ASTM D2915-2010 Practice for Sampling and Data-Analysis for Structural Wood and Wood-Based Products《结构木料的等级的允许特性评定的标准操作规程》.pdf

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1、Designation: D2915 10Standard Practice forSampling and Data-Analysis for Structural Wood and Wood-Based Products1This standard is issued under the fixed designation D2915; the number immediately following the designation indicates the year oforiginal adoption or, in the case of revision, the year of

2、 last revision. A number in parentheses indicates the year of last reapproval. Asuperscript epsilon () indicates an editorial change since the last revision or reapproval.INTRODUCTIONSampling and data analysis should be integrated in the design and evaluation of wood andwood-based structural product

3、s. This practice is useful in assessing the appropriateness of the assignedproperties and for checking the effectiveness of grading procedures. Statistical methodologies areprovided to serve as a basis for the empirical establishment and evaluation of mean and near minimumproperty estimates. These p

4、opulation estimates are then used by product standards to assign structuraldesign values for use with an established design methodology (that is, allowable stress design, loadand resistance factor design, limit states design, etc.). Near-minimum property estimates are typicallyused by the product st

5、andards to define the performance for a variety of structural properties wherestrength is a primary consideration (that is, extreme fiber stress in bending, axial tension, axialcompression, shear, and elasticity for buckling concerns). Population mean estimates are often usedto assess serviceability

6、 design criteria where strength is not the primary design concern (that is,elasticity estimates used for deformation calculations, permissible compression stress at a deforma-tion, etc.).For situations where a manufactured product is sampled repeatedly or lot sizes are small, alternativetest methods

7、 as described in Ref (1)2may be more applicable.1. Scope1.1 This practice covers sampling and analysis proceduresfor the investigation of specified populations of wood andwood-based structural products referred to in this standard asproducts. Appropriate product standards should be referencedfor pre

8、sentation requirements for data. Depending on theinterest of the user, the population from which samples aretaken may range from the products produced at a specificmanufacturing site to all the products produced in a particulargrade from a particular geographic area, during some specifiedinterval of

9、 time. This practice generally assumes that thepopulation is sufficiently large so that, for sampling purposes,it may be considered infinite. Where this assumption isinadequate, that is, the population is assumed finite, many ofthe provisions of this practice may be employed but thesampling and anal

10、ysis procedure must be designed to reflect afinite population. The statistical techniques embodied in thispractice provide procedures to summarize data so that logicaljudgments can be made. This practice does not specify theaction to be taken after the results have been analyzed. Theaction to be tak

11、en depends on the particular requirements of theuser of the product.1.2 The values stated in inch-pound units are to be regardedas the standard.1.3 This practice does not purport to address the adjustmentfactors needed to adjust test data to standardized mechanicaland environmental conditions (that

12、is, temperature, moisture,test span, or load duration).Additionally, it provides a basis forstatistical estimates that will typically require further adjust-ment to determine design values for use with an accepteddesign methodology (that is, allowable stress, limit states, orload and resistance fact

13、or design). It shall be the responsibilityof the user to seek out the appropriate adjustments in specificproduct standards.1This practice is under the jurisdiction of ASTM Committee D07 on Wood andis the direct responsibility of Subcommittee D07.02 on Lumber and EngineeredWood Products.Current editi

14、on approved Nov. 1, 2010. Published January 2011. Originallyapproved in 1970 as D2915 70 T. Last previous edition approved in 2003 asD2915 03. DOI: 10.1520/D2915-10.2The boldface numbers in parentheses refer to the list of references at the end ofthis practice.1Copyright ASTM International, 100 Barr

15、 Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States.1.4 This practice does not purport to address all of thesafety concerns, if any, associated with its use. It is theresponsibility of the user of this standard to establish appro-priate safety and health practices and determi

16、ne the applica-bility of regulatory limitations prior to use.2. Referenced Documents2.1 ASTM Standards:3D9 Terminology Relating to Wood and Wood-Based Prod-uctsD198 Test Methods of Static Tests of Lumber in StructuralSizesD245 Practice for Establishing Structural Grades and Re-lated Allowable Proper

17、ties for Visually Graded LumberD1990 Practice for Establishing Allowable Properties forVisually-Graded Dimension Lumber from In-Grade Testsof Full-Size SpecimensD2555 Practice for Establishing Clear Wood Strength Val-uesD3737 Practice for Establishing Allowable Properties forStructural Glued Laminat

