Degradation of wooden surfaces in historical buildings: integrated sensing and modelling techniques for monitoring and conservation

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1 Degradation of wooden surfaces in historical buildings: integrated sensing and modelling techniques for monitoring and conservation Mariapaola Riggio 1 ; Jakub Sandak 1 ; Anna Sandak 1 ; Ilaria Santoni 1 ; Leszek Babinski 2 1 Trees and Timber Research Institute IVALSA/CNR, San Michele all Adige (TN), Italy; 2 Archaeological Museum in Biskupin, Gąsawa, Poland 1. Introduction The use of wood in buildings has a very long tradition. Due to its biological nature, wooden artefacts can undergo to alteration during their service life that can be caused by mechanical, environmental or biological agents. If these agents alter old buildings, the loss of mechanical resistance of timber structures can be added to loss of cultural heritages. An early evaluation of the altering phenomena is crucial to design remedial treatments and a less invasive restoration intervention. In the presented paper different degradation phenomena were simulated on wooden samples, in order to evaluate their effects on different physical parameters of the material, and fi nally, on its aesthetical and technical durability. In particular, the following altering phenomena were analysed: weathering, biotic decay and anaerobic aging (i.e. waterlogging). Weathering is the general term used to define the slow degradation of materials exposed to the weather. Wood exposed to weathering is degraded by various environmental agents such as solar radiation and stresses imposed by cyclic wetting, temperature changes, environmental pollutants. The rate of weathering varies, depending on timber species, detailing and finishing solution, but most of all on local conditions. Unfinished and unprotected timber weathers as a result of gradual changes to its physical/chemical structure. In general, the process of wood weathering leads to a slow breaking down and removal of surface fibers, roughening of the surface and reduction of the glossiness. The colour of untreated wood also changes rapidly, due to photodegradation of lignin in middle lamella and of wood extractives. The exposed cellulose reflects light giving a silvered appearance but then staining fungi could darken this, leaving a greyish surface [Huanga et al. 2012]. Under extreme conditions, timber may deform, check and split. The formation of discontinuities on the wooden surface can trigger penetration of the wood-decaying biological agents into the material. The susceptibility of wood to biodeterioration is related to its chemical composition. A wide range of organisms can live in and degrade wood, specifically certain bacteria, fungi, insects, crustacea and molluscs. These organisms live by consuming complex organic materials, which form the structural and non-structural components of wood. They can live and proliferate only under specifi c oxygen, water and temperature conditions. The wood-degrading fungi can be divided into moulds, stainers, soft-rot fungi, and wood-rotting Basidiomycetes. These are listed in the order of increasing levels of damage of wood and in the order in which they colonise wood [Singh, 2000]. The distinction between molding and staining is made primarily on the 898

2 basis of the depth of discoloration. The most impairing effect of stain and mold fungi is on the appearance of wood artefacts, while they affect material strength only slightly. Soft-rot fungi are capable of breaking down lignocellulose and therefore of causing severe strength loss. These fungi typically attack wood in permanently moist, or on surfaces that have been alternately wet and dry over a substantial period. Wood-rotting Basidiomycetes can be distinguished in brown rot and white rot. With brown-rot fungi, only the cellulose is extensively removed, the wood takes on a browner colour, and it can crack across the grain, shrink, collapse, and be crushed into powder. With white-rot fungi, both lignin and cellulose usually are removed, the wood may discolour becoming lighter due to the major degradation of lignin. It does not crack across the grain, and until severely degraded, it retains its outward dimensions. When wood is saturated with water and oxygen level is low, bacteria are the main wood degraders. Therefore, bacteria are the main degrading factors of wooden items buried in the ground or submerged in water (waterlogging) such as foundation pillars, ships etc. According to their specific decay pattern three types of wood degrading bacteria are distinguished: cavity, tunnelling and erosion bacteria [Kim et al., 2000]. Erosion bacteria degrade the wood cell wall by producing troughs into it, but don t metabolize lignin. Tunneling bacteria, which degrade interior of wood cell walls, might be able to metabolize all wood chemical components. Bacteria are the main degrading factors of wooden items buried in the ground or submerged in water (waterlogging) such as foundation pillars, ships etc. Estimation of the degradation state is a first and fundamental phase for proper maintenance and conservation of wooden artefacts. Recent developments in the fields of optics and electronics opened new possibilities for non-destructive measurements of various physical and chemical properties of materials. The advantage, beside of the nondestructive character, is the simplicity of measurement and possibility of numerous repetitions what significantly increases the reliability and accuracy of data. Moreover multi-sensor approach combined with multivariate data analysis is often an indispensable tool for the analysis and evaluation of complex phenomena.estimation of the degradation state is a first and fundamental phase for proper maintenance and conservation of wooden artefacts. The goal of this research was to investigate applicability of some nondestructive techniques into evaluation of the nature and severity of degradation of wood exposed to different altering phenomena, namely natural weathering, fungi infection, waterlogging. Another objective of the reported study is to use numerical analysis tools for determination and quantification of degradation stage and prediction of exposure time of degraded wood. 2. Materials and Methods 2.1. Experimental samples Weathering Weathering tests were performed on a set of wooden blocks 80x150x20 mm (tangential x longitudinal x radial direction, respectively) of Scots pine (Pinus silvestris) heartwood. Samples were exposed to natural weathering in seven locations (Fig.1a) in Italy and southern Germany and four exposures (North 899

