Modelling the Material Resistance of Wood—Part 2: Validation and Optimization of the Meyer-Veltrup Model

Abstract

Service life planning with timber requires reliable models for quantifying the effects of exposure-related parameters and the material-inherent resistance of wood against biotic agents. The Meyer-Veltrup model was the first attempt to account for inherent protective properties and the wetting ability of wood to quantify resistance of wood in a quantitative manner. Based on test data on brown, white, and soft rot as well as moisture dynamics, the decay rates of different untreated wood species were predicted relative to the reference species of Norway spruce (Picea abies). The present study aimed to validate and optimize the resistance model for a wider range of wood species including very durable species, thermally and chemically modified wood, and preservative treated wood. The general model structure was shown to also be suitable for highly durable materials, but previously defined maximum thresholds had to be adjusted (i.e., maximum values of factors accounting for wetting ability and inherent protective properties) to 18 instead of 5 compared to Norway spruce. As expected, both the enlarged span in durability and the use of numerous and partly very divergent data sources (i.e., test methods, test locations, and types of data presentation) led to a decrease in the predictive power of the model compared to the original. In addition to the need to enlarge the database quantity and improve its quality, in particular for treated wood, it might be advantageous to use separate models for untreated and treated wood as long as the effect of additional impact variables (e.g., treatment quality) can be accounted for. Nevertheless, the adapted Meyer-Veltrup model will serve as an instrument to quantify material resistance for a wide range of wood-based materials as an input for comprehensive service life prediction software.

1. Introduction

Service life planning and performance prediction of wood are mutually linked. The first comprehensive approaches were in the Australian TimberLife project [1] and in different European research projects such as WoodExter [2], WoodBuild [3], and DuraTB [4]. The latter proposed engineering guidelines accounting for exposure- and resistance-related parameters that can be captured in terms of a dosage, which is a well-defined relationship with a response such as the biological depolymerization of wood. Among the numerous wood-destroying organisms, decay fungi play a vital role globally, since their spores are ubiquitous and can infest wood even under extreme climatic conditions [5,6].

The material-inherent resistance of wood against wood-destroying fungi is the product of chemical ingredients with inhibitory effects on fungal activity (e.g., extractives) and its repellency to moisture. Both can originate either from the natural chemical constitution and anatomy of the wood tissue itself or from man-made improvement through cell wall modification, water-repellant, and/or preservative treatments. The resulting mode of protective action is usually a combination of wood’s inherent resistance and its wetting ability.

The durability of wood and wood-based materials against wood-destroying fungi is usually tested through incubation with monocultures of basidiomycetes under sterile conditions in the laboratory or exposure in field tests with or without ground contact. The moisture content and temperature of wood are kept at a favorable level for most decay fungi under standardized laboratory conditions. Permanent wetting of the wood samples is provided by high aqueous-containing nutritious media such as malt agar (e.g., EN 113-2 [7]) or moist soil substrate in so-called soil-block tests (e.g., AWPA E10-16 [8]). Similarly, wood specimens that are buried to half of their length in soil-contact field tests (e.g., AWPA E7-15 [9] and EN 252 [10]) and in so-called semi-field tests performed with unsterile soil under laboratory conditions (terrestrial microcosms, e.g., ENV 807 [11]; CEN/TS 15083-2 [12], and AWPA E14-16 [13]) are permanently wet. Permanent wetting of test specimens may reduce the mode of protective action by leaching biocidal components. Moreover, this permanently wet condition poorly represents above-ground applications where the wood is exposed to cyclical wetting and drying. Under such conditions, the moisture performance of wood has a greater influence on service life. Wood materials that are low in biocidal components but exhibit high water-repellency, usually perform worse than expected compared with outdoor above-ground conditions where they can dry out and are consequently not constantly wet. Occasionally, this is called “torture testing” [14] since it does not necessarily reflect the anticipated use conditions. Above-ground field tests deliver durability data under quite realistic hazard conditions, but respective test data are sparsely available [15]. Numerous above-ground test methods have been reported, but very few are standardized [16]. Furthermore, they cover a wide range of above-ground use conditions representing different moisture-induced decay risks. In summary, the durability classification of wood rarely considers moisture performance. The European standard EN 350 [17]—at least in its current form—indicates that permeability to water should be considered. Permeability is defined as the “ease with which water penetrates a wood-based matrix (wood of a particular species, wood-based material) and is released by evaporation” [17]. Accordingly, the permeability to water and the rate of release can provide information relevant for wood’s expected service life. However, guidance on the incorporation of this material property in the classification of biological durability of wood is still lacking, but essential for more accurately predicting the service life of timber structures.

Meyer-Veltrup et al. [18] approached predicting the service life of wood above-ground considering the combined effects of wetting ability and durability data. A comprehensive dataset was obtained from laboratory durability tests and still ongoing field tests in three different countries. In addition, four different wetting ability tests were performed with the same material. A dose-response concept was used to predict decay rates for specimens exposed above-ground using various indicating factors. The Meyer-Veltrup model was developed and optimized considering the resistance of wood to brown, white, and soft rot, as well as relevant types of water uptake and release.