18、ed Timber (Glulam)D5055 Specification for Establishing and Monitoring Struc-tural Capacities of Prefabricated Wood I-JoistsD5456 Specification for Evaluation of Structural CompositeLumber ProductsD6570 Practice for Assigning Allowable Properties forMechanically Graded LumberE29 Practice for Using Si

19、gnificant Digits in Test Data toDetermine Conformance with SpecificationsE105 Practice for Probability Sampling of Materials3. Terminology3.1 DefinitionsFor definitions of terms related to wood,refer to Terminology D9.3.2 Definitions of Terms Specific to This Standard:3.2.1 established design method

20、ology, nmethodologyused to determine if a structure will perform adequately usingstructural design values.3.2.2 DiscussionEstablished design methods currentlyused include allowable stress design, load and resistance factordesign, limit states design.3.2.3 products, nwood and wood-based structural pr

21、od-ucts.3.2.4 serviceability, ncondition other than the buildingstrength under which a building is still considered useful.3.2.5 DiscussionServiceability limit state design of struc-tures includes factors such as durability, overall stability, fireresistance, deflection, cracking, and excessive vibr

22、ation.3.2.6 strength, nlevel of stress expressed in terms of forceper area being evaluated for design.3.2.7 structural design values, nunit stresses and stiffnessvalues utilized in design.3.2.8 DiscussionStructural design values are test resultsadjusted for duration of load, factor of safety, and ex

23、pectedservice conditions.3.2.9 tolerance limit (TL), ntolerance limit with 95 %content and 75 % confidence.4. Statistical Methodology4.1 Two general analysis procedures are described underthis practice: parametric and nonparametric. A nonparametricapproach requires fewer assumptions and is generally

24、 moreconservative than a parametric procedure. The parametricapproach assumes a known distribution of the underlyingpopulation, an assumption which, if incorrect, may lead toinaccurate results. Some examples of parametric distributionsare normal, lognormal and Weibull. Therefore, if a parametricappr

25、oach is used, appropriate statistical tests shall be employedto substantiate this choice along with measures of test ad-equacy (3). For parametric approaches in this practice, theexamples provided are based on assuming normality.NOTE 1The assumption of “normality” in the examples is not a givenand s

26、hould be verified before using in real cases. A nonparametricapproach requires fewer assumptions and is generally more conservativethan a parametric procedure.4.2 Population:4.2.1 It is imperative that the population to be evaluated beclearly defined, as inferences made pertain only to that popu-lat

27、ion. In order to define the population, it may be necessary tospecify (1) grade name and description, (2) geographical areaover which sampling will take place (nation, state, manufac-turing site, etc.), (3) species or species group, (4) time span forsampling (a days production, a month, a year, etc.

28、), (5)material dimensions, and (6) moisture content.4.2.2 The sampling program should consider the populationfrom which the test specimens originated, including types ofprocessing methods or marketing practices with respect to anyinfluence they may have on the representative nature of thesample. Tes

29、t specimens may be collected from stock atmanufacturing sites, centers of distribution, at points of end useor directly from current production. Sampling programs shouldconsider potential effects of the sample source, timing, andlocation on the variability of specimen properties.4.3 Sampling Procedu

30、re:4.3.1 Random SamplingThe sampling unit is commonlythe individual test specimen. When this is not the case, see4.3.3. The sampling shall assure random selection of samplingunits from the population described in 4.2 with all members ofthe population sharing equal probability of selection. Theprinci

31、ples of Practice E105 shall be maintained. When sam-pling current production, refer to Practice E105 for a recom-mended sampling procedure (see Appendix X3 of this practicefor an example of this procedure). If samples are selected frominventory, random number tables may be used to determinewhich pie

32、ces will be taken for the sample.4.3.2 Sampling with Unequal ProbabilitiesUnder somecircumstances, it may be advisable to sample with unequal butknown probabilities. Where this is done, the general principlesof Practice E105 shall be maintained, and the sampling methodshall be completely reported.3F

33、or referenced ASTM standards, visit the ASTM website, www.astm.org, orcontact ASTM Customer Service at serviceastm.org. For Annual Book of ASTMStandards volume information, refer to the standards Document Summary page onthe ASTM website.D2915 1024.3.3 Sequential SamplingWhen trying to characterizeho

34、w a certain population may perform in a structure, it may bedeemed more appropriate to choose a sampling unit, such as apackage, that is more representative of how the product will beselected for use. Such a composite sampling unit might consistof a sequential series of pieces chosen to permit estim

35、ation ofthe properties of the unit as well as the pieces. Where this isdone, the principles in 4.3.1 and 4.3.2 apply to these compositesampling units and the sampling method shall be completelyreported.4.4 Sample Size:4.4.1 Selection of a sample size depends upon the propertyor properties to be esti

