Moisture and Bio-Deterioration Risk of Building Materials and Structures

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Moisture and Bio-Deterioration
Risk of Building Materials and Structures
Hannu Viitanen
VTT
Finland
1. Introduction
During the service life of buildings, natural aging and eventual damage of materials due to
different chemical, physical, and biological processes can take place. Ageing of the materials
is one aspect of the environmental processes and involve different chemical, mechanical and
biological reactions of the materials. Bio-deterioration, e.g. mould, decay and insect damage
in buildings, is caused when moisture exceeds the tolerance of structures which may be a
critical factor for durability and usage of different building materials.
Modelling of the development of mould growth and decay development is a tool for
evaluate the eventual risk of ambient humidity or moisture conditions of materials for bio-
deterioration of materials. The modelling can be used in combination of hygro-thermal
analyses of building and building components.
Moisture availability is the primary factor controlling mould growth and decay
development, but the characteristics of the substrate and environmental conditions
determine the dynamics of the growth. However, moist materials may also dry and become
wet again thus, resulting in fluctuating moisture conditions. Mould and decay problems in
buildings are most often caused by moisture damage: water leakage, convection of damp air
and moisture condensation, rising damp from the ground and moisture accumulation in the
structure. Repeated or prolonged moisture penetration into the structure is needed for
damage to develop.
2. Critical environmental conditions for bio-deterioration
There are several biological processes causing aging and damage to buildings and building
components. This is due to natural ageing of materials but also caused by excessive moisture
and damage of materials. For mould development, the minimum (critical) ambient humidity
requirement is shown to be between RH 80 and 95 % depending on other factors like
ambient temperature, exposure time, and the type and surface conditions of building
materials (Table 1) For decay development, the critical humidity is above RH 95 %. Mould
typically affects the quality of the surfaces and the adjacent air space with volatile
compounds and spores. The next stage of moisture induced damage, the decay
development, forms a serious risk for structural strength depending on moisture content,
materials, temperature and time. The worst decay damage cases in North Europe are found
in the floors and lower parts of walls, where water accumulates due to different reasons.

Mass Transfer - Advanced Aspects
580
Type of
organism
Damage / problem type Humidity or moisture range
(RH or MC %)
Temperature
range (°C)
bacteria bio corrosion of many
different materials, smell,
health problems
wet materials
RH > 97 %
ca. -5 to +60
mould fungi surface growth on
different materials,
smell and health problems
Ambient RH > 75 %,
depends on duration,
temperature and mould species
ca. 0 to +50
blue-stain
fungi
blue-stain of wood
permeability
change of wood
Wood moisture content > 25 -
120 %
RH > 95 %
ca. -5 to +45
decay fungi different types of decay in
wood (soft rot, brown rot
or white rot), also many
other materials can be
deteriorated,
Strength loss of materials.
Ambient RH > 95 %,
MC > 25 - 120 %, depends on
duration, temperature, fungus
species and materials
ca. 0 to +45
algae and
lichen
Surface growth of different
materials on outside or
weathered material.
wet materials
also nitrogen and
low pH are needed
ca. 0 to +45
insects Different type of damage
in organic materials,
surface failures or
strength loss.
Ambient RH > 65 %
depends on duration,
temperature, species and
environment
ca. 5 to +50
Table 1. Organisms involving damages and defects of building components (Viitanen and
Salonvaara 2001, Viitanen et al. 2003)
In Northern Europe, the roofs, floors and lower parts of walls are most often exposed to
high humidity and potential attack by biodeterioration processes (Paajanen and Viitanen
1989, Viitanen 2001a, Kääriäinen et al. 1998) when also decay will develop. For the decay
development, the humidity and moisture conditions will be higher than that for mould
growth, and modelling of decay risk is a separate task. Mould growth is often typical in
materials in exterior conditions. In damage conditions, however, different decay types can
be found: brown rot, soft rot, and white rot. In buildings suffering from excessive moisture
loading, brown rot is the most common decay type (Paajanen and Viitanen 1989, Viitanen
2001a).
The other a-biotic factors like UV radiation and quality of substrate (nutrients, pH,
hygroscopicity, water permeability) are also significant for the growth of organisms.
Different organisms, e.g. bacteria, fungi and insects, can grow and live in the building
materials; microbiologically clean buildings probably do not exist, as some contamination
begins as early as during the construction phase. The humidity / moisture conditions
connected with temperature and exposure time are the most important factor for
development of biological problems and damage in buildings.
The research and modelling of mould growth is most often performed under constant
conditions when the ambient humidity conditions and microclimate will prevail for longer
periods. Ayerst (1969) and Smith and Hill (1982) studied the effect of temperature and water