Decay rates from above-ground field tests at different test sites in Norway were predicted with the model. In a second step, the model was validated using data from laboratory and field tests performed in Germany and Sweden. The model was next validated using data from laboratory and field tests by different authors [18,19,20]. Material resistance data were determined for additional wood species [21,22,23] and wood treatments [19,24].

The model was found to be fairly reliable and slightly conservative, and it had the advantage of being implemented into existing engineering design guidelines. Several reality checks revealed a sufficient model fit when linked with a climate exposure model and a decay prediction model [25], as reported for timber bridge structures [4]. However, Brischke et al. [26] reported that the general good fit of the Meyer-Veltrup model did not apply to preservative-treated wood or wood-based materials due to a lack of data on their inherent resistance and/or wetting ability. In addition, at the time, the optimized model did not include extremely durable materials. The most durable materials included in the original research were black locust, English oak, teak, and merbau [18]. The maximum value of modifying factors accounting for inherent protective properties (k_inh) and wetting ability of wood (k_wa) were set to 5.0 as this gave the best fit with above-ground field test data. The maximum threshold might need to be higher for preservative-treated and modified wood. On the other hand, further factors may help account for treatment quality, preservative concentration and retention, modification level, superficial treatments, and coatings. However, it is difficult to quantify these factors, though attempts have been made to model such factors [27].

This study aimed to collect data on the inherent protective properties and the wetting ability of wood to further validate and optimize the Meyer-Veltrup model for material resistance. The range of wood-based materials included preservative-treated and modified woods tested under laboratory and above-ground field conditions in different parts of the world. To better utilize existing durability test data generated in different parts of the world but using different reference wood species, factors for different resistance parameters determined in Part 1 of this publication were applied in this study.

2. Materials and Methods

2.1. The Meyer-Veltrup Model

A model approach according to Meyer-Veltrup et al. [18] was applied to predict the above-ground field performance of the examined timbers. The model includes both the climatic exposure and the resistance of the material. Acceptance for a chosen design and material was expressed as:

Exposure ≤ Resistance

(1)

The exposure was expressed as an exposure dose (D_Ed) determined by daily averages of wood temperature (T) and wood moisture content (MC). The material property was expressed as a resistance dose (D_Rd). The dose would be expressed in days (d) with optimum moisture and temperature conditions for fungal decay as (see [28] and Equation (2)).

$$D_{Ed}\le D_{Rd} \left(\mathrm{d}\right)$$

(2)

where:

D_Ed is the exposure dose (d);

D_Rd is the material resistance dose (d).

The exposure dose D_Ed depends on an annual dose at a specific geographical location and several factors describing the effect of driving rain, local climate, sheltering, distance from the ground, and design details. A detailed description of the development of the exposure module of the model is given by [3]. The present study focused on the resistance dose module of the model, which is the resistance, expressed as material resistance dose D_Rd. The latter is the product of a critical dose D_crit and two factors considering the wetting ability of wood (k_wa) and its inherent durability (k_inh). The approach is described by Equation (3) according to [28]:

$$D_{Rd}=D_{crit}\times k_{wa}\times k_{inh }\left(\mathrm{d}\right)$$

(3)

where:

D_Rd is the material resistance dose (d);

D_crit is the critical dose (d) corresponding to decay rating 1 (EN 252 [10]);

k_wa is a factor accounting for the wetting ability of the material (-) relative to a reference wood species;

k_inh is a factor accounting for the inherent protective properties of the material against decay (-) relative to a reference wood species.

The critical dose (D_crit) was evaluated for Scots pine sapwood and Douglas fir heartwood according to [28]. The critical dose corresponding to decay rating ”1” was more or less independent from the wood species. Instead, differences between species and/or treatments were better expressed by their moisture dynamics and decay inhibiting properties. For Scots pine sapwood and Douglas-fir heartwood, the critical dose was around 325 days with favorable conditions for fungal decay [28], as previously determined in horizontal double-layer experiments performed at several locations in Europe for up to eight years [29].

2.2. Data Acquisition

Data on material resistance based upon laboratory and field wood durability tests and different wetting ability tests were gathered from scientific publications, research reports, and technical guidelines. In addition, raw data in terms of mass loss, decay ratings, or moisture-related characteristics were provided by the authors. Information about the materials included in this study is summarized in

Table 1. Untreated hardwoods included for validation and optimization of the material resistance model.

Wood Species	Common Name	Origin	References
Acer platanoides/A. pseudoplatanus	Norway maple/Sycamore	Europe	[18,30,31,32,33,34]
Alnus glutinosa	Black alder	Europe	[18,31,34,35,36]
Betula pendula/B. pubescens	Silver birch/Downy birch	Europe	[18,31,35,36]
Dicorynia guianensis	Basralocus	S. America	[31,34,37,38,39]
Fagus sylvatica	European beech	Europe	[15,18,19,31,32,33,34,35,36,39,40,41,42,43,44,45,46,47,48,49]
Fraxinus excelsior	European ash	Europe	[18,19,31,35,36]
Intsia bijuga	Merbau	Africa	[18,31,50,51]
Lophira alata	Bongossi	Africa	[31,34,37,38,39,40,52,53]
Peltogyne spp.	Amaranth	C. America	[30,34,38]
Populus tremula	Aspen	Europe	[15,18,32,33,35,36]
Quercus robur/Q. petraea	European oak	Europe	[15,18,19,30,31,34,35,36,38,39,40,41,43,44,46,49,52,53,54,55,56,57,58,59,60]
Robinia pseudoacacia	Black locust	Europe	[18,31,34,36,39,49,52,53,57,61]
Salix caprea	Goat willow	Europe	[18]
Sorbus aucuparia	Rowan	Europe	[18,21]
Tectona grandis	Teak	Asia	[18,31,34,35,36,38,39,62]
Tilia cordata	Lime	Europe	[18]
Ulmus glabra	Wych elm	Europe	[18,55]

,

Table 2. Untreated softwoods included for validation and optimization of the material resistance model. (sw = sapwood).

