Mechanical properties of the most common European woods: a literature review

Wood is an orthotropic material used since ancient time. A literature research about the mechanical properties of density, fracture toughness, modulus of elasticity, and Poisson’s ratio has been done to have a broader view on the subject. The publications relating to the topic were found through the two search engines Scopus and Google Scholar that have yielded several papers, including articles and book sections. In general, there is no standardization on the method of analysis carried out on wood, underlining the great difficulty in studying this complex material. The parameter of density has a great variability and needs a deeper investigation; fracture toughness is not always available in literature, not even in the different directions of the wood sample. Interesting is the modulus of elasticity, which provides a correlation with density, especially in longitudinal section but, again, it needs to be studied in detail. The parameter of Poisson’s ratio is provided as single values in three different directions, but mainly for softwood. All the parameters require a more in-depth study for both softwood and hardwood. Furthermore, the type of analysis, whether experimental or modelling, needs to be standardized to have more comparable results.


INTRODUCTION MATERIALS AND METHODS
he systematic literature review was performed using a six-steps process [7] shown in Fig. 1. The selected topic (mechanical parameters of wood) is very broad and publications dealing with this matter come from different background studies. All the articles, papers and book sections used for this literature review were searched using the web platforms of Scopus (Elsevier, The Netherlands) and Google Scholar.

Research with Scopus and Google Scholar
Studies on wood and mechanical properties published in the literature were identified via the above described search engines. In the first phase, the query search strategies via the Scopus engine were: (S1) "toughness" AND "softwood" and (S2) "toughness" AND "hardwood" which yielded 62 and 49 articles, respectively. The searching process was then refined (step 2 in Fig. 1) providing a selection up to 4 papers per set of keywords. Then, in a second phase, through the search terms of (S3) "acoustic emission" AND "wood properties", (S4) "tensile test" AND "coniferous wood" OR "spruce" and (S5) ("toughness" AND "wood") AND "hazel" a total number of 64 papers matched the search parameters (5, 56 and 3 articles per query search strategy, respectively). The accurate analysis of articles in step 2 ( Fig. 1) retrieved for S 3 one paper: for S 4 two papers and for S5 one paper. A total of 175 publications were found after step 1 but only 12 papers were in line with the topic of the present work after having performed the step two screening (Fig. 1). In addition, a second literature analysis was performed via Google Scholar. The keywords used in the first selection step were (G1) "Fracture Toughness softwood and hardwood", (G2) "Mechanical properties softwood and hardwood", (G3) "Wood fracture characterization", (G 4 ) "Softwood toughness". Then, additional focused search was performed using specific wood species and mechanical parameters i.e. (G 5 ) "Spruce fracture toughness", (G 6 ) "Norway spruce mechanical properties", (G7) "Oak fracture toughness", (G8) "Fracture toughness spruce and birch". For each search outcome a variety of papers were displayed with ten articles per page; each string in the Google Scholar outcomes contained in the title of a paper/book/report the words matching the keywords highlighted in bold. During the first screening (first step, Fig.1) the set of selected documents were obtained after having stopped the reading and discarded document with information no longer matching the search criteria. This generally happened after the first 3 pages displayed by the search engine, providing a total of about 240 papers to be analysed. Among these papers, after a second screening carried out by reading each paper in detail (step 2, Fig. 1), only 18 were considered in line with this research. Then After a final refinement only 8 papers were finally selected. The results of the search strategy are summarized in Tab. 1. Moreover, a book and an article were added to the selected papers. These two additional documents did not come from Scopus or Google Scholar search engines, and they were marked, later on, as "Other Sources". The outcomes of the search steps reported in this section are analysed here statistically to underline the type of studies and catch information about the year, place, and purpose of the investigation presented in the retrieved articles. Regarding the type of document, Google Scholar search engine identified different types of documents (e.g. books or sections of books and papers), whereas the Elsevier database "Scopus" was limited to articles and conference papers. At the conclusion of the selection process, the two platforms together provided a total of 20 publications, two of which overlapped. Among these 18 documents 15 were articles, two were book sections and one was a conference paper. The affiliation of the main authors involved in the topic of this review, is analysed, and shown in map (Fig. 2, [8]  In general, the expertise and scientific background of the authors involved in experimental and theoretical studies on the mechanical properties of woods are labelled by both Scopus and Google Scholar platforms as "Material Sciences", "Agricultural and Biological Sciences" and "Engineering" subjects' areas. The subjects of each retrieved document are analysed using topic labels based on the background of the authors and on the keywords used in their publications. Fig. 3 reports in the inner ring the subjects as obtained by Scopus, while in the outer ring the subjects by Google Scholar.  . a) Number of papers by year after the refine diversified by different colours: orange for "Scopus", blue for "Google Scholar", and grey for "Other Sources". b) Number of documents by year found through Scopus research. Orange solid line: total sum of documents from five groups of keywords drawn each one with dashed lines (S1): "toughness" AND "softwood"; (S2): "toughness" AND "hardwood"; (S3): "acoustic emission" AND "wood properties"; (S4): "tensile test" AND "coniferous wood" OR "spruce"; (S5): "toughness" AND "wood" AND hazel". c) Number of documents by year found through Google Scholar research. Blue solid line: total sum of documents based on the eight set of keywords drawn each one with light lines (G 1 ): "Fracture Toughness softwood and hardwood"; (G 2 ): "Mechanical properties softwood and hardwood"; (G 3 ): "Wood fracture characterization"; (G 4 ): "Softwood toughness"; (G 5 ): "Spruce fracture toughness"; (G 6 ) "Norway spruce mechanical properties"; (G 7 ) "Oak fracture toughness"; (G 8 ) "Fracture toughness spruce and birch".
The analysis of the number of studies per year (Fig. 4a) over the last 40 years can provide an index of interest on the topic from the scientific community. The three different colours (orange, blue, and grey) represent the documents found via the search engines Scopus, Google Scholar, and "Other Sources" respectively. It can be noticed that there are no clear peaks in publications, however the increase of interest can also be derived by the frequency of papers (i.e. number per year) and by the comparison of the outcomes from the two search engines (Fig. 4). The analysis highlights that there was a general interest with publications of books, reports, conference papers between 1995 and 2015 (Google Scholar, Fig. 4c) and that most significative results were published in 2002, in 2007/08 and, recently in 2015/17 (Scopus, Fig. 4b). The final selection approximately yields one or two articles per year. Interesting is the pioneering research in this field -within our time window -occurred in 1981 (Google Scholar) which remained an isolated publication from the United States for quite a long time. The Figs. 4b and 4c represent the total number of documents by year found through Scopus (orange) and Google Scholar (blue). The dotted and dashed lines represent all the papers found per year after the application of the first screening step. While, the full lines marked as "TOT" in the graphs report the total sum of the papers. These two thicker lines demonstrate as the search platforms provide different results: Fig. 4b shows the presence of various peaks, while Fig. 4c predicts an increasing trend up to the maximum peak between 2005 and 2010. Furthermore, Fig. 4b shows that the interest in this field has been growing in recent years because of the presence of several close peaks, even if lower than the highest one occurred in 2002. Fig. 4c, provides information regarding a search platform (Google Scholar) that has a greater selection of document types. Although the two search engines have different trends, they seem comparable as both graphs show an increase of interest over the last 2 decades in the analysed research field of the mechanical properties of wood.

Method for dataset design
All the articles analyzed in this review are, from now on, identified with an ID number reported in Tab. 2. Besides it, the papers are divided according to the search platform from which they were retrieved. In the following sections, the publications will be cited with their specific Paper ID.
Paper ID Reference Source  Starting from the reading of the selected articles and books, a first approach has been to verify whether the described mechanical parameters were obtained experimentally or through numerical simulation, integrating the information with the equations for the calculation of fracture toughness which is considered to be the main parameter to be analysed in the present work. Then, a database (DB) has been created for both types of wood (softwood and hardwood), showing the mechanical properties of density (i.e. the ratio between mass and volume of a given substance, unit kg/m 3 ), fracture toughness (i.e. K IC is the critical stress intensity factor of a crack where propagation suddenly becomes rapid and unlimited, unit kPa*m 0.5 ), modulus of elasticity (MOE) or Young's modulus (i.e. E is the ratio between tension and deformation, unit MPa), and Poisson's ratio (i.e. a pure number is the parameter that describes the expansion or contraction of a material in directions perpendicular to the loading direction, no unit). The values of these variables -taken from the 20 selected documents -are displayed in Tabs. 3 and 4. It is important to underline that not all the parameters were available in each document at the same time. For example, in the book section with ID18 [4] and for articles with ID3 [11], 8 [16], and 12 [20] the type of fracture parameters provided was not comparable or transformable into KIC by means of further equations.