Moisture and Bio-Deterioration Risk of Building Materials and Structures
581
activity on germination and growth of selected mould fungi. They developed isopleths for
the growth conditions of mould fungi on agar media. An isopleths is a boundary that
defines all combinations of temperature and relative humidity that permit a particular
mould growth rate. Grant et al. (1989) analysed and modelled the moisture requirements of
some mould fungi isolated from dwellings. A certain succession, depending on the moisture
requirements of different fungal species: primary, secondary and tertiary colonizers, was
found.
Research with building materials will be better fitted to the moisture problems in buildings.
Adan (1994) used a non-linear regression technique to model sigmoid curves describing
vegetative fungal growth of Penicillium chrysogenum on gypsum board material. He used the
time-of-wetness (TOW) as an overall measure of water availability for fungal growth under
fluctuating humidity conditions. The TOW is defined by the ratio of the cyclic wet period
(RH ≥ 80 %) and the cyclic dry period. The mould growth is a function of the effect of lowest
humidity, time of wetness and high relative humidity frequency, and finally of periods of
wet and dry conditions. He used Low Temperature Scanning Electronic Microscope LTSEM
to analyse the growth and studied the effect of coatings and surface quality on the mould
growth. He also evaluated the effect of distribution of growth density on test results. Clarke
et al. (1998) developed a simulation model and tool for mould growth prediction in
buildings based on an analysis of published data using growth limit curves for six generic
mould categories. These limits have been incorporated within the ESP-r (building Energy
Software) system for use in conjunction within combined heat and moisture flow
simulation.
3. Modeling of development of mould growth
3.1 Mould growth on building materials
Modelling of mould growth and decay development based on humidity, temperature,
exposure time and material will give tools for the evaluation of durability of different
building materials and structures. The models make it possible to evaluate the risk and
development of mould growth and to analyse the critical conditions needed for the start of
growth of microbes and fungi. The model is also a tool to simulate the progress of mould
and decay development under different conditions on the structure surfaces. This requires
that the moisture capacity and moisture transport properties in the material and at the
surface layer have been taken into account in the simulations. In practice there are even
more parameters affecting mould growth, e.g. thickness of the material layers combined
with the local surface heat and mass transfer coefficients. Therefore, the outcome of the
simulations and in-situ observations of biological deterioration may not agree. One of the
results of a newly finished large Finnish research project "Modelling of mould growth" is an
improved and extended mathematical model for mould growth based on development of
mould index in different materials under different exposure conditions (table 2).
Hukka and Viitanen (1999) and Viitanen et al. (2000) presented a model of mould growth
which is based on duration of suitable exposure conditions required before microbial
growth will start or the damage will reach a certain degree. Particular emphasis is focused
on this time period, the so-called response time or response duration, in different humidity
and temperature conditions for the start of mould growth (Figure 1). The model is based on
the large laboratory studies on Scots pine and Norway spruce sapwood.

Mass Transfer - Advanced Aspects
582
70
75
80
85
90
95
100
0 5 10 15 20 25 30
Time (weeks)
RH (%)
1 °C
5 °C
10 °C
20 °C
Fig. 1. Critical humidity (RH %), time (weeks) and temperature needed to start mould
growth on pine sapwood (Viitanen 1996)
The growth of mould in this model was evaluated using the “mould index” scale shown in
Table 2. The model can be used to evaluate the mould growth in different exposure
conditions, and it can be introduced to building physic modeling to evaluate the
performance of different structure. The model is not suitable for evaluate the development
of decay, for which different models exist (Viitanen 1996, Viitanen et al. 2000).
The model describes also the dynamic nature of mould growth under varying temperature
and humidity conditions as it gives the predicted mould index as a function of time.
Simulation results with the model show that under fluctuating humidity, the mould index
will decrease during low humidity or temperature periods, depending on the time periods
(Figure 2). This kind of behaviour can also be found in the “Modelling of mould growth"
study (Viitanen et al 2010).
0
1
2
3
4
5
6
0 28 56 84 112 140 168 196
Time (days)
Mould index
AConstant RH
12/12 h 97/75 RH
6/12 h 97/75 RH
3 / 21 h 97/75 RH
0
1
2
3
4
5
6
0 28 56 84 112 140 168 196
Time (days)
Mould index
B
6/42 h 97/75 RH
7/14 d 97/75 RH
3/4 d 97/75 RH
7/23 d 97/75 RH
1/6 d 97/75 RH
Fig. 2. Modelling the effect of varied fluctuating humidity conditions on the development of
mould index in pine sapwood (Viitanen et al. 2000)
The original index is based on wood materials (Viitanen and Ritschkoff 1991a). New
determinations for index levels 3 and 4 for other materials are presented using bold fonts
and has been presented by Viitanen et al (2011a).