Wood Species	Common Name	Origin	References
Abies alba	Silver fir	Europe	[18,57]
Juniperus communis	Juniper	Europe	[18,21]
Larix decidua	European larch	Europe	[18,19,31,35,36,39,42,45,48,49,55,56,57,63]
Larix sibirica	Siberian larch	Europe	[15,18,31,41,42,60,64,65]
Picea sitchensis	Sitka spruce	Europe	[18]
Pinus spp.	Southern pine sw	N. America	[18,33,61,66]
Pinus radiata	Radiata pine sw	New Zealand	[18,31,34]
Pinus sylvestris	Scots pine	Europe	[15,18,19,31,39,41,42,43,45,49,55,57,59,60,63,65,67,68]
	Scots pine sw		[18,19,31,33,34,39,41,42,43,45,48,49,56,57,61,63,68,69]
Pseudotsuga menziesii	Douglas-fir	N. America, Europe, New Zealand	[15,18,31,34,38,39,40,42,43,45,57,59,63,70,71,72,73]
	Douglas-fir sw		[18,42,66,72]
Thuja plicata	Western red-cedar	N. America	[15,18,30,31,39,58,63,71,73]
		Europe	[18,55]

,

Table 3. Modified timbers included for validation and optimization of the material resistance model. (sw = sapwood, HT = heat treatment, OHT = oil-heat treatment, AC = acetylation, FA = furfurylation, DMDHEU = treatment with 1.3-dimethylol-4.5-dihydroxyethyleneurea).

Wood Species	Common Name	Origin	Modification	References
Fagus sylvatica	European beech	Europe	HT	[19,48,74]
Picea abies	Norway spruce	Europe	HT	[19,32,41,48,59,61,68,75]
Pinus sylvestris	Scots pine	Europe	HT	[18,33,34,41,56,59,60,61,68,75,76]
Fraxinus excelsior	European ash	Europe	OHT	[18]
Picea abies	Norway spruce	Europe	OHT	[18,57]
Pinus spp.	Southern pine sw	USA	AC	[18]
Pinus sylvestris/P. radiata	Scots pine sw/Radiata pine sw	Europe/New Zealand	AC	[18,34,41,60,61,76,77]
Acer platanoides	Norway maple	Europe	FA	[18,32,78]
Pinus spp.	Southern pine sw	USA	FA	[18,32]
Pinus sylvestris	Scots pine sw	Europe	FA	[18,60,79]
Pinus sylvestris	Scots pine sw	Europe	DMDHEU	[18,24,34,76,80]

and

Table 4. Preservative-treated timbers considered for validation and optimization of the material resistance model. (sw = sapwood; CCA = chromated copper arsenate; Cu = copper; EA = ethanolamine; OA = octanoic acid; Quat = quaternary ammonium compounds).

Wood Species	Preservative/Treatment	References
Pinus sylvestris sw	CCA 2 kg/m3	[18,61,65]
	CCA 4 kg/m3	[18,32,33,41,76,79]
	CCA 9 kg/m3	[32,33,41,61,68,79]
Picea abies	Cu (II) sulphate low: 0.35% aqueous solution	[81]
	Cu (II) sulphate high: 1.4% aqueous solution
	CuEA low: 0.098% Cu; 0.51% EA
	CuEA high: 0.39% Cu; 2.05% EA
	CuEAOA low: 0.098% Cu; 0.51% EA; 0.07% OA
	CuEAOA high: 0.39% Cu; 2.05% EA; 0.29% OA
	CuEAOAQuat low: 0.098% Cu; 0.51% EA; 0.07% OA; 0.098% Quat
	CuEAOAQuat high: 0.39% Cu; 2.05% EA; 0.29% OA; 0.39% Quat
	BoronEAOAQuat low: 0.098% B; 0.51% EA; 0.07% OA; 0.098% Quat
	BoronEAOAQuat high: 0.39% B; 2.05% EA; 0.29 OA; 0.39% Quat
Picea abies, Pinus sylvestris sw, Larix decidua	Cu (II) carbonate: 0.25%, dipping 8-h 1 and 24-h	[82]
	Cu (II) carbonate: 0.25%, vacuum 1
	Cu (II) carbonate: 0.25%, vacuum + pressure
	Cu (II) carbonate: 0.5%, dipping 8-h1 and 24-h
	Cu (II) carbonate: 0.5%, vacuum
	Cu (II) carbonate: 0.5%, vacuum + pressure
Pinus sylvestris sw	Metal-free organic	[18,32]

¹ treatments were applied only to Scots pine sw and Norway spruce.

as well as the respective data sources used to calculate the modifying factors k_wa and k_inh, and decay rates in in- and above-ground tests are indicated in

Table 5. Factors accounting for the protective inherent properties (k_inh) and moisture performance (wetting ability, k_wa), and relative decay rate in above-ground field tests of untreated hardwood species. (br = brown rot; wr = white rot; LWU = liquid water uptake; VU = vapor uptake; WR = water release; CWU = capillary water uptake; v_rel. = relative decay rate).

