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Service Life Prediction Based on Hard Data

P.I. Morris

Forintek Canada Corp.

2556 East Mall, Vancouver BC.

Abstract

Service life prediction was a lot easier when one preservative, creosote, and one woodspecies, Scots pine, were used for a simple commodity, utility poles, for over 150 years

and the only changes were in the treatment process and the treatment quality. More

recently, efforts have been made to predict the performance of complex wood frame

buildings. In the absence of hard data on many of the key parameters, default values andadjustment factors have been estimated based on expert opinion. The number of factors,

the lack of data on those factors and the uncertainty around expert estimates make service

life prediction for buildings using a factor method highly speculative. It has, however,been possible to fit an equation to data on stake tests of wood preservatives with at least

some parameters that relate directly to known preservative properties. Unfortunately the

step-wise rating system used for stake tests does not lend itself to accurate modeling.Experiments need to be designed specifically for service life prediction. Even then we

will not be able to predict the performance required by the consumer just from the three

years field test data typically required for standardization of a wood preservative. Data

from accelerated lab tests may be useful for refining parameters in modeling field data.Accelerated tests are useful provided they do not change the deterioration hazard such

that the agents of deterioration have a different capacity to overcome to the preservative

or modification treatment. In any simulation of field exposure, the use of realistic inocula

is critical for valid data since, for example, treatments for use above ground that mayprotect against basidiospores may not be resistant to mycelia or mycelial cords. Recently

building scientists have developed models to predict how long wood components inbuildings will stay at particular moisture contents given known climate, building design

and construction faults. Forintek is providing the data that will show the effect these

moisture contents and times might have in terms of strength loss. Finally, methods ofservice life prediction need to be checked with real life data and not matched against

perception. Concrete and steel are perceived to be durable materials but in a survey of

demolished buildings, wood frame buildings were demolished at a more advanced

average age than concrete and steel buildings.

Introduction

Having experimented with service life prediction several times over the past 20 years, I

can confidently say I can now predict the service life of any wood product in any hazard

class (use category) as 50 years 50 years 19 times out of 20 (p=0.05). Unfortunately

specifiers and consumers are looking for a somewhat greater degree of precision.Furthermore this is becoming increasingly difficult as product life cycles shorten and

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manufacturers look for more rapid standardization and regulator acceptance of their

product. It was not too difficult to believe chromated copper arsenate (CCA) wouldprovide an acceptable service life when it was introduced to the US standards in the

1960s since there were field tests dating back 30 years. The manufacturers were prepared

to put 40-year warranties on the product, and these have been lengthened as further

decades of data were gathered. Now we have preservatives standardized on the basis ofthree years field test data in North America and on the basis of laboratory test data only

in most of Europe. These field and laboratory tests were designed for preservative

treated wood, primarily for structural applications in ground contact and may not beappropriate for physically or chemically modified wood in appearance applications above

ground.

In this paper I will address some key issues around service life prediction based on my

painful experiences in this field. First, I will reflect on how easy service life prediction

was just 20 years ago. I will then proceed to discuss some ten-year old work on a risk

assessment model using what is now called the factor method and similar vintage

attempts to model preservative field test data. I will digress a little to address the use oftropical test sites to predict service lives in temperate zones and summarize our current

efforts to develop data for damage functions for leaky buildings. Finally I will illustratehow important it is to compare the results of expert predictions to real service life data.