Moisture and Bio-Deterioration Risk of Building Materials and Structures
583
Index Description of the growth rate
0 No growth
1 Small amounts of mould on surface (microscope), initial stages of
local growth
2 Several local mould growth colonies on surface (microscope)
3 Visual findings of mould on surface, < 10 % coverage, or,
< 50 % coverage of mould (microscope)
4 Visual findings of mould on surface, 10 - 50 % coverage, or,
>50 % coverage of mould (microscope)
5 Plenty of growth on surface, > 50 % coverage (visual)
6 Heavy and tight growth, coverage about 100 %
Table 2. Mould index for experiments and modeling of mould growth on building materials
Sedlbauer (2001) studied different models to evaluate spore germination and growth of
different mould species on different types of materials. He found, that the isopleths
developed by growth of mould on an artificial medium can be used to evaluate the growth
rate of different fungi. He used a hygrothermal model based on the relative humidity,
temperature and exposure time needed for the spore germination of mould fungi based on
the osmotic potential of spores. He analysed the effect of different climatic conditions on the
spore moisture content and germination. He also evaluated the spore moisture content and
germination time based on calculated time courses of temperature and relative humidity in
various positions of the exterior plaster of an external wall using WUFI program (Sedbauer
and Krus 2003). In Figure 3, a comparison of the critical conditions for mould growth
assumed by some of mould growth models is shown. These curves represent lower limiting
isopleths (humidity levels) for mould growth.
Fig. 3. Comparison of the LIM’s of substrate class 1 (LIM I, biodegradable materials) and
substrate class 2 (LIM II, porous materials) after Sedlbauer (2001) with data from results of
building materials after Viitanen et al. (2000), Clarke et al. (1998) and Hens (1999)

Mass Transfer - Advanced Aspects
584
The first version of the mould growth model was based on large laboratory studies with
pine sapwood (Viitanen and Ritschkoff 1989). The mould growth intensities were
determined at the constant conditions. In the later stages, studies in varied and fluctuated
humidity conditions were performed and based on these studies, mould growth model
(equation 1) was presented by Hukka and Viitanen 1999.
12
1
7 exp( 0.68ln 13.9 ln 0.14 0.33 66.02)
dM kk
dt T RH W SQ
=⋅− − + − + (1)
where the factor k1 represents the intensity of growth (Equation 3), W is the timber species (0
= pine and 1 = spruce) and SQ is the term for surface quality (SQ = 0 for sawn surface, SQ =
1 for kiln dried quality) based on Hukka and Viitanen (1999).
For other materials than wood the value SQ = 0 is used, which omits this factor. Numerical
simulation is typically carried out using one hour time steps (climate data intervals) and
hours are used in the equations instead of days.
1
3
1
1 when 1
2 when 1
1
M
M
M
kM
t
t
=
=
≤
⎧
⎪
⎪
=⎨>
⎪−
⎪
⎩
(2)
In the equation, the factor tM=1 is the time needed to start of the growth (M = 1, Table 2), and
tM=3 the time needed to reach the level M =3. The factor k2 (Equation 3) represents the
moderation of the growth intensity when the mould index (M) level approaches the
maximum peak value in the range of 4 < M < 6.
()
2max
max 1 exp 2.3 ,0kMM
⎡
⎤
⎡⎤
=− ⋅−
⎣⎦
⎣
⎦ (3)
where the maximum mould index Mmax level depends on the current conditions (Equation
4):
2
max 17 2
100 100
crit crit
crit crit
RH RH RH RH
MRH RH
⎛⎞
−−
=+⋅ −⋅
⎜⎟
−−
⎝⎠
(4)
In Equation 4 RHcrit is the limit RH level to start the mould growth (Viitanen et al 2011a).
For other materials than wood, the model has to be modified. The new mould growth
intensity factors are presented as relative values compared to those of the reference material
pine by using Equations (5) and (6).
1,
1
1
Mp
ine
M
t
kt
=
=
= when M < 1 (5)
(
)
3, 1,
1
31
2
Mp
ine M
p
ine
M
M
tt
ktt
==
=
=
−
=⋅ − when M ≥ 1 (6)
where tM=1 is the time needed for the material to start the growth (Mould index reaches level
M = 1), and tM=3 the time needed for the material to reach level M =3. The subscript pine refers
to the value with the reference material pine.