Wood Species	kinh			kwa				vrel.
	br	wr	Soil	LWU	VU	WR	CWU
Acer platanoides/A. pseudoplatanus	2.54	0.94	1.02	0.80	1.14	1.07	1.02	0.90
Alnus glutinosa	1.09	1.04	0.72	0.87	1.14	1.16	1.06	1.35
Betula pendula/B. pubescens	1.01	0.93	0.88	0.76	1.16	1.33	0.36	0.95
Dicorynia guianensis	15.30	16.54	5.11	1.43	1.35	1.25	1.06	0.19
Fagus sylvatica	1.26	0.70	0.61	0.73	1.15	1.30	1.44	1.17
Fraxinus excelsior	6.62	0.75	1.30	1.01	1.17	1.07	0.74	0.39
Intsia bijuga	16.19	9.93	16.33	1.72	1.63	0.55	4.62	0.25
Lophira alata	13.26	14.64	10.52	1.54	1.58	1.28	1.24	0.19
Peltogyne spp.	16.48	18.00	5.11	2.41	1.31	2.17	1.39	0.25
Populus tremula	1.24	0.99	0.94	0.91	1.13	1.09	0.65	1.04
Quercus robur/Q. petraea	14.62	8.04	2.77	1.70	1.34	1.70	0.88	0.47
Robinia pseudoacacia	12.20	12.34	2.67	2.67	2.15	1.24	1.65	0.24
Salix caprea	1.37	1.16	1.46	1.16	1.36	0.85	0.60	0.50
Sorbus aucuparia	1.27	1.25	1.46	0.94	1.00	1.07	0.44	0.56
Tectona grandis	18.00	16.96	7.83	2.08	2.43	1.04	1.17	0.16
Tilia cordata	0.89	1.04	1.39	0.92	1.43	0.83	0.38	0.86
Ulmus glabra	7.33	1.12	1.66	1.14	1.31	0.91	0.49	0.39

,

Table 6. Factors accounting for the protective inherent properties (k_inh) and moisture performance (wetting ability, k_wa), and relative decay rate in above-ground field tests of untreated softwood species. (br = brown rot; wr = white rot; LWU = liquid water uptake; VU = vapor uptake; WR = water release; CWU = capillary water uptake; v_rel. = relative decay rate; sw = sapwood).

Wood Species	kinh			kwa				vrel.
	br	wr	soil	LWU	VU	WR	CWU
Abies alba	1.33	1.22	1.24	0.91	1.09	0.96	0.68	1.14
Juniperus communis	13.05	13.11	7.53	1.30	1.43	0.77	1.20	0.32
Larix decidua	4.13	6.15	2.30	1.81	1.39	0.98	1.87	0.34
Larix sibirica	3.32	1.55	4.86	1.01	1.30	1.05	0.46	0.45
Picea sitchensis	1.09	1.82	1.14	1.17	1.03	1.03	3.92	0.86
Pinus spp. sw (Southern pine)	2.38	10.88	0.87	0.89	1.01	0.69	0.58	0.76
Pinus radiata sw	1.95	0.89	1.16	0.63	0.92	1.29	0.85	0.98
Pinus sylvestris	2.98	6.85	1.86	1.06	1.21	0.89	1.37	0.47
P. sylvestris sw	0.97	0.91	1.14	1.00	1.08	0.88	1.03	0.83
Pseudotsuga menziesii	3.46	9.30	3.34	1.61	1.25	0.93	2.84	0.55
P. menziesii sw	1.63	4.66	1.43	1.14	1.11	0.92	1.01	0.83
Thuja plicata (N.-America)	16.73	11.67	2.63	1.13	1.61	0.59	0.27	0.42
T. plicata (Europe)	18.00	11.10	1.31	0.78	1.29	0.81	0.56	0.35

,

Table 7. Factors accounting for the protective inherent properties (k_inh) and moisture performance (wetting ability, k_wa), and relative decay rate in above-ground field tests of modified timbers. (br = brown rot; wr = white rot; LWU = liquid water uptake; VU = vapor uptake; WR = water release; CWU = capillary water uptake; v_rel. = relative decay rate; TM = thermal modification; OHT = oil-heat treatment; AC = acetylation; FA = furfurylation; DMDHEU = treatment with 1.3-dimethylol-4.5-dihydroxyethyleneurea).