Service Life Prediction based on Historic Data

Simpler times

My earliest foray into service life prediction was made in much simpler times only 20

years ago (Morris and Calver 1985). The largest treated wood volumes were utility poles

and railway sleepers (ties) essentially unchanged for over 150 years, treated withcreosote, essentially unchanged for 150 years, using a treatment process with few

modifications since patenting by John Bethell in 1838. Scots pine was virtually the only

wood species used for utility poles. The major influences on service life were treatmentprocess (Bethell, Reuping or Lowry process) and treatment quality. The UK electricity

supply industry had experienced premature failures of creosoted poles installed during

the 1950s approaching 20% of the population after 20 years in service. They wereexpecting the same rate of failure in more recently installed poles, so we examined

inspection data to see what was really happening. We projected 1974 inspection data

forward 10 years and compared it to 1984 inspection data. This showed in the morerecently installed poles a longer period before premature decay showed up and the

probability of premature decay did not increase as rapidly with age. The data alsoshowed in the older poles a peak in decay incidence coinciding with the peak in pole

installations in 1956 when treaters could not keep up with demand and maintain quality.The subsequent drop in decay incidence could be attributed to the introduction of a

quality assurance scheme in 1958. Based on this examination of historic data we were

able to predict the peak of replacements anticipated in the 1990s would not occur. Wealso identified the need to shift focus from premature failure caused by basidiomycete

attack on the untreated interior of inadequately penetrated poles to mature failure caused

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by soft rot attack on the treated exterior of properly treated poles that had suffered

preservative depletion (Morris and Calver 1985). This made my PhD and postdoctoralresearch on remedial treatment of inadequately penetrated poles of rather less

consequence than I had imagined and I promptly gave it up and left the country. Since

then, the issues I have had to deal with have rapidly become a lot more complex.

Risk Assessment Model for Durable Wood Construction

Hard data or adjustment factors

It was ten years before I ventured back into the arena of service life prediction inresponse to increasing problems with moisture intrusion in wood frame buildings.

Together with Forinteks senior wood engineer I attempted to develop a framework for

modeling the durability of wood buildings consisting of 1) life cycle considerations, 2) adurability risk assessment model and 3) a performance evaluation model. (Varoglu and

Morris 1996). Although we did not get very far with this effort, some examples of our

thinking might be useful to those who would take this work further.

The first aspect covered issues for consideration and information required to make

decisions at each stage of a building life cycle, from conceptual design through final

design, construction, maintenance/repair and demolition. The first steps towards a riskassessment model looked at the demand on durability, the durability capacity and the

degree of loss due to failure. Durability demand factors included hazard class (use

category), climate, local conditions, exposure, required service life and adverse designeffects (Morris 1994). Durability capacity factors included material properties, design,

construction quality, treatment, maintenance/repairs. Degree of loss due to failure was

considered to include consequences of failure and effort required for maintenance and

repair.

In the area of demand, we got as far as breaking down exposure effects into tendency to

wet and rate of drying and the parameters influencing both of them. We also looked atadverse design effects such as water trapping and water uptake and the parameters

affecting each of these. We believed that some of these parameters would be measurable

and if the data were not available, we could set up experiments to quantify their effects.For other parameters not so easy to quantify we resorted to default values and adjustment

factors but there were an awful lot of them (Varoglu and Morris 1996). For example, for

rate of drying of wood components, we considered protection from sun, windiness andnumber of wood surfaces exposed. Since climate data are typically available from

widely spaced locations, we felt that adjustment factors might be necessary to deal withlocal variants such as fog, rain or wind caused by proximity to oceans, lakes or

mountains. For the required service life we took the categories from CSA S478-1995,temporary (0-10 years), short life (10-24 years), medium life (25-49 years), long life (50-

99 years) and permanent (>100 years). We also created a matrix of weighted factors to

deal with consequences of failure and effort required for maintenance and repairs.Consequences included none, inconvenience, some disruption of service, major

disruption of service to injury and death. Effort required for maintenance and repairs

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were none, minor, moderate and major effort, replacement and finally irreplaceable. At

this point, we already had a dozens of adjustment factors and no idea of the accuracy oftheir estimation. The task of verifying these factors seemed insurmountable.

The next intended step was a performance evaluation model in which durability capacity

would be matched against durability demand considering consequences of failure. If thecapacity provided by the conceptual design was inadequate to meet the demand with an

acceptable level of maintenance and repair and a sufficiently low probability of serious

consequences, the design would be adjusted as needed.