36、mated, the actual variation in propertiesoccurring in the population, and the precision with which theproperty is to be estimated. For any property, strength values,or the modulus of elasticity, various percentiles of the popula-tion may be estimated and for all properties, nonparametric orparametri

37、c techniques are applicable. Commonly, the mean isestimated for properties which will eventually be used by theproduct standard to evaluate a serviceability design concern.Near minimum property estimates are typically evaluated forproperties where strength is the primary objective.4.4.2 Determine sa

38、mple size sufficient for estimating themean by a two-stage method, with the use of the followingequation. This equation assumes the data is normally distrib-uted and the mean is to be estimated to within 5 % withspecified confidence:n 5StsaXD25StaCVD2(1)where:n = sample size,s = standard deviation o

39、f specimen values,X= specimen mean value,CV =coefficient of variation, s/X,a = estimate of precision, (0.05), andt = value of the t statistic from Table 1.Often, the values of s, X, and t or CV and t are not knownbefore the testing program begins. However, s and X,orCV,may be approximated by using t

40、he results of some other testprogram, or they may simply be guessed.NOTE 2An example of initial sample size calculation is:Sampling a grade of lumber to determine its mean modulus of elasticity(E).Assuming a 95 % confidence level, the t statistic can be approximatedby 2.s = 300 000 psi (2067 MPa)X=

41、assigned E of the grade = 1 800 000 psi (12 402 MPa)CV = (300 000/1 800 000) = 0.167t =2n =S20.053 0.167D25 44.622 45 pieces!Calculate the sample mean and standard deviation and use them toestimate a new sample size from Eq 1, where the value of t is taken fromTable 1. If the second sample size exce

42、eds the first, the first sample wasinsufficient; obtain and test the additional specimens.NOTE 3More details of this two-stage method are given in Ref (5).4.4.3 Tolerance intervals and their associated tolerancelimits can be one-sided or two-sided. In the examples of thisstandard, it is assumed that

43、 the limits are one-sided lowerlimits. To determine sample size based on a tolerance limit(TL), the desired content (C) and associated confidence levelmust be selected (Note 4). The choice of a specified contentand confidence is dependent upon the end-use of the material,economic considerations, cur

44、rent design practices, code re-quirements, etc. For example, a content of 95 % and aconfidence level of 75 % may be appropriate for a specificproperty of structural lumber. Different confidence levels maybe suitable for different products or specific end uses. Appro-priate content and confidence lev

45、els shall be selected before thesampling plan is designed.NOTE 4The content is an estimate of the proportion of the populationthat lies above the tolerance limit. For example, a tolerance limit with acontent of 95 % describes a level at which 95 % of the population liesabove the tolerance limit. The

46、 confidence level is the percentage of timethat the desired content is expected to be achieved through sampling.4.4.3.1 To determine the sample size for near-minimumproperties, the nonparametric tolerance limit concept of Ref (5)may be used (Table 2). This will provide the sample sizesuitable for se

47、veral options in subsequent near-minimumanalyses. Although the frequency with which the tolerancelimit will fall above (or below) the population value, corre-sponding to the required content, is controlled by the confi-dence level selected, the larger the sample size the more likelyTABLE 1 Values of

48、 the t Statistics Used in CalculatingConfidence IntervalsAdfn 1CI =75% CI =95% CI =99%1 2.414 12.706 63.6572 1.604 4.303 9.9253 1.423 3.182 5.8414 1.344 2.776 4.6045 1.301 2.571 4.0326 1.273 2.447 3.7077 1.254 2.365 3.4998 1.240 2.306 3.3559 1.230 2.262 3.25010 1.221 2.228 3.16911 1.214 2.201 3.1061

49、2 1.209 2.179 3.05513 1.204 2.160 3.01214 1.200 2.145 2.97715 1.197 2.131 2.94716 1.194 2.120 2.92117 1.191 2.110 2.89818 1.189 2.101 2.87819 1.187 2.093 2.86120 1.185 2.086 2.84521 1.183 2.080 2.83122 1.182 2.074 2.89123 1.180 2.069 2.80724 1.179 2.064 2.79725 1.178 2.060 2.78726 1.177 2.056 2.77927 1.176 2.052 2.77128 1.175 2.048 2.76329 1.174 2.045 2.75630 1.173 2.042 2.75040 1.167 2.021 2.70460 1.162 2.000 2.660120 1.156 1.980 2.617 1.150 1.960 2.576AAdapted from Ref (5). For calculating other confidence levels, see Ref (5).D2915 103the tolerance li