For articles with ID11 [19] and ID17 [25] the value of toughness (G f ) was found in place of fracture toughness (K IC ). However, using the Eqn. (1) (also reported in Appendix -section 2, A2), that relates the two parameters to the modulus of elasticity, KIC was calculated and added to the dataset. The paper with ID15 [23], also allowed the calculation of KIC using an equation reported in Tab. 5 after knowing the value of specific gravity (Sg) found in the article itself. Finally, in the case no parameters were found, a horizontal dash was placed in the DB.   The box plots and scatter plots were created using the input recorded on the DB (i.e. Tabs. 3 and 4). A box plot consists of the minimum and maximum range values (i.e. a rectangle shape which contains 50% of the distribution), the upper and lower quartiles (i.e. the whiskers, referring to 1.5 of the interquartile range), the median or the average value (i.e. the horizontal line within the rectangle shape). Often, above and below the whiskers, there are some external points called outliers which represent the extreme values of the dataset. The box plot is a powerful statistical tool that allows to summarize the distribution of a dataset [28]. The use of the scatter-plot, also called x-y diagram, allows to identify the relationship between two variables, clusters of points and outliers and it is one of the most common ways to visualize multidimensional data [29].
In the next paragraphs, box-plots and scatter-plots are used to study and analyze the parameters of density, fracture toughness, modulus of elasticity and Poisson's ratio individually to understand trends and checking possible inconsistencies between the results obtained in literature. Then, by means of scatter plots, the parameters of fracture toughness, modulus of elasticity and Poisson's ratio are studied as a function of the density. 10   , relative humidity (%) and temperature (°C). In Paper ID4, "Sat." is "saturated"; in Paper ID14 "est." is "estimated value". The acronyms are the same of those described in Tab. 3.

RESULTS AND DISCUSSIONS
efore proceeding with the analysis of the values obtained in the DB, it is necessary to understand in which way (i.e. methodological approach; type of geometry; type of test;..) the parameters were found by the authors of the selected papers. Tab. 5 shows the geometries of the samples used, the type of analysis performed on them focusing on the load or displacement and on the acquisition method of the parameters and, if available, the equations used for the calculation of fracture toughness, our main mechanical parameter of interest. The table demonstrates that the majority of the analyses were carried out using CT (Compact Tension) and WCT (Wedge notched CT) specimens. These are the best-known geometries together with the dog bone shape specimen. In general, it can be noted that CT/WCT and dog bone shape samples are used for two types of variables acquisition method. The first, it is used for splitting tests while the second for tensile tests analysis. Moreover, seven papers are linked to the use of a rectangular prism specimen. Differently from the CT/WCT samples, the rectangular prism is linked to various types of variables acquisition method, e.g. Arcan test, Duncan's test, bending, tensile, and compression tests. A further study is necessary for papers ID1 [9] and ID8 [16] because, as shown in Tab. 5, the geometries involved in these researches are different from the others. The first one is a pyramid trunk (DCB specimen) used by the authors to facilitate direct observations of the fracture process, while the second one is a cuboid specimen that was useful for researchers to show how toughness varied around the hazel forks, finally it was different for the wood specie they selected for their study (ID12 [20]). Load and displacement rates are not the same in each study and not all the papers report their values, so it is difficult to find a standard procedure for this type of information. Concerning the acquisition method, it is clear that there is a sort of standardization of the studies about properties of wood: actually, the investigation often starts with a mechanical test on wood (e.g. splitting, bending, compression etc.) and then it continues with an analysis by Scanning Electron Microscope (SEM). Subsequently the values of the mechanical properties found in the experiment are often compared with numerical simulations, such as Finite Element Method (FEM) of analysis. This process was not applied to all the papers: in some articles only a mechanical experiment was carried out, in other cases only the FEM analysis, while in other articles the analysis was used in pairs (as in ID11 [19], which includes a fracture toughness test coupled with a FEM analysis). Another type of test used the Acoustic Emission (AE) No Destructive Technique (NDT) as in ID2 [10], ID3 [11], ID9 [17]. Tab. 5 demonstrates how future studies in this field of research should be based on a specific guideline, which give the possibility to compare search results more precisely. In the following sub-paragraphs, box-plots, and scatter-plots regarding mechanical properties of the most common European woods are reported and the results discussed for both softwood and hardwood.