Unfortunately, we did not even get as far as completing the risk assessment model when

the level of building problems in areas like Vancouver, Seattle and Wilmington NCescalated to the point that immediate assistance to the local building community became

more important than long term efforts to develop a model (Hazleden and Morris 1999).

Hazleden and Morris (1999) addressed the balance between load in terms of rain falling

on a wall and capacity in terms of deflection, drainage, drying and durable materials on a

conceptual basis. Change in capacity at each stage of the building life cycle wasillustrated with a novel graphical method and the potential for development of a

mathematical model was discussed. Nevertheless, efforts turned to ensuring thatdesigners, specifiers and builders had ready access to basic information on design for

durability through a web site jointly run with the Canadian Wood Council www.durable-

wood.com. These more unassuming efforts continue today.

The number of factors, the lack of data on those factors and the uncertainty around expert

estimates makes service life prediction for buildings using a factor method highlyspeculative. It may be more productive to start with a simpler system such as

preservative treated stakes in ground contact.

Modelling Data from Standard Tests:

The perils of experiments not designed for the purpose

Standardisation of new wood preservatives through the American Wood Preservers

Association requires a minimum of three years stake test data at an aggressive test site.However, this is not difficult to achieve with any number of biocides at a high loading.

What is required by the producer and end user is a treatment that they can rely on to

prevent decay for 15, 40 or 60 years depending on the commodity. Ideally we need topredict long-term performance of high preservative loadings from short-term

performance of lower loadings. The standard method for interpreting field test data wasAWPA E3-83 but this did not work well for waterborne preservatives and was so difficult

to follow that it was never used and was removed from the book of standards in 2003. Analternative approach was developed (Morris and Cook 1994, Cook and Morris 1995,

Morris and Rae 1995, Morris 1998), based on the approach of Gray and Dickinson

(1983). A single equation was derived to fit the deterioration curves for a series ofretentions of a preservative, where condition of the wood is expressed as log stake

score on the 100 (sound) to zero (broken) scale.

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(i) Condition of the wood = 100 - eA

(retention)B

(time)C

The fit to the mean rating data was extremely good but then again we had 15 to 20 years

data to work with. We suggested the term eA

varied according to the test site, and the

durability of the untreated stake. It is possible to constrain this term to a specific valuebased on a broad range of data from a specific site and method (Morris and Rae 1995).

The value of B seemed to be specific to each preservative and described the relationship

between increasing the retention and the reduction in the decay rate. This was typicallyan example of diminishing returns (Morris and Rae 1995). The value of C was also

specific to the preservative and described the degree to which the rate of decay increases

with time and depletion of preservative (Morris and Rae 1995). Preservatives like CCAthat do not leach substantially had first order term (linear). Preservatives that do leach

had a second order term (curved). Preservatives that leach and biodegrade had a third

order term (even more curved). Preservatives like creosote that bleed, volatilise, leach

and biodegrade had a fifth order term, probably because biodegradation proceeds faster

with decreasing retention. The validity of this equation was supported by its similarity tothe antilog of the log probability model developed by Hartford and Colley (1984) using

an entirely different approach, where X is the AWPA logscore converted to a probabilityvariable.

(ii) eX

= eA

(retention)B

(time)C

Equation (i) can be reworked to predict time to reach a specific condition for a givenpreservative retention.

