0 , pcr (31)
3.0
2500
3.0exp95.0
exp,exp,
corr
corr
corr
corr
Ri
i
i
ik 125.0 Rk (32)
c1
c2 d c2 d c2
15
where ψcr,p is the cover cracking parameter. If a reinfocing bar is in internal location then kc=1;
for reinforcing bars located at edges or corners of RC elements kc is between 1.2 and 1.4. The
model error, MErcr, can be treated as a lognormal random variable with mean of 1.04 and
COV of 0.09.
The probability of serviceability failure due to excessive cracking, Pf,s, at time t (in years)
can then be calculated as
0Pr, tttP crsf (33)
3. Life Cycle Assessment (LCA)
3.1 LCA Basics
LCA has been defined as a systematic analysis to measure industrial processes and
products by examining the flow of energy and material consumption, waste released into the
environment and evaluate alternatives for environmental improvement (Abd Rashid and
Yusoff 2015). It is the most widely accepted and standardised tool to evaluate the
environmental impact of a material or product (Marinković, 2013). ISO 14040 and ISO 14044
set the framework for an LCA study with the aim of assisting in improving the environmental
performance and create a more informed decision-making process within industries by
considering the entire life cycle of a product, from raw material extraction to final disposal or
potential re-use. According to the standards, LCA mainly consists of four stages: 1) The goal
and scope definition phase, 2) The inventory analysis phase, 3) The impact assessment phase,
and 4) The interpretation phase.
3.1.1 The goal and scope definition phase
The goal of the LCA study should be clearly and unambiguously stated and include
the reason for the study and the targeted audience. A goal for an LCA can be a comparison
between different products that serve the same purpose or an investigation of an alternative
solution. Scope definition is drawing the borders of the study and includes the system
boundaries, the functional unit, data requirements and quality, assumptions, etc.
The functional unit is the unit that best illustrates the impact investigated by the specific
study. The common functional unit used for concrete mixtures and precast concrete is one
cubic meter in each building element, such as beams or columns (Gursel et al. 2014).
Defining the system boundaries is stating what parts of the life cycle of a product is
included in the study. For example, one LCA study can be to evaluate the environmental
impact of concrete as a building material and defining the system boundaries from raw
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material extraction to factory gate (cradle to gate), whilst another LCA study can be defining
the system boundaries from raw material extraction to disposal (cradle to grave) (Marinković,
2013), the third type may have a sustainability focus and include recycling and extensive
reuse (cradle to cradle) (Vieira et al. 2016). The choice of system boundaries depends on the
goal of the study.
3.1.2 The inventory analysis phase
From the first stage, a plan for all the product’s processes that are within the specified
system boundaries can be drawn, from there the inputs and outputs of each process need to be
determined, relevant data collected and calculations made in line with the functional unit. For
example, some of the unit processes in an LCA of a concrete structure can be the extraction of
raw material for cement and transportation, manufacturing of cement, production of concrete
and transportation, construction, use and maintenance of the building, demolishing and
recycling. Data acquisition and calculation can become long and tedious depending on the
system, that is why there are some databases available to help with the process (Marinković,
2013).
One of the sources of controversy in the inventory analysis phase and indeed in the LCA
is the allocation, i.e. allocating the weight or contribution of a specific element to the system.
The debate on the allocation manifests itself best with the use of SCMs (i.e. fly ash, slag and
silica fume) in cement and concrete production. The core of the debate is how to divide the
environmental impact of these by-products between the concrete industry and other industries.
On one hand, if not used in concrete then SCMs become just waste; on the other hand, putting
all the impact on one industry is not fair. That is where the allocation can be used so that the
environmental impact is divided between the two industries. The most common approach is
mass allocation (based on the ratio of the mass of the by-product and the mass of the product
plus by-product), but some argue that this allocation is not fair for the concrete industry and
will eventually drive this industry away from these by-products. Thus, there are claims that
economic allocation would be more suitable (Marinković, 2013).
3.1.3 The impact assessment phase
The aim of Life Cycle Impact Assessment (LCIA) is to connect the inputs with the
outputs based on the chosen functional unit (e.g. fossil fuels with emissions). In this phase
assigning impact categories, category indicators and characterization models are the
foundations for the phase. The process, however, is vulnerable to subjectivity that is why
17
transparency in choice making is pivotal.
3.1.4 The interpretation phase
This phase consists of three main aspects. First, results from the previous steps should
be summarised and any significant issues or ‘interesting’ results need to be highlighted.
Second, evaluation, the aim of which is to increase the confidence and reliability of the study,
which includes completeness check, sensitivity check and consistency check. Finally,
conclusions, recommendations and limitations should be made to summarise the study and
help with decision making.
3.2 Implementation of LCA in the project
The project aims to evaluate the environmental and economic aspects of concrete
structures, by assessing CO2 emissions and absorption using LCA, and production and
maintenance costs using LCCA. The carbonation process in concrete, including CO2
absorption, will be evaluated using the models described in Section 2.1 (mainly, the fib Model
Code model). To account for emissions associated with structural repairs, if such are needed
due to damage caused by carbonation-induced corrosion, the corrosion propagation models
presented in Section 2.2 will be employed. Relevant uncertainties associated with the
prediction of carbonation and damage due to carbonation-induced corrosion will be taken into
account. Thus, results of the LCA will obtained and, subsequently, presented in a probabilistic
format. The probabilistic LCA will be carried out using Monte Carlo simulation.