3 -N- East -E-, South -S-and West-W-). The material was exposed for four years, collecting set of representative samples each year. After collection from the exposure stand, the samples were kept in an air-conditioned dark chamber to avoid the eventual progress of the weathering Decay Norway spruce (Picea abies L. Karst) timber was used for specimen preparation for durability tests. Before the decay test the samples were leached according to the standard EN 84. Durability test against fungal attack was carried out according to the European standard EN 113. Selected Basidiomycetes fungi were used in the tests. White rot fungus was represented by Trametes versicolor CTB 863A and three brown rots were represented by Coniophora puteana BAM Ebw.15, Gleophyllum trabeum BAM Ebw. 109 and Postia placenta FPRL 280 fungi Waterlogging Effect of waterlogging was analysed using Scots pine (Pinus sylvestris L.) and Pedunculate oak (Quercus robur L.) samples. Not waterlogged wood as well as archaeological waterlogged samples were used as reference material. Samples measured approximately mm (longitudinal L tangential T radial R), at moisture content of 12%. Basic density BD of waterlogged specimens, computed as a ratio of the oven-dry wood mass to the volume of the water-saturated sample, was determined for quantifi cation of the degradation degree. Test material was investigated after 2, 4, 6 and 8 years waterlogging in two different deposition environments: under a layer of wet peat and at the bottom of a pit fi lled with water. The deposition sites were located in the Biskupin archaeological site (Poland) Sensing techniques A multi-sensor measurement chain, including both laboratory and on-site techniques, has been developed for the acquisition of wood surface properties at the different structural levels of the material (molecular, microscopic, macroscopic). These techniques include: colour, gloss, roughness measurement, near (NIR) and mid (MIR) infrared spectroscopy and X-Ray Fluorescence spectroscopy Colour measurement and defect detection The colour was measured by using two differing equipments: a spectrometer DataColour equipped with an integrating sphere, and an UV-VIS-NIR spectrometer Maya2000pro (Ocean Optics). Equipment calibration was carried out with the standards provided by the supplier. The colour was characterized in the CIELab system and three parameters, (L*, a* and b*) were computed from each spectra, spectra assuming observer angle of 2 and reference illumination D65. According to the CIELab definition L* represent the lightness (and varies from white to 0 - black), a* and b* are the chromaticity coordinates (+a*/ a* is for the red/green and +b*/ b* for yellow/blue). Digital images of sample surfaces were acquired on HP G2710 scanner ad- 900