(iii) Ln (time) = ln (100-condition) B(ln retention) - A

C

It could also be reworked to determine what retention of a new preservative would give

equivalent time to a reach specified rating to that of a standard retention of a referencepreservative. Initially equation (i) was applied to the mean logscore but it was

recognized that this did not accurately represent the mode of deterioration of individual

stakes. The mean was inevitably a sigmoid curve since it was bound to tend away from100 at the start and tend to zero at the end. Individual stakes tended to follow a simple

curve to failure. Attempts were made to use the mode and the median but the goodness

of fit of the equation to the data dropped dramatically (Morris 1998). This is because theAWPA rating scheme was a step function: 10, 9, 7, 4, 0 at the time we were generating

the test data we used for this work. This rating scheme has now been modified to 10, 9, 8,

7, 6, 4, 0, which may improve the fit but it is still a step function. The equation may bemore appropriately applied to percent residual strength, which is a continuous function.

For structural applications, the 5th

percentile would be more appropriate than the mean,

median or mode since that would predict a minimum life for the majority of the

population. When all is said and done, the basic problem was that this test method wasnever designed to generate data for mathematical modeling. It is likely that service life

prediction will require experiments specifically designed for the purpose.

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This method is still not going to predict 15 to 60 years performance from three yearsstake test data. It may, however, be possible to estimate the value of B for new

preservatives through accelerated laboratory testing to determine the relationship between

retention and rate of decay and the value of C by quantifying the rate of each mode of

depletion. Using a constrained value for A and values of B and C from laboratory testsmay allow the model to be fitted to field test data at an earlier stage. Service life

prediction will thus require employment of a suite of laboratory and field tests

specifically designed for the purpose.

Using Tropical Test Sites

Ensuring Acceleration Without Change

Major obstacles to the prediction of service life particularly for above ground applications

are the lack of realism of laboratory tests and the time required for field tests. To speed

up the generation of field test data, many researchers have turned to using tropical orsubtropical test sites where temperatures are consistently higher, rainfall is more reliableand decay rates are much faster. We have certainly done this with weathering tests of

coatings (Groves, McFarling and Morris 2004). However this approach involves

potential pitfalls that may not be fully appreciated. Constantly moist conditions aboveground as found in windward Hawaii may favour colonization by white rot fungi and

jelly fungi, whereas fluctuating moisture conditions more common in North America

favour colonization by Gleophyllum species which are brown rot fungi. These groups offungi may have very different preservative tolerances such that results obtained over the

short term in tropical climates may not be predictive of long-term performance in

temperate climates. This could become particularly important as we move towards using

organic biocides that are degraded by the lignin and extractive breakdown mechanisms ofthe white rot fungi. It is imperative to ensure that accelerated tests simply accelerate and

do not fundamentally change the mechanism of failure. To that end we are attempting to

develop an accelerated above ground laboratory test using spore inoculum (Morris 2004).

Simulating Conditions in Leaky Build ings

The Need for Appropriate Inoculum

Around the time Varoglu and Morris (1996) abandoned their efforts to predict building

performance, the building science community began to develop hygrothermal models to

predict moisture conditions in buildings (Karagiozis and Salonvaara 1995). Initiallythese focused on cold climate condensation problems but they were later modified toallow for liquid water intrusion (Kumaran et al. 2002). Once these were close to

completion, they began to look at adding damage functions to predict the impact of these

moisture contents on damage to components (Nofal and Kumaran 1999). The criticaldata for these models are the minimum relative humidity and equilibrium moisture

content for decay to start and the time to initiation of decay. It was well known that

decay would not start below 20% moisture content but would start and proceed rapidly

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above 30% moisture content. Between these boundaries was a gray area with many

authors disagreeing about the minimum moisture content for decay. Initially the data usedin these calculations came from experiments on Norway spruce and Scots pine using

European fungus strains (Viitanen 1997) and on aspen OSB under pure culture conditions

(Schmidt 1988). A threshold of 80% RH for unacceptable moisture conditions was

proposed by building scientists based on their review of the biological literature. Modelsrun using 80% RH as the threshold showed wood frame buildings designed and built

perfectly using conventional wood products would be exposed to unacceptable

moisture conditions everywhere in North America except in desert regions. Forintekproposed 95% RH as the threshold based on the work of Viitanen (1997) and offered to

develop data more relevant to North America (Morris and Winandy 2002). Models run

using 95% RH as the threshold showed wood frame buildings designed and builtperfectly using conventional wood products would not be exposed to unacceptable

moisture conditions. Buildings with design and construction faults would be OK in most

locations but would be exposed to unacceptable moisture conditions in areas such as

Seattle, Vancouver and Wilmington NC. Since that seemed to reflect real life we were

comfortable with 95% RH as an interim solution while we developed more relevant data.