3.2.1 The LCA goal and scope
The assessment will be performed for a concrete element (e.g. beam, column, slab, etc.).
In order for the results to be comparable, the Functional Unit (FU) choice is kg of
CO2/element and £/element. For illustrative purposes kg of CO2 /m3 of concrete and £/m
3 of
concrete will also be used. The system boundaries are from raw materials extraction to
demolishing that includes raw material extraction, raw material transportation, cement
manufacturing, concrete mixing, transport, placement, use (only maintenance from
carbonation-induced corrosion will be considered) and demolishing. The allocation for SCM
in the production of concrete will be mass based as per common practice. Otherwise, all other
materials accounted in the LCA are specifically for the concrete production so that they will
18
have full environmental weight. Figure 3 shows the boundaries, inputs and outputs of the
LCA process.
Figure 3. Boundaries, inputs and outputs of the LCA
Concrete materials extraction and
production (cement, aggregate, water,
reinforcement and SCMs)
Concrete production
Concrete placement
Concrete structure demolished
Transport to concrete plant
Transport to site
Beginning of life span
End of life span
CO2 emissions
Cost
CO2 emissions (maintenance)
Cost
CO2 absorption (carbonation)
CO2 absorption
(carbonation)
Cost
Fuel consumption
(CO2)
Embodied CO2
Fuel consumption
(CO2)
Fuel consumption
(CO2)
Fuel consumption
(CO2)
Embodied CO2
CO2 emissions
Cost
Fuel consumption
(CO2)
CO2 emissions
Cost
Fuel consumption
(CO2)
CO2 emissions
Cost
CO2 emissions
Cost
19
3.2.2 Life Cycle Inventory (LCI) analysis
The type of data and data sources required in the model are described in the following
table.
Table 4. Data required for LCI analysis
Phase Data required Data source
Preliminary Location Determined
Climate conditions Section 3.2.2.1
Element properties and
dimensions
Determined
Cement mix Determined
Raw concrete materials
(including transportation to
concrete plant)
CO2 emissions and cost Section 3.2.2.2
Concrete batching,
transportation and placement
CO2 emissions and cost Section 3.2.2.3
Use and maintenance/repair CO2 absorption
(carbonation).
CO2 emissions (repair) and
cost
Section 3.2.2.4
Demolishing CO2 absorption
(carbonation)
and cost
Section 3.2.2.5
CO2,total = RCO2 + PCO2+ UCO2+ DCO2 (34)
Costtotal = RCost + PCost + UCost + Dcost (35)
where CO2 ,total and Costtotal are the total CO2 balance and total cost incurred from the
element under consideration, respectively; RCO2 and RCost are the CO2 emissions and cost
associated with concrete raw materials; PCO2 and PCost are the CO2 emissions and cost
associated with producing, transporting and placing the concrete element on the site; UCO2 and
UCost are the CO2 uptake and cost for the life span of the concrete element (maintenance cost);
and DCO2 and Dcost are the CO2 uptake after the concrete element is demolished and cost of
demolishing the concrete element, respectively.
3.2.2.1 Climate conditions
Climate conditions are one of the main factors controlling the carbonation process. Thus,
to accurately predict the progress of concrete carbonation over time the corresponding
changes in climate conditions need to be taken into account. The IPCC projections for future
20
climate include a number of scenarios, while every scenario includes three projections of CO2
emissions, namely, low emissions, medium emissions and high emissions. As the CO2
concentration affects other climatic parameters (temperature, relative humidity, etc.), each
scenario corresponds to different values of the climatic parameters. Thus, to account for
climate change, first, a projection scenario needs to be chosen and then the CO2 concentration
and other climatic parameters need to be determined accordingly. For the UK, the Met Office
has developed climate projections in accordance to the IPCC climate scenarios, UKCP09. A
summary of the implications of each scenario along with the projected values of climatic
parameters can be found in the UKCP09 Briefing Report (Jenkins et al. 2009).
3.2.2.2 Raw concrete materials (including transportation to concrete plant)
Table 5 and Table 6 give values of CO2 emissions from Portland cement, SCMs and
factory-made cements from cradle to gate. An average transportation impact is 11 kg of
CO2/tonne of Portland cement and 7 kg of CO2/tonne of SCM. Reinforcement roughly share
the same value as SCM. The transportation impact is added proportionally according to
cement type. For example, if a cement is 30% SCM and 70% Portland cement then
transportation impact is (0.3 * 7) + (0.7 * 11) (Concrete Centre 2015).
Table 5. Embodied CO2 for main constituents of reinforced concrete
Material Embodied CO2 (kg of CO2/tonne of
material)
Portland cement CEM I 913
Ground granulated blast furnace slag (GGBS) 67
Fly ash (FA) 4
Aggregate 5
Reinforcement 427
Table 6. Embodied CO2 for some factory-made cements
Cement Type Addition (%) Embodied CO2 (kg of CO2
/tonne of material
(low – High)
CEM II/A-LL or L 6 – 20 Limestone 859 - 745
CEM II/A-V 6 – 20 FA 858 - 746
CEM II/B-V 21 – 35 FA 722 - 615
CEM II/B-S 21 – 35 GGBS 735 - 639
CEM III/A 36 – 65 GGBS 622 - 363
21
CEM III/B 66 – 80 GGBS 381 - 236
CEM IV/B-V 36-55 FA 598 - 413
The average embodied CO2 in aggregate including transport is 9.3 kg of CO2 /tonne
(Concrete Centre 2011).