Density
Looking at Fig. 5, it can be noticed that hardwood has higher density values than softwood, as already known in the literature. Particularly, this review confirms as the density of the eight most common hardwood species has values ranging between 467 and 910 kg/m³. Specifically, as regards hardwoods, one of the woods less dense is Cherry (Ch), as already known; while Beech (Bee) and Birch (Bir) species represent, respectively, the lowest (minimum) and the highest (maximum) values of density. In fact, as reported in Fig. 5a, their box-plots have a wider range of values. Beech and Birch density values (ID6, [14]) varies respectively from a minimum of 540 kg/m³ to a maximum of 910 kg/m³, and from a minimum of 510 kg/m³ to a maximum of 830 kg/m³. Since wood is an orthotropic material, all the values that are significantly lower or higher than the average value of Beech and Birch (that are respectively 725 kg/m³ and 670 kg/m³), could be derived from analysis carried out in different directions. Going on with the analysis, the DB reported in Tab. 4 shows how the smallest Birch density values was reported in the paper ID16, [24]. In this article, the authors analysed the most external parts of the Finnish Birch stem, that are also the regions where orthotropy is higher, measuring the density between the rings 12 and 40 of the stem cross section. The punctual measurements of this research pointed out different results compared to the average value reported in Fig. 5. Concerning softwoods (Fig. 5b), the most common species reported here, have a range with lower values if compared to hardwood, ranging between 225 and 590 kg/m³. It is possible to notice that there are different species of Spruce (Sp). In this case, density values were available for Spruce (Sp, mostly European) and Spruce Engelmann (Sp_Eng) which are comparable, although with a single value from the literature. Similarly, fir with only one density value deriving from the analysis of ID8 [16] does not allow a proper statistical interpretation. Then, Pine and Scots Pine (Pine_Sc) that are among the most common species of European woods highlight a higher average and a smaller dispersion for Scots Pine than Pine.

Fracture toughness (K IC )
The graphs regarding fracture toughness (Fig. 6) Fig. 7 relates to all the possible 6 directions (LR and LT in addition to the previous ones cited for hardwood). It can be noticed that these values are similar among the different species even in the different directionalities. In general, all the values are under 800 kPa·m⁰⸱⁵ in RL, TL, TR, and RT directions. The exceptions are found for LT and LR directions that have higher and more dispersed values. For example, the fracture toughness for pine in LR direction ranges between 1520 and 2998 kPa·m⁰⸱⁵ (ID14 [22]). This is the only specie, that in literature reported data concerning LR and LT, no values were found for Spruce or Fir. Both hardwood and softwood present some outliers which refer to Oak and Pine in article ID7 [15]. These values of fracture toughness were obtained from specimens with a relative humidity (RH) very different from the relative humidity values of the other specimens in the DB. The scatterplots in Fig. 8 about hardwood and softwood relate KIC versus the density and at once summarize the information described up to now. In the softwood graph (Fig. 8 SW), values show lower KIC at lower density and a certain dispersion in the KIC which ranges from ca 100 to 700 kPa·m⁰·⁵. Moreover, the error bars in "x" axis are larger than those of "y" axis, highlighting as the results available in literature, concerning density, have still high variability. The hardwood graph (Fig. 8 HW) highlights the greater variability among the different species and directionality especially for the KIC data. It also shows that the above-mentioned values are in a wider range than those of softwood. The error bars in "y" axis point out that K IC values are distributed in a wider range mainly in TR direction.