One of the key concerns in developing relevant data was considered to be the use ofappropriate inocula. Untreated wood or treatments for use above ground that may be

resistant to basidiospores may not be resistant to mycelia or mycelial cords. Inside

buildings constructed with kiln-dried lumber and engineered wood products that aresterile from heat of manufacturing, basidiomycetes can arrive on the wood only in the

form of basidiospores. These are notoriously difficult to produce reliably under

laboratory conditions (Croan and Highley 1991) and do not reliably germinate, even onfresh sapwood (Gray 1990). Viitanen (1997) appropriately used spores and minute pieces

of fungus mycelium as close to spores as possible in terms of inoculum potential.Schmidt (1988) used infected wood and soil because he was not expecting his data to be

used for damage functions. Curling, Clausen and Winandy (2002) used massive mycelial

inoculation through vermiculite because the intent was to monitor the progress of decay,not determine time to initiation (Morris and Winandy 2002). Suzuki et al. (2005) used no

inoculum and sealed their samples inside airtight chambers, consequently they had some

problems getting initiation of decay. Saito (2005), also working on developing data for

damage functions at the Building Research Establishment in Japan, used small pieces ofinfected wood as inoculum that provide much greater inoculum potential than a

basidiospore. In studies where massive mycelial inoculum was used, particularly when

growing on wood, infection of test specimens typically occurred immediately in allspecimens.

In the work at Forintek (Clark, Symons and Morris 2005) minute amounts of myceliumof known test fungi and the natural airspora from outdoor air have been used to determine

the limiting moisture contents for decay initiation. Aspen OSB, Canadian softwood

Plywood (CSP) and solid Western hemlock heartwood samples were exposed to a range

of humidities, in one case with pre-wetting, and regularly inoculated with Gleophyllumtrabeum orCoriolus versicolor. Outdoor airspora was brought in by a fan operating for

one hour twice a day. At 40% MC and 20 C it took Aspen OSB and hemlock heartwood

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21 weeks to show some samples colonized and substantial loss of strength in a few

samples was not noted until 34 weeks. Similarly, aspen OSB in a pilot study, showedsubstantial strength loss after 34 weeks at conditions close as possible to 100% RH and

20C. After three years at 90% or 95% RH and 20C, aspen OSB, hemlock heartwood and

CSP did not show any basidiomycete colonization or loss of strength (Clark, Symons and

Morris 2005). Even under ideal conditions for decay, the probability of initiation onhemlock heartwood boards indoors was only about 0.10 per year, meaning it would take

ten years for all samples to get infected if this was indeed a linear function. This was not

inconsistent with the 0.18 per year noted by Morris and Winandy (2002) for hemlock L-joints outdoors. This clearly illustrates the importance of using appropriate inocula in

tests designed to simulate real life as opposed to typical massive mycelial inoculum used

in standard laboratory tests of wood treatments designed to generate data as rapidly andconsistently as possible.