4 ditionally to colour spectra. Image processing algorithms were developed in LabView (National Instruments) for image analysis and defects detection Gloss analysis Gloss meter REFO 60 produced by DrLange with an angle of incidence of 60 has been used for measuring light-refl ective properties of the surfaces. Three measurements in two directions (perpendicular to the grain and longitudinal -along the grain) were taken from the surface of each sample. Bi-directional refl ectance function was scrutinized by means of custom 3D gloss meter developed at IVALSA/CNR in addition to standard gloss measurements Surface roughness analysis The surface roughness was measured by means of stylus, laser displacement sensor and 3D triangulation scanner. The surface maps were then analysed with Gwyddion ( open-source software. The cut off-fi lter for the 2D profiles was 0,8mm, where the pre-processing of the 3D maps was based on the low pass filtering (Gaussian noise reduction). Several surface-texture parameters can be obtained from this method. Three commonly accepted roughness parameters including Ra (average surface roughness), Rz (mean maximum of the peak-to-valley height) and Ry (maximum roughness) were computed for quantification of the surface roughness Infrared spectroscopy NIR and FT-IR spectroscopy has been used as a technique for obtaining information about the molecular structure of the wood surfaces. Vector N-22 NIR and ALPHA FT-IR spectrophotometers (both produced by Bruker Optics GmbH) were used for characterization of the aged wood surfaces. The NIR spectral range was from 4000cm-1 to 12000cm-1 and for FT-IR spectral range was from 400cm-1 to 4000cm-1. Spectra were postprocessed and analysed with OPUS 7.0 software Elemental composition X-ray fluorescence (XRF) spectrometer (Oxford - X-MET5100) was used for quantification of the changes to the atomic composition (especially metals) of the weathered wood samples. The X-ray tube of the instrument was energized with a voltage of 45 kv and current intensity of 40 μa. The scanning time was 30 seconds. Both, decrease and increase of the elements content (due to leaching and deposition respectively) was evaluated Modelling techniques Parameters collected by means of the different NDT techniques were analysed with multivariate statistical methods, in order to identify causes, effects and progress of degradation phenomena. Commercially available (The Unscrambler X, by CAMO Software AS and OPUS 6.5 by Bruker Optics GmbH ) and custom made (LabView by National Instruments) software packages were used for post processing and data mi- 901

5 Fig.1 - Appearance of wood surfaces exposed to degrading factors (weathering, decay and waterlogging) ning. Principle components analysis (PCA), Partial Least Squares (PLS) and multiple linear regression were used for chemometric model development. 3. Analysis of results Figure 1 shows examples of pine wood samples surface appearance altered by various phenomena as studied in this research. In the weathered samples characteristic grey colour of surface is visible, which is related to the photodegradation of lignin. Moreover visible crack and splits are present after 4 years of natural weathering. Decayed wood possesses characteristic discolorations, dimensional/shape changes, elevated roughness and often distortions related to collapse of cell wall. In waterlogged wood however, changes of colour were noticed even after short term leaching in water. Pine wood become slightly lighter, when oak become significantly darker. Changes of some of the most significant parameters of wood properties are briefly presented in the following sections for the differing degradation mechanisms Weathering The full set of data from measurements on weathered samples was processed using multivariate analysis. Figure 2a presents the statistical model (multiple linear regression) developed to predict the exposure time (in years) of surfaces affected by weathering. As can be seen in Fig.2b, each sensing technique contributes to the model, even if not all parameters are equally significant. In particular, it can be noted that, XRF data are affected by the exposure time: even if the metal/mineral content in the fresh wood is minimal, the quantity changes during weathering due to possible deposition of chemicals with rainwater and/or air pollutions (Sandak J. et al., 2013) Decay Infrared spectroscopy has a great potential to be used as a method for rapid classification of decay type, assuming development of proper identification models (Sandak A. et al. 2013b). In the example presented here, the second derivatives of all raw spectra have been subjected to PCA and the obtained results are presented in Figure 3. Spruce samples degraded by the investigated fungi were grouped and clearly separated from each other. The infrared spectroscopy can be also useful tool for direct estimation of the fungi degradation degree. In the example presented below the NIR and IR spectra were subjected to PLS, where the mass loss was considered as a re- 902

6 ference for the model. Prediction capability of the model developed is shown on Figure 4. The cross-validation used here was the one leave out approach. Optimization procedures were carried out, in order to obtain the best performing models. the most efficient set of parameters was; first derivative pre-processing of NIR spectra in regions cm-1 and cm- 1. The corresponding coeffi cient of determination r2 is equal to 0.93, the root mean square error of cross-validation RMSECV is 3.7% of mass loss, the residual prediction deviation RPD is 3.79 and the rank (number of principal components used) is 6. Very similar results were also obtained by means of IR spectroscopy, where the best PLS model for mass loss prediction was with vector normalization in the range of cm-1. The r2 is 0.93, RMSECV is 2.1% of mass loss value, RPD is 3.77 and the rank is Waterlogging PLS regression models were also used for development of calibration models for waterlogged wood. An example of basic density prediction is presented in Figure 5. The r2 calculated after cross-validation is 0.99, RMSECV is 1.04kg/ m3, RPD is 7.77 and the rank is 7, obtained with pre-processing of NIR spec- Fig.2 - Multiple linear regression model for prediction of exposure time (a) and importance of parameters (regression t-values) for the model (b) 903