3.2.2.3 Concrete batching, transportation and placement
Emissions from transportation are fairly easy to determine based on the knowledge of the
exact location and supplier practices, however, the determination may become complicated
when introducing factors such as traffic and probable cause of delay for other road users, also,
if the round trip for the concrete truck has multiple goals or deliveries. Concrete Centre (2017)
gives an average value of 8.4 kg of CO2/tonne of concrete delivered. Flower et al. (2007)
found the emissions associated with concrete batching and placement to be 0.0033 tonnes of
CO2/m3 of concrete and 0.009 tonnes of CO2/m3, respectively. However, these figures vary
drastically between different sites.
3.2.2.4 Use and maintenance
Use phase
The only considered aspects in the use phase are CO2 emissions from maintenance/repair,
CO2 absorption from carbonation and cost of repairs. The amount of CO2uptake by concrete
due to carbonation (UCO2) will be calculated using the below (Yang et al. 2014)
UCO2= 𝑥𝑐 × 𝐴𝑠𝑓× 𝑎𝐶𝑂2 (36)
where 𝑥𝑐 is the carbonation depth, 𝐴𝑠𝑓 is the is the exposed surface area of concrete and
𝑎𝐶𝑂2 is the CO2 – binding capacity of concrete, which can be estimated using Eq. (7). The
degree of hydration, αH, in this equation can be calculated as (Yang et al. 2014)
𝛼𝐻 (t) = 𝛼∞ 𝑡
2 + 𝑡 (37)
𝛼∞= 1.031 × 𝑊/𝐶
0.194 + 𝑊/𝐶 (38)
In this study, carbonation depth will be modelled using the fib model (2006) – see Section
2.1. The summary of the model parameters and their description is given in Table 7 below.
22
Table 7. Carbonation modelling parameters
Parameter Distribution Mean Standard deviation
Concrete cover (c) Lognormal Determined in design 8-10 mm without
execution
requirement
Relative Humidity for the
carbonated layer, RHreal
Beta
distribution
Determined using
UKCP09
Determined using
UKCP09
Reference relative humidity,
RHref
Deterministic 65% -
Exponent (ge) Deterministic 2.5 -
Exponent (fe) Deterministic 5 -
Curing period (tc) Deterministic Determined in design -
Exponent of regression (bc) Normal -0.567 0.024
Regression parameter (kt) Normal 1.25 0.35
Error term (εt) Normal 315.5 48
CO2 concentration (Cs)
(kg/m3)
Normal Determined from
CO2concentration
projection (IPCC)
COV=0.122
Life span (t) Deterministic Determined in design -
Reference time (t0) (years) Deterministic 0.0767 -
Probability of driving rain
(Psr)
Deterministic Depends on exposure
conditions
-
Time of wetness (ToW) Deterministic Number of days in a
year with precipitation
>2.5 mm / 365
-
Exponent of regression (bw) Normal 0.446 0.163
Maintenance/repair
As explained previously, repair is required when either corrosion starts or
corrosion-induced cracking becomes excessive. The models for the calculation of the time
these events are described in Sections 2.1 and 2.2, respectively. The repair consists in
removing the damaged (i.e. carbonated) concrete layer and replacing it with a new one. The
replaced concrete will be the same as the original one and be treated in the same way, i.e.
carbonation will take place as if it were a new structure. It is assumed that the rubble from
removing the damaged concrete will be disposed of immediately and will have no
contribution to CO2 uptake. It is also assumed that repair will take place in the following year
of which failure has occurred. Finally, it is assumed that the repair process happens instantly
and does not affect the concrete element service life (i.e. no indirect costs).
23
3.2.2.5 Demolishing, disposal and recycling
According to the literature, CO2 emitted during demolishing is complex to estimate
and have a very small contribution. Hence, it will be neglected in this study. According to
(Mineral Products Association 2016), the crushed concrete structure remains on site for an
average of 26 weeks before it is used, either as landfill or RCA. Both uses have a contribution
to the CO2 life cycle of a concrete structure either by underground carbonation or allocation,
but they will not be considered in this study as the limit will be the removal from site.
Carbonation in this phase follows the same principles as in the use phase, the only difference
is in estimating the new surface area available for carbonation after demolishing (𝐴𝑠𝑓).
According to Yang et al. (2014) the surface area of the demolished concrete can be calculated
using the following equation
𝐴𝑠𝑓 = π × H × L × (D−2𝑥𝑐)/𝑑𝑎 (39)
where H, L and D are the element’s height length and depth, respectively, and 𝑑𝑎 is the
average diameter of the resulted aggregate from demolishing. It is hard to predict the diameter
of the demolished concrete as the procedure is far from being standardised. If the demolished
concrete is crushed to be disposed of as landfill, a typical value for the diameter is 0.1 m
(Pommer and Pade 2005).