Real Service Life Data

Reality Check

Wood is commonly perceived as an inherently non-durable building material and wood-

frame buildings as inherently of shorter life than brick, concrete and steel. A survey of

architects generated the following average estimates of building service life: 90 years forconcrete, 72 years for steel frame and 55 years for wood frame (OConnor and

Dangerfield 2004, OConnoret al. 2005). Forintek set out to determine whether there is a

correlation between the structural material and the reason for demolition. The AthenaInstitute was contracted by Forintek to survey the owners of buildings demolished in the

city of St Paul Minnesota in the years 2000 to part of 2003 (OConnoret al. 2005). Data

were obtained for a total of 227 buildings, 105 non-residential and 122 residential. Of the

non-residential buildings, 54 were concrete, 30 were wood, 10 were steel and 11 were acombination of materials. Of the residential buildings, 118 were wood-framed, 3 were

concrete and none were steel-framed. The primary focus was the spread of age at

demolition for each structural material. Considering the entire data set, 18% of woodbuildings were over 100 years old, 49% were 76-100 years old, and 18% were 51-75

years old. Only 14% were less than 50 years old when demolished. In contrast 63% of

concrete buildings were less than 50 years old when demolished, 18% were 51-75 yearsold, 12% were 76 100 years old and only 5% were over 100 years old. In even more

stark contrast, 80% of steel buildings were under 50 years old when demolished, however

this was from a data set of only 10 buildings.

It was considered that these data might have been skewed by the large number of woodframe residential buildings, thus the data were examined in the same way for just non-

residential buildings. The results were much the same with the overwhelming majority ofconcrete and steel framed buildings demolished after less than 50 years and the

overwhelming majority of wood-frame buildings demolished after more than 50 years.

The top three reasons for demolition were given as land redevelopment, building not

suitable for anticipated use and buildings physical condition. Of buildings in the last

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category, deterioration was mostly related to lack of maintenance. There were eight

incidents of structural degradation. Six of these were in wood frame buildings, perhapsnot surprisingly because these were of a higher average age. All six had failures of the

concrete foundation. Two of these also had wood rot but both were over 76 years old.

Of the buildings in the category of not suitable for anticipated use, wood had by far the

lowest representation, likely because wood-frame buildings are much easier to adapt tonew uses. Clearly, resistance to deterioration of the structural material is not the primary

determinant of building service life and architects need to change their perception of

wood frame construction. This has important implications for the prediction of servicelife based on the factor method where these factors are estimated by experts.

Conclusions

Service life prediction was easier when one preservative was used for 150 years.

The number of factors, the lack of data on those factors and the uncertaintyaround expert estimates, makes service life prediction using a factor method

highly speculative. Experiments need to be designed specifically for service life prediction.

Data from lab tests may be useful for refining parameters in modeling field data.

Accelerated tests are useful provided they do not change the deterioration hazard.

The use of realistic inocula is critical for valid data.

Methods of service life prediction need to be checked with real life data.

References

Canadian Standards Association. 1995. S478-1995. Guideline on durability in buildings.Canadian Standards Association, Etobicoke, Ontario. 93p

Clark, J.E., P. Symons and P.I. Morris. 2005. Time to initiation of decay on sheathing

under constant conditions. Report to the Canadian Forest Service. ForintekCanada Corp. Vancouver BC. 40p

Cook, J.A., Morris, P.I. 1995. Modeling data from stake tests of waterborne woodpreservatives. Reprint: 5p. Forest Products Journal 45(11/12): 61-65.

Croan, S.C and Highley, T.L. 1991. Conditions for carpogenesis and basidiosporogenesis

by brown rot fungi basidiomycete Gleophyllum trabeum. Mater. U Organ. 27:1-9.

Curling, S.F., CA Clausen and J.E. Winandy 2002. Relationship between mechanicalproperties, weight loss and chemical composition of wood during incipient

brown-rot decay. For. Prod. J. 52(7/8): 34-39.

7/27/2019 4harddat

10/11

Gray, S.M. 1990. Antagonism to spore germination in Scots pine. International Research

Group on Wood Preservation Document No. IRG/WP/1458. IRG Stockholm,Sweden. 8p.