Wood Species and Treatment	kinh			kwa				vrel.
	br	wr	Soil	LWU	VU	WR	CWU
F. sylvatica—TM	9.60	7.60	4.68	1.79	2.86	0.43	3.24	0.02
P. abies—TM	8.67	4.95	2.98	6.99	2.09	6.44	1.40	0.34
P. sylvestris—TM	8.74	9.72	5.36	1.87	1.80	1.70	1.47	0.53
F. excelsior—OHT	18.00	9.71	11.79	1.99	2.91	0.60	1.60	0.07
P. abies—OHT	18.00	18.00	9.66	1.73	2.35	0.51	0.88	0.16
Pinus spp. sw (Southern pine)—AC	18.00	18.00	17.78	1.32	2.86	0.31	0.76	0.04
P. sylvestris P. radiata sw—AC	18.00	18.00	16.69	1.57	3.01	1.43	1.28	0.07
A. platanoides—FA	14.72	10.09	3.86	1.89	2.92	0.23	1.08	0.05
Pinus spp. sw (Southern pine)—FA	10.88	12.67	6.54	1.73	2.23	0.40	1.45	0.12
P. sylvestris sw—FA	18.00	18.00	7.53	2.79	3.30	0.23	1.54	0.27

and

Table 8. Factors accounting for the protective inherent properties (k_inh) and moisture performance (wetting ability, k_wa), and relative decay rate in above-ground field tests of preservative-treated timbers. (br = brown rot; wr = white rot; LWU = liquid water uptake; VU = vapor uptake; WR = water release; CWU = capillary water uptake; v_rel. = relative decay rate; CCA = chromated copper arsenate; Cu = copper; EA = ethanolamine; OA = octanoic acid; Quat = quaternary ammonium compounds; P.s. = Pinus sylvestris; P.a. = Picea abies; L.d. = Larix decidua).

Wood Species and Treatment	kinh			kwa				vrel.
	br	wr	Soil	LWU	VU	WR	CWU
P. sylvestris, CCA, 2 kg/m3	18.00	18.00	5.12	0.92	0.97	0.87	2.49	0.10
P. sylvestris, CCA, 4 kg/m3	18.00	18.00	7.79	1.34	0.92	1.22	1.35	0.13
P. sylvestris, CCA, 9 kg/m3	9.66	18.00	11.87	0.83	1.02	0.88	1.02	0.06
P. abies, Cu (II) sulph. low	6.75	10.37	1.82	0.90	0.80	0.97	1.04	0.69
P. abies, Cu (II) sulph. high	8.77	10.54	2.66	0.89	0.77	1.31	0.81	0.63
P. abies, CuEA low	7.13	8.94	2.37	0.88	0.90	1.28	0.94	0.61
P. abies, CuEA high	8.00	7.15	2.00	0.97	0.92	0.90	1.10	0.65
P. abies, CuEAOA low	6.48	8.80	1.72	1.04	0.98	1.16	0.91	0.11
P. abies, CuEAOA high	7.34	6.12	1.98	0.98	0.93	1.06	1.50	0.57
P. abies, CuEAOAQuat low	13.87	9.95	1.45	0.87	1.03	0.94	0.83	0.21
P. abies, CuEAOAQuat high	16.42	7.78	1.84	0.90	1.10	0.75	1.16	0.01
P. abies, BorEAOAQuat low	12.53	9.76	0.85	0.99	0.89	0.95	1.41	0.86
P. abies, BorEAOAQuat high	13.00	8.30	0.88	1.08	0.75	1.08	4.28	0.61
P. abies, Cu 0.25%, dip. 8-h	11.43	16.02	1.47	1.19	0.83	0.51	0.79	0.58
P. abies, Cu 0.25%, dip. 24-h	13.71	18.00	1.71	1.17	0.74	0.78	0.71	0.46
P. abies, Cu 0.25%, vac.	18.00	18.00	3.57	1.14	0.73	0.77	0.81	0.17
P. abies, Cu 0.25%, vac. + press.	16.01	15.30	4.50	1.20	0.83	0.45	0.75	0.03
P. abies, Cu 0.5%, dip. 8-h	13.76	18.00	1.54	1.21	0.81	0.36	1.01	0.39
P. abies, Cu 0.5%, dip. 24-h	14.48	18.00	2.94	1.16	0.83	0.33	1.02	0.42
P. abies, Cu 0.5%, vac.	15.35	15.25	3.18	1.25	0.80	0.32	0.97	0.13
P. abies, Cu 0.5%, vac. + press.	15.19	15.07	3.60	1.29	0.72	0.45	0.88	0.15
P. sylvestris, Cu 0.25%, dip. 8-h	13.27	10.20	1.39	1.31	0.90	0.36	4.96	0.16
P. sylvestris, Cu 0.25%, dip. 24-h	13.69	11.08	2.38	1.34	0.88	0.30	1.86	0.09
P. sylvestris, Cu 0.25%, vac.	18.00	18.00	2.01	1.28	0.80	0.53	2.64	0.09
P. sylvestris, Cu 0.25%, vac. + press.	18.00	17.64	3.03	1.14	0.76	0.33	1.81	0.00
P. sylvestris, Cu 0.5%, dip. 8-h	13.83	14.45	2.55	1.20	0.77	0.26	2.63	0.13
P. sylvestris, Cu 0.5%, dip. 24-h	16.94	15.84	2.75	1.21	0.76	0.32	2.23	0.09
P. sylvestris, Cu 0.5%, vac.	17.23	18.00	3.59	1.22	0.67	0.53	1.60	0.03
P. sylvestris, Cu 0.5%, vac. + press.	15.49	17.34	3.28	1.22	0.65	0.37	2.73	0.00
L. decidua, Cu 0.25%, dip. 24-h	11.94	11.58	1.03	2.10	0.85	0.54	15.49	0.00
L. decidua, Cu 0.25%, vac. + press.	17.99	18.00	1.10	1.85	0.84	0.28	5.63	0.17
L. decidua, Cu 0.5%, dip. 24-h	13.84	14.53	1.14	2.02	0.94	0.18	4.30	0.09
L. decidua, Cu 0.5%, vac.	17.60	18.00	0.87	2.03	0.93	0.28	17.00	0.06
L. decidua, Cu 0.5%, vac. + press.	14.78	14.00	1.32	1.73	0.76	0.19	4.42	0.20
P. sylvestris, metal-free organic	18.00	18.00	2.41	0.85	1.05	0.78	0.48	0.09

