Ceccotti, Ario

Kuilen van de, J. W. G.

Moschi, M.

Togni, Marco

English

Florence Research

Strength data of Italian red spruce (Picea abies)J.W.G. van de Kuilen1,2, M. Togni3, M. Moschi1, A. Ceccotti11CNR-Ivalsa, San Michele all'Adige, Italy,2Delft University of Technology, Delft, the Netherlands3University of Florence, Florence, ItalySummaryStrength data of Italian grown spruce that has been gathered during various research projectsover the last 13 years have been combined into one large dataset. The data has been analysedfor mechanical properties such as bending strength, modulus of elasticity as well as density.Visual grades have been determined using the German and Italian standards and wereanalysed for their strength profiles. Normal, Lognormal and Weibull distributions have beendetermined. The use of ultrasound grading as an additional parameter has been tried, but wasfound not very useful in improving the yield in higher strength classes.AcknowledgementThe Autonomous Province of Trentino is greatly acknowledged for making this workpossible. In addition, Mario Pinna from the mechanical testing laboratory of CNR Ivalsa inSan Michele all'Adige is greatfully acknowledged for his continuous support and help in allthe testing that has been done in recent years.IntroductionThe Val di Fiemme region in Trentino is one of the largest softwood producing areas of Italyfor use in structural applications. The whole province of Trentino produces around 400.000m3 of sawn timber each year, of which a large part is used structurally. In the last 15 yearsstrength data has been gathered in several research projects consisting of a variety of materialsizes, origins and qualities. The projects dealt with both visual and machine strength gradingusing ultrasound. The visual grading rules applied were in accordance with Italian standardswhich closely resemble the German visual grading rules for softwoods. Besides visual gradingall beams were also graded using ultrasound. Strength profiles have been determined inaccordance with European standards EN 384. Characteristic values and the strength profilesof the subsamples have been determined and the scatter in mean and characteristic values forbending strength, modulus of elasticity and density are analysed statistically. In addition, thewhole sample is analysed for the depth effect, which was found to comply well with EN 384.Ultrasound grading has been performed and the efficiency is analysed. The yield in higherstrength classes is determined. A combination of both visual and ultrasound grading improvedthe yield in high strength classes considerably. Boundaries for the ultrasound wave speedhave been determined. It was found that traditional strength profiles of EN 338 are notsatisfactory for the timber studied, and for use in combination with a design standard aspecific strength profile has been determined.2. Material and methodsThe Val di Fiemme region in Trentino is one of the largest softwood producing regions inItaly, with a production of more than 50.000 m3 each year. White and Red Spruce are themain species. Over the last fifteen years several projects have been performed to determinethe characteristic strength values of the timber. The samples from a number of these projectsare gathered in table 1.Due to the different research projects not all beams were graded and tested according to thesame principles. However, from all beams the visual classification according to DIN4074/UNI 11035 has been determined and a 'knot ratio' has been determined as knot sizedivided by the face dimension on which the knot is visible. Furthermore, density, local MoEand bending strength have been determined in the laboratory, including ultrasound wavespeed and dynamic MoE.Table 1. Samples of Italian spruce over 13 year span.SizesSample Year No.width depth length test length1 1991/1992 215 90 150 4000 27002 2003 45 45 70 1400 12603 2003 50 45 70 2000 12604 2003 45 70 110 2200 19805 2003 45 85 150 3000 27006 2004 45 90 150 2200 21007 2004 45 90 145 2200 21008 2004 42 90 145 2200 21009 2004 24 125 260 4000 380010 2004 78 75 200 4200 380011 2004 72 90 250 4200 380012 2005 48 60 150 3000 27003. Visual gradesItalian standard UNI 11035 is approximately similar to DIN 4074 with regard to the knotrequirements. Three grades and reject are considered, namely Grade S1, S2 and S3,corresponding to the German grades S13, S10, S7 and Reject.Table 2 Visual grades and yield for each subsample.SampleNo. width depth S1 S2 S3 Reject Wholen. 