Moisture and Bio-Deterioration Risk of Building Materials and Structures
585
The mould growth maximum values set restrictions for the growth and limit the index to
realistic levels. For the new set of materials the equation of the maximum mould index level
was written in form shown in equation 7 (Ojanen et al 2010, Viitanen et al 2011a):
2
max 100 100
crit crit
crit crit
RH RH RH RH
MAB C
RH RH
⎛⎞
−−
=+⋅ −⋅
⎜⎟
−−
⎝⎠
(7)
In this equation the coefficients A, B and C can have values that depend on the material
class. The new Mmax has an effect on the factor k2 (Equation 3) and it contributes to the
simulation results. Table 4 presents the maximum levels of mould index values for different
materials under different conditions. These results were classified to material sensitivity
groups, presented both for growth intensities and maximum mould index levels. Table 3
gives the values for the growth intensity parameter k1 classes and for the coefficients of the
maximum mould index factors Mmax and k2. The factor RHmin represents the minimum
humidity level for starting mould growth for each material group.
k
1 k
2 (Mmax) RHmin
Sensitivity class M<1 M≥1 A B C %
very sensitive, vs 1 2 1 7 2 80
sensitive, s 0.578 0.386 0.3 6 1 80
medium resistant, mr 0.072 0.097 0 5 1.5 85
resistant, r 0.033 0.014 0 3 1 85
Table 3. Parameters for the sensitivity classes of the updated mould model (Ojanen et al
2010, Viitanen et al 2011a)
Sensitivity
class Materials
Very
sensitive Pine sapwood
Sensitive
Glued wooden
boards, PUR (paper
surface), spruce
Medium
resistant
Concrete, aerated
and cellular
concrete, glass wool,
polyester wool
Resistant PUR polished
surface
Table 4. Mould growth sensitivity classes and some corresponding materials in the research.
The figure in table illustrates the predicted mould growth for the established sensitivity
classes for constant conditions at 97 % RH and 22 C (Viitanen et al 2011)
0
1
2
3
4
5
6
0
7
13
20
26
33
40
46
53
59
66
73
79
86
92
99
106
112
119
Time [wee ks]
Mould Inde x
very sens itive
sensitiv e
medium resistant
resi sta nt

Mass Transfer - Advanced Aspects
586
The factors presented in Table 3 form the new basis for numerical simulation of mould
growth on different material surfaces. These values will be applied in the following studies
where the model performance will be evaluated.
Table 4 represents the tested materials, whose resulting mould indexes were used for the
determination of k1 for the respective classes. The k1 classes were determined by using
expert estimation for most suitable values.
3.2 Decline of the mould growth and mould index caused by frost or dry condition
As living organisms mould fungi need water and suitable temperature to grow. When
conditions are unfavourable for fungi, activity of mould fungi will be inactivated depending
on the extent of the frost or dryness and the time periods of unfavourable conditions. In
Figure 4, the humidity and temperature conditions of microclimate for the favourable and
unfavourable conditions for mould growth is shown. The rate of humidity and temperature
and the time periods in favourable and unfavourable conditions will affect on the growth
rate of mould. Especially the longer periods in low humidity or temperature will cause
decline of the mould growth and development and even the decline of mould index.
Fig. 4. Illustration of the regimes for the favourable and unfavourable conditions for mould
growth (Viitanen et al 2011a)
The decline of mould growth on wooden surface has been modelled based on cyclic changes
between two humidity conditions (Equation 8). The decline of mould index under different
fluctuating conditions is modelled and shown in the figure 2.
1
1
1
0.00133, when - 6 h
0, w 6 h 24 h
0.000667, when - 24 h
tt
dM hen t t
dt tt
−≤
⎧
⎪
=≤−≤
⎨
⎪−>
⎩
(8)
where M is the mould index and t is the time (h) from the moment t1 when the conditions
on the critical surface changed from growth to outside growth conditions (Hukka and
Viitanen 1999).
Under long period seasonal variations of humidity conditions the decline of mould index
may differ from that presented in Equation 8. Also the material may have a significant effect
65
70
75
80
85
90
95
100
-15 -10 -5 0 5 10 15 20 25 30 35
T, oC
% RH
65
70
75
80
85
90
95
100
-15 -10 -5 0 5 1 0 15 20 25 30 35
T, oC
% RH
Mould growth
conditions
No mould growth
Decline of mould index
Critical R H curve