. Results from the in-ground field tests were used to supplement laboratory tests when accounting for soft rot resistance to calculate the respective factor k_inh.

2.3. Test Methods for Determining the Modifying Factors k_inh and k_wa

Meyer-Veltrup et al. [18] determined the modifying factors k_inh and k_wa on the basis of different laboratory durability test methods against brown, white, and soft rot-causing fungi and different moisture performance tests accounting for liquid water uptake during submersion, water vapor uptake at high relative humidity (RH), desorption tests at low RH (approx. 0%), and capillary water uptake (CWU) of end-grain surfaces. The test protocols are described in detail in Part 1 of this publication [83]. In each case, the reference wood species was Norway spruce (Picea abies). This study enlarged the pool of datasets and also included results where Scots pine sapwood (Pinus sylvestris) and European beech (Fagus sylvatica) were used as reference species. Factors accounting for the relationship between the material resistance and its respective components for the three reference species were applied as described in Part 1 [83] of this publication. In addition to standard basidiomycete tests with brown and white rot fungi (e.g., EN 113-2 [7]) and soil contact soft rot tests under laboratory (e.g., ENV 807 [11]) and field conditions (e.g., EN 252 [10]), results from basidiomycete mini-block tests [84] were considered. Results from submersion and floating tests according to CEN/TS 16818 [85] were considered for calculating k_wa factors in addition to the tests described in Part 1 [83].

Furthermore, results of above-ground tests performed at different locations worldwide were obtained in horizontal lap-joint tests [45,69,86], sandwich tests [16], decking tests [19], deck tests [63,81], close-to-ground mini-stake tests [79], multiple layer tests [15], block tests [79,80,81], vertically hanging stakes [57], painted and unpainted L-joint tests [15,87], horizontal double layer tests [57], and modified horizontal double layer tests [68].

A decay rating of specimens in and above-ground was performed regularly (usually once per year) with the help of a pick test. The depth and distribution of decay were determined and rated using the five-step scheme according to EN 252 [10] as follows: 0 = sound, 1 = slight attack, 2 = moderate attack, 3 = severe attack, and 4 = failure. Some studies used the American and/or Australian rating system (10 to 0), which were transformed to the EN 252 scale as suggested by Stirling et al. [88].

The normal process requires that all specimens of a specific material must have reached decay rating 4 to determine mean lifetime. This was not the case for all materials. Therefore, the mean decay rate v_mean was calculated according to [18] as follows (Equation (4)):

$$v_{mean}=\frac{{\sum }_i^n{v}_i}{n}=\frac{{\sum }_i^n\frac{R}{t}}{n}$$

(4)

where:

v_mean is the mean decay rate of specimens (a⁻¹);

v_i is the decay rate of single specimens (a⁻¹);

R is the decay rating according to EN 252 [10];

t is the exposure time (a);

n is the number of replicate specimens (-).

For further modeling, a relative decay rate was calculated with Norway spruce as a reference species (Equation (5)) to become independent of the respective test location and test method. When other wood species than Norway spruce were used as reference, species- or genus-specific factors were established for correcting the values according to the relationships between reference species and Norway spruce.

$$v_{rel.}=\frac{v_{species x}}{v_{reference}}$$

(5)

where:

v_rel. is the relative decay rate (-);

v_{species x} is the decay rate of species x (a⁻¹);

v_reference is the decay rate of a reference, here: Norway spruce, (a⁻¹).

2.4. Evaluation Procedure and Model Fitting

Negative mass losses (ML), i.e., mass gains, in laboratory decay tests were considered to be equal to zero for further calculations. To avoid unrealistically high relative values (factors), a threshold (Thr) was set to 18.0 for both factors, leaving the values in the following range: 0 < k_wa ≤ Thr and 0 < k_inh ≤ Thr according to the best fit of the model based on the method of least squares.

The above-ground performance of wood was the target measure in this study. Performance was quantified by calculating the resistance dose (D_Rd) according to Equation (3) above, averaged for all available datasets per material (Table 5, Table 6, Table 7 and Table 8), and used for optimizing the resistance model using a power regression function and the method of least squares.