4. Life Cycle Cost Analysis (LCCA)
4.1 LCCA Basics
Generally, to compare different alternatives with the same benefits the life-cycle cost of a
structure up to time t, LCC(t), can be expressed as
)()()()( tCtCtCCCtLCC FMINCD (40)
where CD is the design cost, CC the construction cost (materials and labor, i.e. CC basically
equals RCost+Pcost from Eq.(35)), CIN(t) the cost of inspections, CM(t) the cost of
maintenance/repair (equivalent to UCost in Eq. (35)), and CF(t) the cost of failure (damages,
cost of life, injury). If to assume that CD, CIN(t) and CF(t) are similar for different alternatives
then Eq. (40) may be simplified
)()( tCCtLCC MC (41)
Since repairs of different alternatives may occur at different times in order to obtain consistent
24
results the present worth method (e.g. Val and Stewart 2003) can be employed, which
involves discounting costs of failure to their present values
Mt
MM
r
ctC
1)( (42)
where cM is the cost of repair set at the time of decision making, tM the time of repair, and r
the discount rate. Discount rates are influenced by a number of economic, social and political
factors and can be quite variable (typically, discount rates vary between 2% and 8%).
Due to uncertainties associated with material properties, loads and environmental
conditions repairs associated with serviceability failures are random events with
time-dependent probabilities of occurrence. It is typical to consider failures at discrete points
in time so that their probabilities are equal to the cumulative probability of failure over a
corresponding time interval, e.g., one failure event per year with a corresponding annual
probability. Thus, CM(t) is a discrete random variable which at failure times ti assumes
different values, ci, given by Eq. (42), i.e.
it
Fi
r
cc
1 (43)
with probabilities of occurrence, pi. Subsequently, LCC(t) becomes a discrete random variable
as well. Decision making is then usually based on its expected value E[LCC(t)]
)()( tCECtLCCE MC (44)
where E[CM(t)] is the expected cost of repairs. For example, for a single structural element,
which will need to be repaired only once during t years of service, and when cM is assumed to
be independent of the time of repair E[CM(t)] is estimated as
N
i
iiM cptCE1
)( (45)
where N is the number of points in time at which the possibility of repair is considered.
4.2 Relevant costs
Estimating costs associated with construction is difficult because the cost data varies
significantly if at all found. However, for the aim of this study, indicative costs were obtained
from the literature and market research. The values are summarised in Table 8.
25
Table 8. Cost for concrete materials and processes
Item/process Price in £
CEM I 46 (per tonne)
Fly ash 40 (per tonne)
GGBS 44 (per tonne)
Aggregate 18 (per tonne)
Reinforcing steel 0.75 (per kg)
Concrete patching, transport and placement 95 (per m3)
Demolishing 35 (per m3)
5. Case study
5.1 Description
The case study chosen for analysis is an internal reinforced concrete column in a
multi-storey car park in London, UK, with the following properties (Table 9).
Table 9. RC column characteristics
Characteristic Value
Cross-sectional dimensions 0.4×0.4 m
Height 3 m
Concrete compressive strength 42.5 N/mm2
Reinforcement 0.02m @ 0.25 m c/c (7850 kg/m3 density)
Concrete cover 0.035 m
Finishing None
Life span 100 years (starting from 2020)
Curing period 28 days
Three different cement mixes are to be compared CEM I, CEM I + 22% FA (k = 0.5) and
CEM III/B. No distinction in the aggregate size has been made. In the case of CEM I + 22%
FA any reference to the cement content (C) is replaced with (C + (addition × k)). Table 10
describes the quantities of materials used to produce the column for the three different mixes.
26
Emissions associated with producing, transporting and placing of the column were calculated
using Section Error! Reference source not found..2 and converted to the functional unit.
The CO2 emissions calculations from cradle to the placement of the concrete element on the
site were approached deterministically due to a lack of data on uncertainty of relevant
parameters.
Table 10. Concrete mixes constituents
Material Mix 1 (kg/m3) Mix 2 (kg/m
3) Mix 3 (kg/m
3)
Portland cement CEM I 370 296.4 102.6
FA 0 83.6 0
GGBS 0 0 277.4
Water 205 186 205
Aggregates (0-20mm) 1800 1800 1800
The climate scenario chosen for the study is a medium emissions scenario. Since it is
assumed that the service life of the structure is 100 years, values of average climatic
conditions for the period between 2020 and 2120 were considered. Table 11 shows relevant
climatic variables for the current and future climate conditions, which will be used in the
analysis.
A VBA program has been written in Excel for carrying out probabilistic analysis based
the models described above and using Monte Carlo simulation. The program estimates the
carbonation depth, CO2 uptake by carbonation in service life, number and cost of repairs, CO2
emissions from maintenance and the probability of serviceability failure due to corrosion (in
this study it is related to corrosion initiation). The discount rate used in the analysis is 4%.
The simulation is run 200,000 times to ensure sufficient accuracy and results are expressed in
terms of a mean and standard deviation of the estimated random parameters. To calculate the
carbonation depth and CO2 uptake by recycled concrete after demolishing it is assumed that
the crushed concrete will remain on site unsheltered. It also also assumed that the concrete
element will be crushed immediately after the end of service life for the purpose of disposal
by landfill. The values of the Psr and da have been selected accordingly. The volume of
27
reinforcement is subtracted from the total element volume. The total CO2 balance term refers
to the total CO2 emissions minus the total CO2 uptake.