67 117 28 1 2151)1 90 150 % 31.2 54.4 13.0 0.4 100n. 10 17 11 7 452 45 70 % 22.2 37.8 24.4 15.6 100n. 10 11 24 5 503 45 70 % 20.0 22.0 48.0 10.0 100n. 15 20 6 4 454 70 110 % 33.3 44.4 13.3 8.9 100n. 15 24 4 2 455 85 150 % 33.3 53.3 8.9 4.4 100n. 9 26 9 0 446 90 150 % 20.4 59.2 20.4 0 100n. 3 25 17 0 457 90 145 % 6.7 55.5 37.7 0 100n. 8 14 15 0 378 90 145 % 21.6 37.8 40.5 0 100n. 4 12 4 3 239 120 260 % 18.2 54.5 17.3 13.6 100n. 3 21 23 25 7210 75 200 % 4.2 29.2 31.9 34.7 100n. 0 30 13 27 7111 90 250 % 0 42.8 18.5 38.6 100n. 10 17 16 5 4812 60 150 % 20.8 35.4 33.3 10.4 100The German grades have been assigned to strength classes C30, C24, C16 of EN 338respectively, wheras for the Italian grades specific profiles have been set up in the nationalstandard [UNI 11035-2]. The typical characteristic density values for strength classes C30,C24 and C16 are 380, 350, and 310 kg/m3. Mean values are approximately 20% higher. TheItalian grades S1, S2 and S3 are assigned to EN 338 strength classes C27, C22 and C16respectively, having characteristic density values of 370, 340 and 310 kg/m3. Thecharacteristic density of the samples are gathered in Annex A show that in a number of casesthe minimum requirements are not met. This is especially the case for strength class C30.4. Test resultsThe data for density, static modulus of elasticity and bending strength have been gathered foreach of the subsamples in Annex A, B and C respectively. If all samples are regarded as one,the dataset contains more than 750 test results. Correlation coefficients have been determinedfor this basic data as well as the graded data and are gathered in Table 3 and 4 respectively.Table 3. Basic properties of ungraded spruceProperty Density Modulus ofElasticity Bending strengthAverage 420.0 10131 39.1Standarddeviation 30.6 2141 12.9Table 4. Basic properties for graded spruceDensitykg/m3Modulus of ElasticityN/mm2Bending strengthN/mm2Grade n % Average St.dev. Average St.dev Average St.devS1 155 10.8 420.1 31.6 11315 1864 49.5 11.6S2 355 21.0 420.5 31.6 10207 1966 38.8 11.2S3 168 45.4 422.9 29.2 9375 2139 33.8 12.3R 80 22.8 411.9 26.5 9085 2196 31.5 10.1The low correlation coefficient between density and bending strength (R2 = 0.09) indicatesthat visual grading may have an effect on the bending strength and modulus of elasticity, buthardly or no effect whatsoever on the density values. This influences the strength classassignment considerably. In table 5 the correlation coefficients between the differentparameters are given.The relationship between the knot ratio (KAR), calculated as minimum knot diameter dividedby the face dimension on which the knot is visible, is show in Figure 1. The existence of adepth effect has been studied by fitting a power- equation to the basic data. Therefore, allTable 5. Correlation coefficientsProperty Density Modulus ofElasticityBendingstrengthKARDensity 1Modulus ofElasticity 0.43 1Bendingstrength 0.30 0.71 1KAR -0.03 -0.47 -0.52 1data-values have been divided by the average bending strength and the ratio has been plottedagainst the depth, see Figure 2. The depth effect could be described using the followingrelationship:2425.0150=hkh (1) 5. Frequency distributionsand characteristic valuesFor the three grades (dataadjusted for the deptheffect) as well as for the full(ungraded) dataset, thedistribution functions havebeen determined. In allcases it was found thatWeibull and Normaldistributions described thedata better than log-normaldistributions. the parametersof the distributions havebeen gathered in Table 6. InFigure 3 the data and thedistributions are shown. Thecumulative frequencydistributions of the threegrades are shown in Figure4.The non-parametriccharacteristic strengthvalues have beendetermined for the threedifferent grades as well asfor the ungraded material,see Table 7. In addition, thecharacteristic values usingthe three distributions arealso given. From the lowertail shown in Figure 3 it canbe concluded that Normaland Lognormal give anunderprediction of the 5-thpercentile value of the strength data whereas the Lognormal distribution gives anoverprediction.R2 = 0.26901020304050607080901000.