To identify the most suitable indicators, different factors and factor combinations (based on results from the wetting ability and durability tests) were used to correlate the relative resistance dose D_Rd,rel. (Equation (6)) with the relative mean decay rate (v_rel.). Therefore, both measures were set relative to the reference species Norway spruce. To calculate k_inh from laboratory basidiomycete tests (Equation (7)), ML was factorized and either used as mean of ML caused by brown and white rot (br:wr,mean) or as worst case, i.e., the maximum relative ML representing white, brown, and soft rot, and exposure to soil, and thus the minimum k_inh (min). Factors obtained in laboratory soil bed tests and in-ground field tests were averaged or k_inh was calculated from soft rot tests with soil contact (k_inh,soil).

$$D_{Rd, rel.}=\frac{D_{Rd, species x}}{D_{Rd, reference}}$$

(6)

where:

D_Rd,rel. is the relative resistance dose (-);

D_{Rd,species x} is the resistance dose of species x (a⁻¹);

D_Rd is the resistance dose of the reference, here: Norway spruce, (a⁻¹).

$$k_{inh}=\frac{\frac{{\sum }_{i=1}^n{k}_{inh, soil, i}}{n} +\frac{ {\sum }_{j=1}^n{k}_{inh, non-soil, j}}{n}}{2}$$

(7)

where:

k_inh is the factor accounting for the inherent protective properties of the material against decay (-);

k_{inh,soil, i} is the factor accounting for the inherent protective properties of the material against decay in tests with soil contact (-);

k_{inh,non-soil, j} is the factor accounting for the inherent protective properties of the material against decay in tests without soil contact (-);

n is the number of tests.

3. Results and Discussion

3.1. Untreated Timber

The results from the different moisture performance and durability tests are given in Table 5, Table 6, Table 7 and Table 8 and are expressed as factors k_wa and k_inh. The results differed markedly between wood species, treatments, and test methods. The factor accounting for protective inherent properties k_inh varied among the hardwoods between 0.61 (beech, soil) and 18.0 (Amaranth, white rot, and teak, brown rot, Table 5), i.e., the Thr for both factors, k_inh and k_wa (see Section 2.4. Evaluation Procedure and Model fitting). The factors accounting for moisture performance (wetting ability, k_wa) showed less variation, i.e., between 0.38 (lime, CWU) and 2.43 (teak, liquid water uptake (LWU)). Similarly, k_inh varied more than k_wa among the softwood species (Table 6).

The relative decay rate (v_rel.) was between 0.16 (teak) and 1.35 (alder) among the hardwoods and between 0.32 (juniper) and 1.14 (silver fir) among the softwoods. The wider range in biological durability of hardwood species compared to softwoods is consistent with previous reports [15,89].

3.2. Modified Timber

In general, both factors, k_inh and k_wa, of differently modified timber were higher compared to respective untreated wood species and showed lower v_rel. in above-ground durability tests (Table 7). The factor k_wa varied between 0.43 and 3.24 for heat-treated beech, which also showed the lowest v_rel. In contrast, thermally modified Norway spruce and Scots pine also showed rather high k_inh (2.98–9.39), but suffered from comparatively high v_rel., i.e., 0.34 and 0.53, respectively. The latter might at least partly be explained through the increased brittleness of thermally modified wood [89], which can be mistaken for fungal decay, since the effect of high temperatures and brown rot decay on wood’s structural integrity is very similar [90,91]. Thus, the severity of fungal decay in thermally modified wood can easily be overestimated.

3.3. Preservative-Treated Timber

The preservative-treated timbers generally showed high k_inh values, often close to or at Thr = 18.0. In contrast, the wetting ability factors were at least partly negatively affected by the treatments. In particular, many copper-treated woods showed low k_wa values in desorption tests, i.e., 0.18 at minimum, which can be explained by increased sorption of wood after impregnation with aqueous salt solutions [92] and by the presence of quaternary ammonium compounds. These chemicals are known as surfactants and can therefore lower the surface tension between treated wood and water [93].

The summary table suggests that k_inh is dependent on the wood species (i.e., initial durability, permeability), the type and concentration of the wood preservative, and the impregnation process (Table 8). Furthermore, one needs to consider the type of decay and respective test fungi, such as copper-tolerant brown rot fungi, which make co-biocides play an important role in copper-containing preservatives [94]. This became particularly evident for wood treated with preservatives containing only copper as the active ingredient. In addition, the unexpectedly high v_rel. might be attributed to the leaching of active ingredients such as boron, which led to high k_inh values in laboratory tests without pre-aging.

Copper-based treatments are generally effective in the laboratory against fungal mono-cultures. Even shell treatments can be sufficient to prevent hyphal penetration to the central part of the specimens. However, surface cracks are formed when larger specimens are exposed outdoors, which enables access to the specimen core by decay fungi. In addition, leaching of active ingredients contributes to the higher susceptibility of wood to decay as well. However, copper mobility has also been shown to protect checks that expose untreated wood from the germination of basidiospores from copper-tolerant fungi [95,96]. Copper-ethanolamine-treated wood was considerably less effective against soft rot fungi than CCA-treated wood. On the one hand, this is likely the result of increased copper leaching during exposure to terrestrial microcosm. Up to 80% of the copper from the specimens is leached during such tests due to humic acids in compost soil and other acids excreted by fungi and bacteria [97]. On the other hand, arsenic also serves as an efficient co-biocide against soft rot fungi.