Moisture and Bio-Deterioration Risk of Building Materials and Structures
587
on the decline process. The decline of mould index for other materials was presented using a
constant, relative coefficient for each material (Equation 9).
0
mat
mat
dM dM
C
dt dt
=⋅ (9)
where (dM/dt)mat is the mould decline intensity for each material, (dM/dt)0 is that for pine
in the original model (Equation 9), and Cmat is the relative coefficient for mould index
decline used in the simulation model. The original decline model for wood could be applied
using these additional factors (Ojanen et al 2010, Viitanen et al 2011a).
The relative decline of mould for different materials was determined using laboratory
experiments with walls (Ojanen et al 2010). The temperature and relative humidity
conditions on the critical boundary layer between two different materials were monitored
continuously. The mould index level of the material surfaces was determined with suitable
intervals by opening the structure from three different parts. The experimental target
conditions at the interface of the two materials are presented in Table 5.
These experimental walls had mould growth after the first warm and humid period
(‘Summer/autumn’). The mould decline was determined by the change of the mould index
during the second period, a four month long ‘Winter’ period causing freezing temperatures
at the critical boundary. The mould index values were determined for both material surfaces
on each critical interface. Figure 11 presents the relative mould decline values (Cmat) solved
from the observations in the experiments. The results include the detected mean, minimum
and maximum mould index values.
Stage 1 2 3 4
Season Summer/autumn Winter Spring High exposure
Time, months 7 4 6 12
RH % 80 … 100 92 … 100 60 … 95 94 … 100
Temperature °C 27 … 18 -5 … +3 2 … 10 20 … 24
Table 5. Exposure conditions during the wall assemble test (Ojanen et al 2010)
The decline of mould intensity on different materials under unfavourable mould growth
conditions could be presented as decline classes (Table 6). This classification is based on few
measurements with relatively large scattering and it should be considered as the first
approximation of these classes. It was found, that the decline was larger within wood
Ceff Description
1.0 Pine in original model, short periods
0.5 Significant Relevant decline
0.25 Relatively low decline
0.1 Almost no decline
Table 6. Classification of relative mould index decline (Ojanen et al 2010)

Mass Transfer - Advanced Aspects
588
4. Modeling of development of decay
4.1 Causes for decay damages in buildings
The excessive water into the building structure and materials is the basic cause to different
bio-deterioration problems like decay. For instance, in washrooms water often penetrates
through inside surfaces or pipe leakage into the structures causing long lasting high
humidity conditions. In old wooden buildings, the floor has often been built above a cold
ventilated basement or crawl space, where high humidity conditions may exist. If water is
penetrated in the crawl space, the ventilation may not keep the floor dry and mould growth
is obvious. If ventilation caps are closed, severe decay problems have been found, e.g. dry
rot damage (Paajanen and Viitanen 1989, Kääriäinen et al. 1998).
In connection of the decay, also microbial contamination on the surfaces in the crawl space
is typically much higher than inside the building. The level of fungal spores in the crawl
space is about ten times as high as indoors. In crawl spaces, spore concentrations in a range
of 103-105 colony-forming units per gram (cfu/g) of material are common. The levels have
usually been highest on wood-based boards and on timber (Hyvärinen et al. 2002). In cases
of heavy fungal colonisation, airborne spore concentrations of up to 103-104 cfu/m3 have
been detected.
The slab-on-ground structure without thermal insulation below the concrete slab has been
used in old buildings. This type a floor is very sensitive for water damage and microbial
growth. Especially in detached houses built between 1960 and 1980, wooden beams are
often supported on concrete slabs on grade. Partial decay or insect damage is often found in
the lower sill plate of exterior walls due to water penetration from the basement (Kääriäinen
et al. 1998).
Decay is the more severe result of high moisture exposure of wooden structures when the
materials are wet for long periods. According to laboratory studies, the growth of decay
fungi and decay development can start when the ambient humidity level in the
microclimate remains for several weeks above RH 95 – 100 % and moisture content of pine
sapwood above 25 – 30 % (Viitanen and Ritschkoff 1991b, Morris et al 2006). According to
experience, decay will develop when moisture content of wood exceeds the fibre saturation
point (RH above 99.9 % or wood moisture content 30 %, but also the variation of conditions
and temperature has an important effect.
4.2 Modeling the decay development
4.2.1 A model for decay development in pine sapwood
There is always a vide variation within the growth condition of different fungus species,
and we need overall evaluation on the growth activity and decay development of a
“typical” example fungi like typical decay fungi (e.g. Coniophora puteana or Gloeophyllum
sepiarium). VTT has done comprehensive research in mould and decay growth and their
numerical modelling on timber (Viitanen 1996, Viitanen et al 2003) presented a model of
decay development in pine and spruce sapwood.
Later a new model was developed from the work presented in references Viitanen and
Ritschkoff (1991b), Viitanen (1996), and Viitanen (1997). In these references, the decay
growth of brown rot in spruce and pine sapwood is studied experimentally in different
constant relative humidity and temperature conditions. In the present model, only the data
of pine sapwood is considered. Based on the experimental findings presented in references,
a model for variable conditions is proposed (Toratti et al 2009)). This model is a time
stepping scheme. The development of decay is modelled with two consecutive processes:

Moisture and Bio-Deterioration Risk of Building Materials and Structures
589
a) Activation process:
This is termed as α parameter, which is initially 0 and gradually grows depending on the air
conditions to a limit value of 1. This process is able to recover in favourable conditions (dry
air) at a given rate (although no experimental evidence of recovery is available).
b) Mass loss process:
This occurs when the activation process has fully developed (α=1) otherwise it does not
occur. This process is naturally irrecoverable.
These processes only occur when the temperature is 0..30 °C and the relative humidity is
95% or above. Outside these condition bounds, the activation process may recover, but the
mass loss process is simply stopped. The activation process is as given in Equation 2.
The recovery time (i.e. α recovers from a value of 1 back to 0) is assumed to be 17520 hours
(2 years). Recovery takes place when the conditions are outside the bounds of the decay
growth.
The model can be used for evaluation the exposure condition for the eventual risk of decay
to develop. For example, recorded temperatures and relative humidity are given for the
Helsinki area. This climate is shown in the figure 1 for a one year period. According to the
model, this climate seems to induce a low mass loss of 1.1 % in 4 years (Figures 4 and 5).
During the first year, no decay development will occur in untreated pine sapwood. After 3
and 4 years exposure, decay is expected to occur only to a very limited extent in the surface
of unprotected pine sapwood. Under normal use conditions, the cladding is protected by
paints or other coatings. The direct influence of water on the wood surface is very small, and
decay development will be significantly retarded or even negligible.
0
0
Activation process 0..1
() ( ) , where
or (in favorable conditions of decay)
(,)
2.3 0.035 0.024
(,) 3024 [hours]
42.0 0.14 0.45
tt
crit
crit
td
t
tRHT
TRHTRH
tRHT TRH
α
ααα
α
=
==Δ
Δ
Δ=
+−×
⎡⎤
=××
⎢⎥
−+ +
⎣⎦
∑
∫
(10)
The mass loss process proceeds the activation process, when α has reached 1(Eq. 11).
1
1
24 4
Massloss process when 1
(,) (,)
()
(,)5.96 10 1.96 10 6.25 10 [%/ ]
tt
tat
tat
ML RH T ML RH T
ML t dt t
dt dt
ML RH T TRHhour
dt
α
α
α
′′
=
=
−− −
≥
⎛⎞
′==×Δ
⎜⎟
⎝⎠
=− × + × + ×
∑
∫ (11)
For advanced decay to develop, a significantly longer period is needed, and after a 10 years
period, severe decay in unprotected and uncovered pine sapwood can be expected in the
Helsinki area. The design of details has a strongly marked effect on the durability and