Table 11. Climatic variables
Parameter Current climate Future climate
Cs.atm (mean value) kg/m3 0.00082
0.001058
Cs.atm (standard deviation) kg/m3 0.0001 0.0001
Cs.emi (mean value) kg/m3 9.84×10
-6 12.70×10
-6
Cs.emi (standard deviation) kg/m3 0.93×10
-6 1.2×10
-6
Relative humidity (mean value) 78% 76.1%
Relative humidity (standard deviation) 1.18% 2.34%
ToW days 100 days 68
5.2 Results
5.2.1 Embodied CO2
The calculations for embodied CO2 revealed that the concrete raw materials provide the
main contribution to the total embodied CO2. Moreover, climate change only affects CO2
associated with maintenance, which, except for Mix 3, is very small. Figure 4 shows the mean
values of embodied CO2 for different processes and constituents of the element. However, the
values associated with CO2 emissions from maintenance have a high coefficient of variation
originating from high values of the correponding standard deviations. The standard deviations
for future climate conditions are 4.009, 1.425 and 17.685 for Mix 1, Mix 2 and Mix 3,
respectively. For current climate conditions, the values are 1.5, 0.434 and 12.575 for the same
order of mixes.
5.2.2 Carbonation depth and CO2 uptake
The mean values of CO2 uptake in service life for both climate conditions are shown in
Figure 5 for the three mixes. Table 12 presents values of the mean and standard deviations of
carbonation depth and the standard deviation for CO2 uptake in service life for both climate
conditions for the three mixes.
28
Figure 4. Mean values of embodied CO2 in the RC element
Since climate change affects the parameters governing the carbonation depth, and
carbonation depth, in turn, is a governing parameter for CO2 uptake, it is natural for the CO2
uptake to increase with climate change. Climate change leads to an increase in the carbonation
depths.
205.492 205.492
173.628 173.628
96.382 96.382
16.688 16.688
16.688 16.688
16.688 16.688
15.316 15.316
15.280 15.280
15.316 15.316
0.357 0.056
0.058 0.006
13.703 6.349
0
20
40
60
80
100
120
140
160
180
200
220
240
MIX 1 (FUTURE CLIMATE)
MIX 1 (CURRENT CLIMATE)
MIX 2 (FUTURE CLIMATE)
MIX 2 (CURRENT CLIMATE)
MIX 3 (FUTURE CLIMATE)
MIX 3 (CURRENT CLIMATE)
kg
of
CO
2/e
lem
ent
Mean embodied CO2 in the concrete element
Embodied CO_2 from use phase (maintenance)
Embodied CO_2 from concrete patching, transport and placement
Embodied CO_2 from reinforcement
Embodied CO_2 from concrete raw materials
29
Figure 5. Mean CO2 uptake by the RC element
Table 12. Values of carbonation depths and CO2 uptake in the RC element
Parameter Current climate Future climate
Mix 1 Mix 2 Mix 3 Mix 1 Mix 2 Mix 3
Carbonation depth (mm) (mean) 12.75 10.59 28.02 15.31 12.73 35.83
Carbonation depth (mm)
(standard deviation)
3.25 2.72 8.19 4.01 3.31 11.50
CO2 uptake in service life
(standard deviation) (kg/element)
1.533 1.172 3.823 1.89 1.425 5.334
5.2.3 Costs and repairs
The cost from cradle to the RC element placement has been calculated deterministically.
However, the number of repairs and the cost of repairs were calculated by the probabilistic
analysis. The model also produced the probability of failure (i.e. probability of corrosion
initiation) for the life span of the RC element. A comparison of the mean values of repair costs
is made in Figure 6. Table 13 shows the initial cost of the element (excluding repairs) and
7.219 6.013 5.474 4.554
16.827
13.188
2.437
2.024 1.849
1.539
5.035
4.190
0
2
4
6
8
10
12
14
16
18
20
22
MIX 1 (FUTURE CLIMATE)
MIX 1 (CURRENT CLIMATE)
MIX 2 (FUTURE CLIMATE)
MIX 2 (CURRENT CLIMATE)
MIX 3 (FUTURE CLIMATE)
MIX 3 (CURRENT CLIMATE)
kg o
f C
O2/e
lem
en
t
Mean CO2 uptake
CO_2 uptake from service-life CO_2 uptake after demolishing
30
illustrates uncertainty associated with repairs by showing the probability of failure and
number of repairs (mean and standard deviation).
Figure 6. Mean cost of repairs for the RC element
Table 13. Repairs and costs of the RC element
Parameter Current climate Future climate
Mix 1 Mix2 Mix 3 Mix 1 Mix 2 Mix 3
Number of repairs (mean) 0.0014 0.0002 0.214 0.008 0.0017 0.426
Number of repairs
(standard deviation)
0.038 0.0145 0.0417 0.0894 0.0414 0.546
Initial cost of element, £ 115.26 115.24 115.00 115.26 115.24 115.00
Cost of repairs (standard
deviation), £
0.056 0.020 1.359 0.148 0.056 2.372
Probability of failure 0.0014 0.0002 0.2137 0.0081 0.0017 0.4256
0.01129 0.00156 0.00203 0.00027
1.25793
0.49324
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.10
1.20
MIX 1 (FUTURE CLIMATE)
MIX 1 (CURRENT CLIMATE)
MIX 2 (FUTURE CLIMATE)
MIX 2 (CURRENT CLIMATE)
MIX 3 (FUTURE CLIMATE)
MIX 3 (CURRENT CLIMATE)
£
Mean cost of repairs
31
The mean total CO2 balance (including the contribution of CO2 uptake) is presented in
Figure 7.