Modulus of Elasticity
As expected, MOE values in longitudinal (EL) direction are much higher than those in the radial (ER) and tangential (ET) directions, as reported in Figs. 9 and 10. This is a direct consequence of the material orthotropy. In most of the examined hardwoods, the MOE in longitudinal direction is at least one order of magnitude larger than that in radial and tangential directions.  The mean value of E L is always greater than 10000 MPa. In particular, it is noticed that among all the hardwood analysed ( Fig. 9), the Beech (ID6 [14]) is the one with the highest MOE value in the longitudinal direction (EL= 140004000 MPa). Nevertheless, the range of MOE values for being high (i.e. 4000 MPa). The values measured in radial and tangential directions, despite being lower, follow the same trend: ET is slightly lower than ER except for Cherry (ID5 [13]), which has a value of ET = 885 MPa and a value of ER = 1069 MPa, that remains slightly greater. The MOE values in these two directions never exceed 2000 MPa.
On the other hand, as regards the softwoods (Fig. 10), the trend of the MOE values is not as regular as for hardwoods. For these types of wood, it can be noted that the average value of EL is always between 8000 MPa and 11000 MPa, which is lower than that of hardwood. However, there are some exceptions, in particular it can be noted that Pine and Fir have average values of EL respectively equal to 14125 and 13810 MPa considering the error bar. These values, together with the MOE EL for Spruce equals to 11250 MPa, are comparable to those of the hardwoods in the same direction. Also, in this case, the values of E R and E T are significantly lower than E L ; again, it can be emphasized that the MOE values in the radial direction are slightly higher than those in the tangential direction regarding Spruce. High values of ER, in relation to the trend of hardwoods, can be highlighted by Fig. 10. Pine (ID8 [16]) shows a value of ER = 5554 MPa, while Fir (ID8 [16]) has E R = 5713 MPa. Nothing can be said about the trend of the MOE in relation to the tangential direction for these two types of wood since no data were found in literature for this mechanical parameter.
Looking at the MOE trend as a function of density (Fig. 11), the hardwoods (Lower plot) exhibit a density that in general is greater than that of the softwoods (Upper plot); the trend that can be observed from the diagrams is that MOE in longitudinal direction is increasing with density: It is noted that Alder (ID2 [10]) has an average density of 510 kg/m 3 and a value of EL of 11700 MPa, Oak (ID2 [10]) shows an average density of 553 kg/m 3 and a value of EL of 13000 MPa and, again, for the Birch (ID6 [14]) it results E L equals to 15500 MPa. However, we need to pay attention to the density values of these wood species that although are well close, however the one used to calculate E L is affected by a scatter error much higher than the density value used to calculate ER and ET. Ash (ID2 [10]), which has an average density value of 701 kg/m 3 , has the highest average E L value of 15800 MPa. Finally, we find the Beech (ID5 [13]) that has a mean density higher than Ash (725 kg/m 3 ). However, the error scatter of Beech density is rather high, as we can see in Tab. 4; in addition, the average value of EL for this kind of hardwood is 14000 MPa but the maximum observed value in the data set used for evaluating this average value is the highest ever in this analysis and equal to 18000 MPa. With the exception of this last MOE, that is affected by a quite high error, and neglecting woods like Cherry and Walnut of which we do not have enough data, the trend of the MOE turns out to be almost linear, which means that wood species with higher density have larger MOE in the longitudinal direction. The same does not occur for MOE in radial and tangential direction: the values of ER and ET are often comparable each other and never exceeding 2000 MPa. Instead, for the softwoods (Fig. 11, upper plot) we cannot observe in the analysis of data the same trend we have found for the hardwoods. In fact, it is not always true that MOE in longitudinal direction increases with the wood density. It can be observed, indeed, that the Spruce (ID10 [18]) passes from a value of density of 275 kg/m 3 and of E L equal to 12800 MPa to a value of density of 479 kg/m 3 and of E L = 10000 MPa (ID2 [10]), that turns out to be even smaller. The only softwood showing an increase of MOE in longitudinal direction as a function of density is the Pine, which goes from density equals to 365 kg/m 3 and E L = 12750 MPa (ID8 [16]) to a density equals to 590 kg/m 3 and E L = 15500 MPa (ID4 [12]).