Gray, S.M. and D.J. Dickinson 1983. CCA modifications and their effects on soft-rot in

hardwoods. Part 2. International Research Group on Wood PreservationDocument No. IRG/WP/3244. IRG Stockholm, Sweden.20p

Groves, K., McFarling, S., Morris, P.I. 2004. Field performance of transparent coatings.Proc. a. conv. Canadian Wood Preservation Assoc. 25: 157-173

Hazleden, D.G., Morris, P.I. 1999. Designing for durable wood construction: the 4 Ds.Durability of Building Materials and Components 8 Eds. M.Lacasse and D.J.

Vanier. 1:734-745.

Karagiozis, A.N. and M.Salonvaara, (1995), "Influence of material properties on the

hygrothermal performance of a high-rise residential wall",ASHRAE Transactions,1: 647-655.

Kumaran, K. P. Mukopadhyaya, S.M. Cornick, M.A. Lacasse, W. Maref, M. Rousseau,

M. Nofal, J.d.Quirt, and A. Dalgliesh. 2002. A Methodology to Develop Moisture

Management Strategies for Wood-Frame Walls in North America: Application toStucco-Clad Walls. Proceedings 6

thNordic Symposium on Building Physics.

Morris, P.I. 1994. A proposal for an international wood preservation standard. International Research Group on Wood Preservation, Document No. IRG/WP/94-

20031. IRG Stockholm, Sweden. 9p.

Morris, P.I. 1998. Beyond the log probability model. American Wood Preservers'

Association Annual Meeting. Proceedings (94): 267-283.

Morris, P. 2004. The role of field testing in standardisation. Paper: 13p. American Wood

Preservers' Association Annual Meeting Proceedings (100): in press

Morris, P.I., Calver, B. 1985. Wood pole decay - Mythology and magic. Part I

Mythology. Distribution Developments. December 1985 6-12p.

Morris, P.I., Cook, J.A. 1994. Predicting preservative performance by field testing. In

Wood Preservation in the 90s and Beyond, Savannah, Georgia, September 1994.

Forest Products Society. 116-121

Morris, P.I., Rae, S. 1995. What information can we glean from field testing?

International Research Group on Wood Preservation Document No. IRG/WP/95-

20057. IRG Stockholm, Sweden. 16p.

7/27/2019 4harddat

11/11

Morris, P.I., Winandy, J.E. 2002. Limiting conditions for decay in wood systems. Paper:

International Research Group on Wood Preservation, Document No. IRG/WP-10421. IRG Stockholm, Sweden. 11p.

Nofal, M and K. Kumaran. 1999. Durability assessment of wood-frame construction

using the concept of damage functions. Durability of Building Materials andComponents 8 Eds. M.Lacasse and D. J. Vanier. 1: 766-779

O'Connor, J. and J. Dangerfield. 2004. The environmental benefits of woodconstruction. Proceedings, 8th World Conference on Timber Engineering. 1: 171-

176.

O'Connor, J., S. Horst and C. Argeles. 2005. Survey on Actual Service Lives for North

American Buildings. Proceedings, 10th International Conference on Durability of

Building Materials and Components

Saito, H. 2005. Personal communication.

Schmidt. E. L. 1988. Decay and strength reductions in aspen waferboard. in Proceedingsof the IUFRO Wood Protection Subgroup. IUFRO Div 5. conference on Wood

Protection. V. Mathur, A. Preston, and D.J. Dickinson Eds. 24-36

Suzuki, H. Y. Kitadani, K. Suzuki, A. Iwamae and H. Nagai. 2005. Development on

damage functions of wood decay for building envelope design. International

Research Group on Wood Preservation, Document No. IRG/WP/05-10556. IRGStockholm, Sweden. 12p.

Varoglu, E., Morris, P.I. 1996. Towards a risk assessment model for durable wood

construction. Report to the Canadian Forest Service. Forintek Canada Corp. 21p.

Viitanen, H. 1997. Modelling the time factor in the development of brown-rot decay in

pine and spruce sapwood The effect of critical humidity and temperature

conditions. Holzforschung 51(2): 88-106.