3.4. Model Fitting

The most suitable factors and factor combinations, respectively based on results from the wetting ability and durability tests, can be extracted from

Table 9. Coefficient of determination R² for the relationship between the relative resistance dose D_Rd and the relative mean decay rate (v_rel.) in above-ground durability tests. D_Rd was calculated based on different combinations of factors accounting for protective inherent properties (k_inh) and moisture performance (wetting ability, k_wa) using data from the references provided in Table 5, Table 6, Table 7 and Table 8. First line: R² at a threshold of 17, second line R² with the threshold in brackets that gave the best fit. (br = brown rot; wr = white rot; LWU = liquid water uptake; VU = vapor uptake; WR = water release; CWU = capillary water uptake).

kinh	kwa
	LWU	VU	WR	CWU	Mean 1	Min 2	1 3
Soil	0.133 0.218 (2)	0.059 0.072 (4)	0.000 0.202 (1)	0.291 0.295 (14)	0.168 0.202 (3)	0.010 >0.015 (∞)	0.110 0.166 (3)
br:wr,Mean	0.298 0.299 (26)	0.255 0.267 (34)	0.076 0.112 (38)	0.369 0.375 (36)	0.341 -	0.138 0.172 (36)	0.311 0.313 (26)
Min 2	0.150 0.241 (2)	0.073 0.087 (4)	0.000 >0.002 (∞)	0.376 0.381 (36)	0.189 0.217 (3)	0.018 >0.021 (∞)	0.133 0.190 (3)
Soil:no Soil,Mean	0.288 0.291 (27)	0.220 0.241 (35)	0.045 0.081 (38)	0.377 0.383 (36)	0.337 -	0.109 0.148 (37)	0.311 0.314 (26)
Mean 1	0.295 0.297 (26)	0.238 0.255 (35)	0.058 0.095 (38)	0.376 0.381 (36)	0.342 -	0.122 0.160 (37)	0.315 0.317 (26)
1 3	0.295 0.297 (34)	0.000 0.035 (1)	0.209 0.258 (1)	0.176 0.177 (16)	0.094 0.097 (15)	0.110 0.126 (1)	-

¹ Mean of the four different k_wa or k_inh values, respectively, was used for modeling. ² Minimum of the four different k_wa or k_inh values, respectively, was used for modeling (worst case). ³ Factor set to 1.

where the coefficients of determination R² for the various combinations are summarized. One of the best fits (R² = 0.336; Figure 1c) between v_rel. (Equation (5)) and the relative resistance dose (D_Rd,_rel) (Equation (6)) was achieved by using the mean value of the four k_wa factors multiplied with the k_inh factors based on soil contact tests and k_inh factors based on non-soil contact tests weighted equally (Equation (7)). This pair of factors led to the best fit already in the initial material resistance model presented by Meyer-Veltrup et al. [18]. However, the fit itself was poor and a slightly better fit (R² up to 0.376) was reached when k_wa was calculated based on CWU data and combined with k_inh, either based on mean, minimum, or equally weighted values from soil and non-soil tests. However, the latter was mostly attributed to a series of tests performed with softwood species treated with different copper systems (Table 8); these showed highly varying CWU and thus contributed to higher prominence of a relationship between the relative D_Rd and v_rel., which as such was only poorly fitted (see Figure 1b).

In contrast to the differently treated materials, a good fit between relative D_Rd and v_rel. was observed for the untreated timbers (R² = 0.823) at a threshold for k_inh and k_wa of Thr = 3. Meyer-Veltrup et al. [18] only used untreated wood for the initial model which had an even better fit (R² = 0.912), but covered a smaller range of durability. In this study, several very durable tropical hardwood species were considered, such as Bongossi, Amaranth, merbau, Basralocus, and teak. Furthermore, a larger variety of data sources representing different test methods, test locations, and inspection and assessment techniques were considered and helped explain that both D_Rd and v_rel. scatter more compared to the previous model. However, the model was robust even when including several very durable wood species from a variety of data sources (R² = 0.823).

4. Conclusions

The Meyer-Veltrup model predicts relative decay rates (v_rel.) based on a material resistance dose representing the inherent protective properties of wood and its wetting ability. The optimized Meyer-Veltrup model presented in this paper produced reliable decay rate estimates for a range of new species and treatments. However, the predictive power of the model was stronger for untreated than for modified and preservative-treated wood. Both modified and treated wood add influence factors to the model, such as type and concentration of the preservative, varying levels of treatment intensity (e.g., weight present gain or treatment temperatures), process conditions, and an increased variability within and between treatment batches. It is assumed that material- and treatment-specific parameters of water-repellant treated and coated wood will also affect the reliability of the model. To further increase the accuracy and fit of the model, more data are needed, which should: (1) match laboratory indicators and field performance, and (2) cover further parameters, which are specific for treated wood and were lacking (i.e., unavailable) in the recent study.

Assessing data quality remains a major challenge when utilizing test data, especially for treated material. Due to the lack of directly matched data for a specific material, it is unavoidable to group sets of data that are similar but still show some discrepancies. In Part 3 of this paper [98], we will utilize the presented modeling approach and provide estimates of relative decay rates based on: (1) above-ground field tests at many different locations, and (2) relative D_Rd values from an intensive global survey on durability data.