In the radial and tangential directions, one can basically observe constant MOE values with an increase in density for Spruce.
We cannot say anything about the other types of considered woods for the lack of data, but the anomaly of the Fir and Pine is still confirmed because they have higher values of E R in relation to hardwoods.

Poisson's Ratio
Several considerations can be made on the Poisson's ratio. To this aim, we recall that under linear elastic conditions when an element is loaded axially, the strain along the direction perpendicular to the load is proportional to the axial strain. The ratio of the transverse to the axial strain is the Poisson's ratio. In the following, we will identify these parameters by two capital letters: the first one is referred to the direction of the applied stress and the second one to the direction of the lateral strain. For hardwoods, the Poisson's ratio Longitudinal-Tangential (LT) is never less than 0.3 and this value is assumed by woods such as Alder, Oak and Ash (ID2 [10]). For some more specific wood species such as Oak (Red) and Ash (White) the values are slightly higher than 0.4 (see Tab.4; ID18 [4]). However, it must be considered that these latter results may be a consequence also of the type of test and sample geometry. The highest value is observed from the Birch (Yellow) and is 0.451 (ID18 [4]).
As for the Poisson's ratio Longitudinal-Radial (LR), it should be noted that for woods such as Alder, Oak and Ash it assumes the same value as the LT case. For the rest of the hardwoods, the value of the Poisson's ratio LR is kept below 0.4, except for the Birch (Yellow) which takes a value of 0.426 (ID18 [4]). Finally, the values of the Poisson's ratio Radial-Tangential (RT) seem to be the highest, despite the lack of some data, we can see that in general the average values are always greater than 0.5, except for Walnut whose average value is equal to 0.4105 (ID5 [13]). For wood such as Walnut and Cherry, the error scatter is substantial: 0.11 and 0.19, respectively. An increase in the RT Poisson's ratio is noted for the Birch (Yellow) which assumes a value of 0.697 (ID18 [4]).
As for the softwood it can be noticed that the values of the Poisson's ratio are not far from those found for the hardwood.
It is interesting to note that the LT values of the different types of Spruce fall in the same range, namely between 0.4 and 0.5, with the maximum value assumed by Spruce Dry Norway (ID14 [22]) very likely due to the geometry assumed by the sample that turns out to be rectangular prism. The values observed for the different types of pine appear to be always smaller than those of Spruce and in fact are contained in a range from 0.26 to 0.315. Also, the Fir (Subalpine) has a value less than Spruce that turns out to be equal to 0.341 (ID17 [25]). Concerning the value of the Poisson's ratio LR, we can notice that for Spruce the error scatter from the average value of approximately 0.4 is quite high and it can reach the value of 0.55; instead, for the Pine wood in Fig. 14 is evident a sharp decrease in the Poisson's ratio LR that turns out to be 0.13 caused by the low wood moisture content (MC) (ID4 [12]). Trends slightly higher than those of LT are recorded for other types of pine.
Concerning the RT Poisson's ratio, we generally notice an increase in the Poisson's ratio compared to LR, both for the different types of pine and for some types of Spruce (e.g. spruce dry Norway ID13 [21], Sitka, and Engelmann ID18 [4]).
The maximum recorded value is assumed by this latter specie and it is equal to 0.53. In the paper ID10 [18], it is noticed a strong reduction in the value of the RT Poisson's ratio, very probably due to the type of specimen geometry, that provides the value of 0.21. Consequently, it is therefore rather difficult to establish a common pattern for the trend followed by this parameter on the examined woods population. It is important to analyse the behaviour of the Poisson's ratio in relation to the density of different kinds of wood as reported in Fig. 14. The Poisson Ratio values are quite large for hardwoods having a high density (Fig. 14, lower plot), especially for wood such as Walnut and Cherry, where it is noted that a small variation in density is accompanied by a rather large variation in the Poisson RT value. Instead for woods such as Alder, Oak and Ash the values of LT and LR are always constant at 0.3 also varying the density (ID2 [10]). For softwoods (Fig. 14, upper plot), that have a lower density than hardwoods -and in particular for Spruce -we note that all the values of LT, LR and RT are contained in a range from 0.2 to 0.5, although the different data obtained on Spruce indicate different densities. It is interesting to note that the Pine, which has a density comparable to that of hardwoods, has values of LT and LR much lower than the hardwood themselves.