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00Bending strength (N/mm2 )Figure 1. Relationship between KAR and bending strength0.000.200.400.600.801.001.201.401.601.802.000 50 100 150 200 250 300Depth (mm)Relative bending strength (N/mm2)Figure 2. Depth effect for the full datasetTable 6. Distribution parameters of spruce of Val di FiemmeNormal Lognormal Weibull (2-parameter)Dataset m s m s m vAll data 38.96 12.09 3.633 0.339 42.987 3.421Grade S1 48.17 10.97 3.844 0.262 52.265 5.127Grade S2 38.25 10.11 3.628 0.283 41.884 4.091Grade S3 32.47 10.98 3.433 0.359 35.531 3.204Table 7. Characteristic values of the bending strengthDataset Normal Lognormal Weibull Non-parametricAll data 18.98 21.65 18.04 20.27Grade S1 30.10 30.34 29.28 27.62Grade S2 21.61 23.63 20.26 21.97Grade S3 14.42 17.16 14.06 16.22Whereas in most cases the Non-parametric value is in between the values of the threedistributions, this is not the case for Grade S1. A lower value of close to 8%  is observed inthis case. The non-parametric values indicate that the grades exactly fulfil the requirementsfor C27, C22 and C16 strength classes.With regard to the modulus of elasticity it may be concluded that not all the requirements forthe strength classes C27, C22 and C16 have been met. The requirements are 11500, 10000and 8000 respectively and from the test results it follows that for the S1 grade the averagemodulus of elasticity is 11315 and for the S2 and S3 grade 10200 and 9375 respectively. Sothe S1 grade fails the modulus of elasticity by 1.7%. The characteristic density values for thethree classes are 368, 368 and 375 kg/m3 for S1, S2 and S3 respectively. Officially, also herethe S1 grade fails the requirement, but the difference is extremely small. (368 versus 370required) . It can be concluded that all requirements for the strength classes have been met,01020304050607080901000 10 20 30 40 50 60 70 80 90Bending strength (N/mm2)Percent (%)0510152025300 5 10 15 20 25 30Bending strength (N/mm2)Data (n= 752)Normal dist.Lognormal dist.Weibull dist.Figure 3. Ungraded data of spruce of Val di Fiemme. Left: full distribution, Right: lowertaleexcept for the modulus of elasticity for the S1 grade, which is slightly to low. Thecharacteristic modulus of elasticity parallel to the grain however is 8240 based on a standardnormal distribution, whereas EN 384 specifies a value of 0.67E0,mean = 7580 N/mm2.6. UltrasoundmeasurementsAll beams have beenmeasured with theSylvatest strength gradingmachine. The ultrasonicwave velocity has beendetermined and on thatbasis the ultrasoundmodulus of elasticity. Thecorrelation with thebending strength is shownin Figure 5 and thecorrelation betweendynamic modulus ofelasticity and staticmodulus of elasticity isshown in Figure 6.Correlation betweensquared ultrasound speed and bending strength was slightly better than ultrasound velocitywith R2 = 0.325 versus R2 = 0.317. This has previously also been found by Ceccotti et al. [1].With these relatively low coefficients of determination there seems to be no benefit inapplying a grading machine based on ultrasound in the industry. Multiple regression analysisusing MoEstatic and knot parameter gives the following results:847.10563.1810643.3 3 +⋅−⋅= − KARMoEf statm  with R2 = 0.572 and shown in Figure 7.or:01020304050607080901000 10 20 30 40 50 60 70 80 90Bending strength (N/mm2)Percentage (%)Grade S1Grade S2Grade S3Figure 4. Cumulative frequency distributions of Grades S1, S2 and S3y = 0.0039x - 8.5232R2 = 0.33970.0010.0020.0030.0040.0050.0060.0070.0080.0090.00100.000 5000 10000 15000 20000MoE-ultrasound (N/mm2)Bending strength (N/mm2 )Figure 5. Relationship between MoE determined usingultrasound and the bending strength327.9766.3310332.3 3 +⋅−⋅= − KARMoEf ultrasoundm with R2 = 0.505 when using MoEultrasoundThe use of density as a strength determining parameter does not give an improved prediction.7. ConclusionsA dataset of Italian red spruce has been created containing on the basis of more than 700bending tests on visually graded material. Distribution function parameters have beendetermined which may be used for the derivation of characteristic values and for