Figure 7. Mean total CO2 balance
5.3 Discussion
As expected the CO2 emissions from raw concrete materials of have the greatest share of
the total CO2 emissions with percentages of 86.4% and 84.4% for Mix 1 and Mix 2,
respectively, for future climate conditions. For these two mixes there is practically no
difference in the share of the raw materials emissions between current and future climate
conditions. However, for Mix 3 this percentage for future climate is 67.8% and it is almost 4%
higher for current climate. This can be explained by the fact that climate change only affects
CO2 emissions associated with maintenance. For Mix 1 and Mix 2, the number of repairs is
low for both climate conditions, resulting in very low maintenance CO2 emissions. On the
other hand, the number of repairs for Mix 3 is significantly higher, especially for future
climate conditions, resulting in relatively high maintenance CO2 emissions.
Mix 2 and Mix 3 show a reduction in CO2 emissions by 13.5% and 40.3% compared to
228.198 229.515
198.332 199.510
120.228 117.358
0
30
60
90
120
150
180
210
240
MIX 1 (FUTURE CLIMATE)
MIX 1 (CURRENT CLIMATE)
MIX 2 (FUTURE CLIMATE)
MIX 2 (CURRENT CLIMATE)
MIX 3 (FUTURE CLIMATE)
MIX 3 (CURRENT CLIMATE)
kg
of
CO
2/e
lem
ent
Mean total CO2 balance
32
Mix 1. Mix 3 shows an impressive 30.9% reduction in CO2 emissions compared with Mix 2,
which clearly illustrates the ability of GGBS to reduce CO2 emissions. Of course, the high
replacement ratios in Mix 3 plays a big role in achieving such a result. These results are
consistent with data found in the literature. For example, Tait and Cheung (2016) provide
similar results; minor differences are thought to be because of different ratios of concrete
constituents (e.g. cement content, aggregate).
Climate change impact reveals itself the most in the use phase and after it. The CO2
uptake for service life increase by 20.1%, 20.2% and 27.6% from current climate to future
climate for Mix 1, Mix 2 and Mix 3, respectively. The carbonation depth, being the governing
parameter of CO2 uptake, follows the same trend. Additionally, CO2 uptake for the demolished
concrete increases by around 20% for all the three mixes.
CO2 uptake absorbs back 3.4%, 3% and 12.9% of the total CO2 emitted in the concrete
element life cycle in current climate conditions for Mix 1, Mix 2 and Mix 3, respectively; the
percentages increases to 4.1%, 3.6% and 15.4% in the same order for future climate
conditions. Again, Mix 3 excels in CO2 performance by absorbing 126.4% more CO2 in total
CO2 uptake than Mix 1, whilst, Mix 2 showed a disappointing 24.2% decrease in the same
category.
CO2 uptake for the demolished concrete is around 25% of the total CO2 uptake.
Considering that the concrete was crushed to an average diameter of 0.1 m and remained on
site for 0.5 years (both values are conservative), the contribution is remarkable and can play a
bigger role in reducing the total CO2 balance in concrete structures. If the crushing process
and the afterward disposal are designed to maximise CO2 uptake, the benefits to be reaped can
be significant.
It should be noted that studies covering all parameters included in this study are relatively
rare. Yet, carbonation depth, CO2 uptake in service life and after demolishing for Mix 1 and
Mix 3 are consistent with other studies and is well within the range found in the available
literature (Yoon et al. 2007; Kikuchi and Kuroda, 2011; Andersson et al. 2013; Yang et al.
2015b; Andr Es Salas et al. 2016). However, Mix 2 results are rather suspicious. In a study by
Lye et al. (2015) that investigated almost all available literature on carbonation in FA cements,
it has been found that the use of FA in concrete gives a rise of carbonation depth by about 50%
33
compared to CEM I. That makes the decrease of 24.2% from this study irrational and
inconsistent. The lower than expected carbonation depth and consequently less CO2 uptake for
Mix 2 is originated from the inverse carbonation resistance (𝑅𝐴𝐶𝐶,0−1 ) value found in the fib
(2006).
Mix 2 and Mix 3 show a decline in the total CO2 balance by 13.1% and 47.3%,
respectively, compared to Mix 1. Mix 3 reduced the total CO2 balance by 39.4% compared to
Mix 2. Furthermore, climate change drops the total CO2 balance by 0.6% for Mix 1 and Mix 2
from current to future climate conditions. On the other hand, for Mix 3 the same parameter
surprisingly increases by 2.4%. This can be explained by CO2 emissions from maintenance.