CONCLUSION
he acquisition of mechanical properties (e.g. density, fracture toughness, Modulus of Elasticity and Poisson`s Ratio) of the most common European woods has drawn a growing attention over the last 2 decades, although it is tending now to slightly decline in respect to the peak found around 2002-2008. This literature review has highlighted that, despite the large number of studies, not many are focused on the retrieve of its mechanical properties and especially on how they change in relation to both the surrounding environment -in which different types of wood materials are preservedand the methodological approach applied to obtain the results. This gap can be explained by both the orthotropic and hygroscopic nature of wood which makes time consuming, costly and difficult to conduct experiments to retrieve these mechanical parameters with both hardwood and softwood. In fact, beside the geometry of the samples used in these experiments, the materials themselves cannot be directly compared one to the other because of the differences in wood quality and seasoning process, past samples history, samples preparation tools, wood moisture content and sample defect. In addition, differences can be also introduced by the applied experimental procedure which includes type of equipment and setting parameters, laboratory room conditions, test duration, experience of the researchers. This literature review aimed to analyse and put order in the existing values of the mechanical parameters of the most common wooden species used in Europe and partially in Northern America. The achieved results in this work highlighted that: (i) Not all the mechanical variables are obtained at the same time for the type of analyzed wood and generally the analyzed wood section are limited as it requires a lot of material and expertise for the preparation of samples in all the possible directions in the radial, tangential, and longitudinal (RTL) plane. (ii) Concerning softwood, although the most used species are few, however there is a wide range of variability in the results for pine and spruce due to the slight, different species that not always are well defined in the work found in literature. (iii) A standardized methodological approach to retrieve the mechanical properties does not still exist. This is evident in the different geometries and sample dimensions found in the analyzed papers. Moreover, only seldom information on setting parameters of load and displacement used during the experiments are provided together with a detailed description of the acquisition method. This makes almost impossible to repeat the test to compare accurately the results. (iv) The density remains still a parameter to study in more detail as for both SW and HW its variability per specie remains quite wide. Similarly, for the fracture toughness, that in addition should be estimated in all the different directions of the RTL plane. Looking at the MOE it is possible to find a certain agreement on E R and E T values for HW but not for SW that shows still the need of a deeper investigation to better evaluate the ER. Similarly, for the EL of both HW and SW that generally reports wide error bands. What is interesting is that E L seems to show a certain correlation with density. Finally, the DB related to the Poisson`s ratio highlights research works that provided mainly single values without an error band and mainly for SW. For future experimental research on this topic, it is necessary to find a standardization of the analysis processes in order to achieve more comparable results as regards the investigation techniques on wood. Standardized and detailed descriptions of the experimental procedure (including instrumental setup, load and displacement rate), geometry and dimensions of the samples should be mandatory would allow, through the use of existing mathematical formulas (A2), to calculate and compare both toughness G f and fracture toughness K IC although not the MOE and the density that have to be calculated experimentally. Recently another approach, underlined in this work in Tab. 5, is provided by the Finite Element Modelling analysis that is very promising but still need as input mechanical properties values. In conclusion both for an experimental or a modelling approach a standardized method of analysis could guarantee a homogeneity of data for the creation of well-organized database. T been by far the most important wood for aircraft construction and other specialty uses are ladder rails and sounding boards for pianos. : critical load value S: centre of the span Sg: specific gravity (relative density). Ratio between the density of an object and a standard (generally water H2O). uB, uB΄, uC, uC΄: node displacements in the "x" direction v B , v B΄ , v C , v C΄ : node displacements in the "y" direction W [mm]: distance from the loading line to the end of the specimen w [mm]: width of the specimen YI (or F(a/W)): shape factor ζ I : correction factor = 2810 σ C : critical stress

APPENDIX -SECTION 2
The equations above are mentioned with their specific number in Tab. 5. They are associated to the papers whose references are shown in Tab. 2.