probabilisticcalculations.Literature[1] Ceccotti, A., Nakia, T., Togni, M., Strength grading of structural timber by non-destructive methods: a case study in Italy. Proc. First European Symposium onNondestructive Evaluation of Wood, Vol. 2, September 21-23, 1994, Sopron, Hungary,Annex A. Density values for subsamples 1 to 12.y = 0.9036x - 1537.3R2 = 0.61950200040006000800010000120001400016000180000 5000 10000 15000 20000MoE-static (N/mm2)MoE-ultrasound (N/mm2)Figure 6. Relationship between static MoE andultrasonic MoEFigure 7. Multiple regression analysisy = 1.0004x + 0.0071R2 = 0.57240.0010.0020.0030.0040.0050.0060.0070.0080.0090.00100.000 20 40 60 80Predicted strength (MoE-static + KAR) (N/mm2)Actual bending strength (N/mm2SampleNo. width depthS1 S2 S3 Reject Wholen. 67 117 28 1 2151)1 90 150 ρkρmean381.7421.5373.3420.5381.0416.5 [-]376.9420.2n. 10 17 11 7 452 45 70 ρkρmean351.5407.0397.3433.5403.8440.9389.9417.4382.4426.9n. 10 11 24 5 503 45 70 ρkρmean377.6405.8343.8416.1383.5417.5384.6414.6371.2414.6n. 15 20 6 4 454 70 110 ρkρmean374.2419.1375.4416.3363.2410.7395.5403.0374.1415.3n. 15 24 4 2 455 85 150 ρkρmean379.0408.3386.1421.4372.1407.8392.4404.0381.2415.0n. 9 26 9 0 446 90 150 ρkρmean340.2398.7350.1408.7392.4410.8 [-]354.8407.1n. 3 25 17 0 457 90 145 ρkρmean318.9368.4343.1393.9361.4424.6 [-]342.2403.8n. 8 14 15 0 378 90 145 ρkρmean337.8424.9367.0413.6362.6417.6 [-]358.7417.6n. 4 12 4 3 239 120 260 ρkρmean347.9399.1370.7420.9361.8416.0360.4430.4364.4416.7n. 3 21 23 25 7210 75 200 ρkρmean356.1385.2334.8408.4388.7430.8355.0406.7358.3416.7n. 0 30 13 27 7111 90 250 ρkρmean[-] 372.3405.1369.4404.3368.0413.2369.6408.2n. 10 17 16 5 4812 60 150 ρkρmean374.0422.8380.7422.7374.8424.5362.6409.6376.3422.91) Data given differs slightly for those presented by Ceccotti et al. [1994].  The difference iscaused by the fact that here the basic data is reported wheras Ceccotti et al. reported the dataof 192 beams that failed inside the loading points. Differences however are small and do notaffect the conclusions drawn here.Annex B. Local Modulus of elasticity for subsamples 1 to 12.SampleNo. width depthS1 S2 S3 Reject Wholen. 67 117 28 1 2151 90 150 Emeansd.12064139510733176494101509 [-]109761843n. 10 17 11 7 452 45 70 Emeansd.10727287212465195412687226373242671113332968n. 10 11 24 5 503 45 70 Emeansd.10703225190071771872620736275142289432270n. 15 20 6 4 454 70 110 Emeansd.109551492990714749534162180482082100411762n. 15 24 4 2 455 85 150 Emeansd.102351530876012407604697 [-]91891520n. 9 26 9 0 446 90 150 Emeansd.11175206210847154397331951 [-]106901767n. 3 25 17 0 457 90 145 Emeansd.88776398066172277501273 [-]80011519n. 8 14 15 0 378 90 145 Emeansd.1034226609412189387701770 [-]93472217n. 4 12 4 3 239 120 260 Emeansd.1132288098932320814210968645250897432138n. 3 21 23 25 7210 75 200 Emeansd.110451180106211827107391706102781666107091746n. 0 30 13 27 7111 90 250 Emeansd. [-]99761407867810209408197095121654n. 10 17 16 5 4812 60 150 Emeansd.10675221710061223292582354775432296812266Annex C. Bending strength values for subsamples 1 to 12.SampleNo. width depthS1 S2 S3 Reject Wholen. 67 117 28 1 2151 90 150 fmeansd.53.78.642.38.635.37.7 [-]44.510.6n. 10 17 11 7 452 45 70 fmeansd.57.9117.7158.2613.4250.8517.6930.899.8452.1117.56n. 10 11 24 5 503 45 70 fmeansd.53.0911.4142.8810.3939.3811.2028.517.9741.8512.56n. 15 20 6 4 454 70 110 fmeansd.48.8811.2142.9210.5534.019.9232.7513.3542.8111.98n. 15 24 4 2 455 85 150 fmeansd.39.379.4832.899.0425.557.03 [-]34.749.90n. 9 26 9 0 446 90 150 fmeansd.41.7811.2937.587.5832.847.01 [-]37.478.62n. 3 25 17 0 457 90 145 fmeansd.41.472.9228.177.7824.306.55 [-]27.598.16n. 8 14 15 0 378 90 145 fmeansd.37.867.8832.329.0427.718.26 [-]31.219.12n. 4 12 4 3 239 120 260 fmeansd.35.757.5730.558.0722.567.0823.505.6929.428.52n. 3 21 23 25 7210 75 200 fmeansd.45.6711.9639.4312.8240.338.9737.029.8239.8610.98n. 0 30 13 27 7111 90 250 fmeansd. [-]33.568.8524.277.8528.838.3029.989.03n. 10 17 16 5 4812 60 150 fmeansd.51.3810.7836.458.3631.9712.4536.6313.4337.9513.17

Strength data of Italian Red spruce (Picea abies)

https://flore.unifi.it/bitstream/2158/242352/1/coste24-bordeaux-vdKuilen-Togni-Moschi-Ceccotti.pdf

Strength data of Italian Red spruce (Picea abies)

Abstract

Similar works

Full text

Available Versions

Florence Research