When comparing Mix 3 to the other two mixes, it is superior in every aspect except for
maintenance. The carbonation depth in Mix 3 is 143% and 182% larger than Mix 1 and Mix 2,
respectively. This leads to a higher number of failures (with an average of 150 times more
than other mixes) and, consequently. higher CO2 emissions from maintenance. Mix 3 has
higher values of the carbonation depth that increases its CO2 uptake but also shortens its life
span and necessitates more repairs; the same conclusion was made by Andr Es Salas et al.,
(2016). For Mix 3, in current climate conditions, CO2 emissions from maintenance are 48.1%
of the CO2 uptake in service life and for future climate conditions, the percentage reaches
almost 81.4%.
The mentioned issue of the increased number of failures for Mix 3 and its associated
consequences can be avoided if the sustainability and durability issues are to be considered
earlier and integrated into the design. To illustrate, the effects of increasing the concrete cover
for Mix 3 by 5 mm and 15 mm were investigated and compared to the original design. The 5
mm increase in the concrete cover brings down the number of repairs by 77.6%. CO2
emissions from maintenance decrease by 58.2%, the CO2 uptake during the service life also
decreases but only by 5.5%. Replacing with a 50 mm concrete cover reduces the number of
repairs by 773.8%. The CO2 emissions from maintenance and CO2 uptake from the service life
drop by 548% and 11.6% respectively. However, the element was designed according to the
current standards. If the developed model would have been used alongside the standards at the
design stage, a design that optimises CO2 balance, cost and performance could have been
achieved.
34
6. Conclusions
The LCA-LCCA analysis of a case study has shown that the use of ‘green’ concretes in
RC structural elements leads to a reduction in carbon dioxide emissions, while the life-cycle
cost of the elements remains similar to that of the elements made from Portland cement
concrete. However, the use of ‘green’ concretes increases the probability of
carbonation-induced corrosion, in particular in conditions of climate change. Since the
analysis has taken into account only direct costs associated with the repair of the RC element
damaged by corrosion, the result may change when indirect costs (e.g. loss of income due to
interruption of the structure services, losses incurred on users of the structure, etc.) will be
considered as well. The indirect costs are usually much larger than the direct costs but much
more difficult to estimate.
Acknowledgments
This research was supported by the HORIZON 2020 Marie Skłodowska-Curie Research
Fellowship Programme H2020 - 658475, titled: Climate-resilient pathways for the
development of concrete infrastructure: adaptation, mitigation and sustainability (ClimatCon).
References
Abd Rashid AF, Yusoff S (2015) A review of life cycle assessment method for building
industry. Renewable and Sustainable Energy Reviews, 45, 244–248.
Andersson R et al. (2013) Calculating CO2 uptake for existing concrete structures during and
after service life. Environmental Science and Technology, 47(20), 11625–11633.
Andr Es Salas D et al. (2016) Environmental impacts, life cycle assessment and potential
improvement measures for cement production: a literature review. Journal of Cleaner
Production, 113, 114–122.
Bastidas-Arteaga E, Schoefs F, Stewart MG, Wang X (2013) Influence of global warming on
durability of corroding RC structures: A probabilistic approach. Eng Struct, 51, 259-266.
Castel A, Francois R, Arliguie G (1999) Effect of loading on carbonation penetration in
reinforced concrete elements. Cem Concr Res, 29, 561-565.
CEB (1997) New approach to durability design - An example for carbonation induced
corrosion. CEB Bulletin No. 238, CEB, Paris.
Chernin L, Val DV (2011) Prediction of corrosion-induced cover cracking in reinforced
concrete structures. Construct Build Mater, 25, 1854-1869.
Concrete Centre (2017). Specifying Sustainable Concrete: Understanding the role of
35
constituent materials. MPA The Concrete Centre.
DuraCrete (1998) Modelling of Degradation, Document BE95-1347/R4-5, The European
Union – Brite EuRam III.
Eamon CD, Jensen EA, Grace NF, Shi X (2012) Life-cycle cost analysis of alternative
reinforcement materials for bridge superstructure considering cost and maintenance
uncertainties. J Mater Civil Eng, ASCE. 24, 373-380.
El Maaddawy T, Soudki K (2007) A model for prediction of time from corrosion initiation to
corrosion cracking. Cem Concr Compos, 29, 168-175.
fib (2006) ModelCode for Service Life Design. fib Bulletin 34, fib, Lausanne.
fib (2013) Integrated life cycle assessment of concrete structures: State-of-the-Art Report. fib
Bulletin 71, fib, Lausanne.
Flower DJM, Sanjayan JG, Hellweg S (2007) Green house gas emissions due to concrete
manufacture. Int J LCA, 12(125), 12–282.
Gursel AP, Masanet E, Horvath A, Stadel A (2014) Life-cycle inventory analysis of concrete
production: A critical review. Cem Concr Compos, 51, 38-48.
Haselbach L (2009) Potential for carbon dioxide absorption in concrete. J Environ Eng, ASCE,
135(6) 465-472.
Jenkins GJ et al. (2009) UK Climate Projections: Briefing report. Met Office Hadley Centre,
Exeter, UK. http://ukclimateprojections.defra.gov.uk
Kendall A, Keoleian GA, Helfand GE (2008) Integrated life-cycle assessment and life-cycle
cost analysis model for concrete bridge deck applications. J Infrastruct Syst, ASCE, 14(3)
214-222.