Mass Transfer - Advanced Aspects
590
service life of wood structure. If there is a detail collecting the water, the moisture conditions
are suitable for long time for decay to develop. If the structure and details are well planned
so that there is no water sink and the structure can be dried after occasionally wetting, the
conditions for decay development will not be reached, and there are actually no limits for
the service life of wood.
4.2.2 Evaluation of decay risk in different part of Europe using decay model
The empirical wood decay model was run using the ERA-40 data for air temperature,
humidity and precipitation at 6 hour intervals (Viitanen et al 2010b). ERA-40 is a massive
data archive produced by the European Centre of Medium-Range Weather Forecasts
(ECMWF). The reanalysis involves a comprehensive use of a wide range of observational
systems including, of course, the basic synoptic surface weather measurements. The ERA-40
domain covers all of Europe and has a grid spacing of approximately 270 km. The nature of
the data and the reanalysis methods of ERA-40 are described in detail in Uppala et al. [2005].
The evaluation of decay development in the model is based on the mass loss caused by the
decay fungus. Within specified limitations, the mass loss is an applicable variable for
evaluating the decay development in wood. The decay development model will give a
general assumption of the effect of humidity, temperature and exposure time on the start
and progress of the decay.
The resulting modelled mass loss in 1961-1970 at the calculation points of the ERA-40 grid
were analyzed by a chart production software producing a maps of wood decay in Europe
(Figure 7). First a map on decay risk protected from rain and then a map on decay risk of
pine sapwood exposed to rain. A modification of the weather data was made so that the
humidity of air was set to 100% during precipitation (at non-freezing temperatures) as this is
thought to result in a full saturation in the wood surface.
-40
-20
0
20
40
60
80
100
0 2000 4000 60 00 8 00 0 10000
Time [h]
Temp C or RH%
RH
te mp
Fig. 5. Measured climate data (Helsinki) used in the decay model for one year (Viitanen et al
2010b)

Moisture and Bio-Deterioration Risk of Building Materials and Structures
591
α
Mass loss [%]
Mass loss [%]
1.2
1
0.8
0.6
0.4
0.2
0
Time
[
years
]
1 2 3 4 5
1.2
1
0.8
0.6
0.4
0.2
0
α
Fig. 6. No activation of growth or decay development during the first and second years, an
activation of decay process after 4 years exposure may be expected (Viitanen et al 2010b)
Fig. 7. a) Modelled mass loss (in %) of small pieces of pine wood that are protected from the
rain or b) exposed to rain in 10 years in Europe (from [Viitanen et al. 2010b, 2011b)
The risk of decay activity in different part of Europe can be evaluated on the map. If we
evaluate the decay activity rate in Helsinki to be 1, then the decay risk in north-western part
of Portugal and in West Ireland is 2 times and in Atlantic part of France and Belgium it will

Mass Transfer - Advanced Aspects
592
be between 2 and 2.5 times higher than that in Helsinki. In North Scandinavia it would be
between 0.5 and 0.25, which will point out, the effect if climate on risk of decay development
in outdoor structure varied vide within Europe. These coefficients can be used as one step to
evaluate the effect of macroclimate conditions on service life of cladding and decking.
Another way to evaluate the macroclimate conditions is presented by Thelandersson et al
(2011) using Meteonorm climate data. By calculating the daily dose and accumulating the
dose for one year a measure of the risk of decay is obtained. This is made for several sites,
and the result in terms of dosedays can be compared between the different sites. To be able
to compare different sites, the dose was transferred to a relative dose by dividing it by the
dose for the “base-station” Helsinki. Due to the variation of climate across Europe, relative
doses between 0.6 (northern Scandinavia) and 2.1 (Atlantic coast in Southern Europe) were
obtained.
5. Conclusion
There are several factors involved with the bio-deterioration of materials and buildings, and
mathematic modelling that may help us to understand the complicated interaction of many
factors. The presented numerical mould growth and decay development models are based
on experimental results from several research projects. It is suitable for post-processing
temperature and humidity data from any numerical simulation of hygrothermal conditions
in building constructions. However, it must be kept in mind when performing the
assessment that there is a great uncertainty coupled to this kind of analysis: the variation of
the material sensitivities is high, estimation of a product sensitivity class is difficult without
testing, the surface treatments may enhance or reduce growth potential, different mould
species have different requirements for growth and the evaluation of the actual conditions
in the critical material layers may include uncertainties. The best way to use the predicted
mould growth and decay development as an assessment tool is to compare different
solutions with each others: The solution with the lowest risk for the mould growth or decay
development would most probably also have least other moisture related problems
6. Acknowledgment
This chapter is based on several research performed at VTT and other research institutes
and universities.
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