7 Fig.3 - Principal component analysis of spruce wood degraded by brown and white rot fungi; Fig.4 - Prediction of mass loss caused by fungi with PLS tra by the second derivative in the spectral ranges cm-1. It is also possible to characterize the waterlogged wood degradation state by means of colour change. Figure 6 presents changes of lightness L* values for both oak and pine woods waterlogged in different periods of time. Analysis of visible reflectance spectra show that pine tends to lighten along the waterlogging time, while the opposite tendency is observed for waterlogged oak. It can be noted that changes are less apparent in the case of pine, while a significant drop of L* values can be observed in oak samples, even after only 2 years of waterlogging. Finally, the wood buried under wet soil tends to darkening after long periods waterlogging (archaeological wood for example). 4. Conclusions The selected results presented in this manuscript show some examples of successful application of multi-sensor analysis, combined with multivariate analysis, for the characterization of wood modifications due to various altering phenomena. Potential implementation of different numerical methods/algori- Fig.5 - PLS model calculated for basic density of spruce wood, after 2, 4, 6, 8 years of waterlogging, and archaeological waterlogged samples; Fig.6 - Changes to lightness (L*) component of 904 the surface color in water and peat waterlogged pine and oak woods

8 thms to extract useful information from the huge measurement datasets was also highlighted. The implemented methodology showed to be effective into the evaluation and simulation of different mechanisms of wood degradation in historical buildings. Models for prediction of wood exposure time, basic density of wooden members and mass loss caused by fungal decay were developed and positively validated. After further developments such methodologies can serve as assisting tools for non destructive assessment of the wooden structures, service life prediction of buildings/surfaces and to support selection of optimal conservation process. Acknowledgments Part of this work has been conducted within the framework of the project SWORFISH (team 2009 incoming (CALL 2) and Trentino-PCOFUND-GA ) co-fi nanced by Provincia Autonoma di Trento. References European Committee for Standardization EN 113, Wood preservatives - Test method for determining the protective effectiveness against wood destroying basidiomycetes - Determination of the toxic values, European Committee for Standardization EN 84, Wood preservatives. Accelerated ageing of treated wood prior to biological testing. Leaching procedure, Huanga X., Kocaefea D., Kocaefea Y., Bolukb Y., Pichettea A., 2012, A spectrocolourimetric and chemical study on colour modifi cation of heat-treated wood during artifi cial weathering, «Applied Surface Science», 258, Kim Y.S., Singh A. P., 2000, Micromorphological characteristics of wood biodegradation in wet environments: a review, «IAWA Journal», Vol. 21 (2), Sandak A., Ferrari S., Sandak J., Allegretti O., Terziev N., Riggio M., 2013, Monitoring of Wood Decay by near Infrared Spectroscopy, in Structural Health Assessment of Timber Structures, eds. M. Piazza and M. Riggio, Volume 778 of Advanced Materials Research. Sandak J., Sandak A., Pauliny D., Riggio M., Bonfa S., Meglioli S., 2013, A Multi Sensor Approach for Prediction of Weathering Effects on Exposed Timber Structure, in Structural Health Assessment of Timber Structures, eds. M. Piazza and M. Riggio, Volume 778 of Advanced Materials Research. Singh J., 2000, Fungal Problems, in Historic Buildings, J. Archit. Conserv., 6, Abstract Wood is one of the oldest building materials and, thanks to its favourable mechanical and technological properties, one of the most versatile (e.g. both apt for structural and not structural uses). Due to its biological nature, wooden artefacts can undergo to alteration during their service life that can be caused by mechanical, environmental or biological agents (bacteria, fungi, insects). Wood surfaces are the most vulnerable parts of the artefact, since they are the most exposed to altering factors, such as wear and/or weathering. The assessment of actual conditions of wooden surfaces in existing buildings and the possibility of predicting their future performance is a key issue for any conservation plan. In this paper it is discussed the integrated use of NDT methodologies and modelling techniques to assess, monitor and predict wood surface properties and alterations, as a support for conservation actions. 905