Kikuchi T, Kuroda Y (2011) Carbon Dioxide Uptake in Demolished and Crushed Concrete.
Journal of Advanced Concrete Technology, 9(1) 115–124.
Larrard T, Bastidas-Arteaga E, Duprat F, Shoefs F (2014) Effects of climate variations and
global warming on the durability of RC structures subjected to carbonation. Civ Eng
Environ Syst, 31(2) 153-164.
Lepech MD, Geiker M, Stang H (2014) Probabilistic design and management of
environmentally sustainable repair and rehabilitation of reinforced concrete structures.
Cem Concr Compos, 47, 19–31.
LIFECON (2003) Deliverable D 3.2: Service Life Models. European Community: Fifth
Framework Program.
Lloyd SM, Ries R (2007) Characterizing, propagating, and analyzing uncertainty in life-cycle
36
assessment. J Ind Ecol, 11, 161-179.
Marinković SB (2013) Eco-efficient concrete: 3. Life cycle assessment (LCA) aspects of
concrete. Woodhead Publishing Ltd, Sawston, Cambridge, UK.
Meyer C (2009) The greening of the concrete industry. Cem Concr Compos, 31(8) 601-605.
Mineral Products Association (2016) Whole-Life Carbon and Buildings: Concrete solutions
for reducing embodied and operational CO2.
Mullard JA, Stewart MG (2011) Corrosion-induced cover cracking: new test data and
predictive models. ACI Struct J 108(1) 71–79.
Müller HS, Haist M, Vogel M (2014) Assessment of the sustainability potential of concrete
and concrete structures considering their environmental impact, performance and lifetime.
Constr Build Mater, 67, 321-337.
Narasimhan N, Chew MYL (2009) Integration of durability with structural design: An optimal
life cycle cost based design procedure for reinforced concrete structures. Constr Build
Mater, 23, 918-929.
Pade C, Guimaraes M (2007) The CO2 uptake of concrete in a 100 year perspective. Cem
Concr Res, 37, 1348–1356.
Papadakis VG (2000) Effect of supplementary cementing materials on concrete resistance
against carbonation and chloride ingress. Cem Concr Res, 30, 291-299.
Papadakis VG, Vayenas CG, Fardis MN (1991) Fundamental modeling and experimental
investigation of concrete carbonation. ACI Mater J, 88, 363-373.
Pommer, K. and Pade, C. (2005) Guidelines - Uptake of carbon dioxide in the life cycle
inventory of concrete.
http://www.nordicinnovation.org/Global/_Publications/Reports/2005/03018_guidelines_-
_uptake_of_carbon_dioxide_in_the_life_cycle_inventory_of_concrete.pdf
Stewart MG, Wang X, Nguyen MN (2012) Climate change adaptation for corrosion control of
concrete infrastructure. Struct Saf, 35, 29-39.
Ta V-L, Bonnet S, Kiesse TS, Ventura A (2016) A new meta-model to calculate carbonation
front depth within concrete structures. Constr Build Mater, 129, 172-181.
Tae S, Baek C, Shin S (2011) Life cycle CO2 evaluation on reinforced concrete structures
with high-strength concrete. Environ Impact Assess Rev, 31(3) 253-260.
Tait MW, Cheung WM (2016) A comparative cradle-to-gate life cycle assessment of three
concrete mix designs. Int J LCA, 21(6), 847-860.
Taylor HFW (1997) Cement Chemistry. 2nd
ed., Thomas Telford, London.
Talukdar S, Banthia N, Grace JR (2012a) Carbonation in concrete infrastructure in the context
37
of global climate change – Part 1: Experimental results and model development. Cem
Concr Compos, 34(8) 924-930.
Talukdar S, Banthia N, Grace JR (2012b) Carbonation in concrete infrastructure in the context
of global climate change – Part 2: Canadian urban simulations. Cem Concr Compos, 34(8)
931-935.
Val DV (2005) Effect of different limit states on life-cycle of RC structures in corrosive
environment. J Infrastruct Syst, ASCE, 11, 231-240.
Val DV, Stewart MG (2003). Life-cycle cost analysis of reinforced concrete structures in
marine environments. Struct Saf, 25(4) 343-362.
Van den Heede P, De Belie N (2012) Environmental impact and life cycle assessment (LCA)
of traditional and ‘green’ concretes: Literature review and theoretical calculations. Cem
Concr Compos, 34, 431-442.
Vieira DR, Calmon JL, Coelho FZ (2016) Life cycle assessment (LCA) applied to the
manufacturing of common and ecological concrete: A review. Constr Build Mater, 124,
656–666.
Wu P, Xia B, Zhao X (2014) The importance of use and end-of-life phases to the life cycle
greenhouse gas (GHG) emissions of concrete – A review. Renew Sustain Energy Rev, 37,
360-369.
Yang K-H, Seo E-A, Tae S-H (2014) Carbonation and CO2 uptake of concrete. Environ
Impact Assess Rev, 46, 43–52.
Yang K-H et al. (2015) Effect of supplementary cementitious materials on reduction of CO2
emissions from concrete. Journal of Cleaner Production, 103, 774–783.
Yoon I-S, Çopuroğlu O, Park K-B (2007) Effect of global climatic change on carbonation
progress of concrete. Atmospheric Environment, 41(34) 7274-7285.
