Study On The Strength Properties Of Larch Dimension Lumber

Chinese wood construction developed until the early 21st century. Study on wood construction and materials is still very weak. National standard specifications of mechanical properties of dimension lumber characteristic value and design reference value is calculated from falling from a small clear sample test values. For security purposes, the actual structural design calculations, to intensity multiplied by a factor of safety less than 1. The result is too much waste of material. The aim of the study is to develop specifications for the static strength testing standards and evaluation system of dimension lumber, promote application Larch to building structure and increasing the added value of Larch dimension lumber.According to American Society for Testing and Material Standards (ASTM) D4761-05, MOE, MOR, UTS and UCS of three sizes (40mm×65mm×4000mm, 40mm×90mm×4000mm, 40mm×140mm×4000mm) and four grades, a total of 4927 dimension lumber were tested. Base on testing results, the characteristic values of MOE, MOR, UTS and UCS were established using Non- parametric and parametric method.The goodness of fit of mechanical properties fitting to normal, lognormal and Weibull distribution are compared. Based on the weakest chain theory, statistical model for the relationship between strength and size of dimension lumber was established. Relative error analysis was used to estimate model precision. Monte Carlo technique was used to simulate sampling scheme to establishing the distribution of dimension lumber strength and evaluated the relative merits of various sampling scheme in order to predict strength distribution.The results showed that:1. Characteristic values of MOE, MOR, UTS and UCS estimated by Non- parametric method: Characteristic values of MOE for 40mm×65mm dimension lumber gradeⅠc,Ⅱc,Ⅲc andⅣc are 15792MPa, 14181MPa, 13746 MPa and 13309 MPa respectively, for 40mm×90mm dimension lumber gradeⅠc,Ⅱc,Ⅲc andⅣc are 14122MPa, 12114MPa, 12545MPa and 12197MPa, for 40mm×140mm dimension lumber gradeⅠc,Ⅱc,Ⅲc andⅣc are 14814MPa, 12951MPa, 14407MPa and 14974MPa. Characteristic values of MOR for 40mm×65mm dimension lumber gradeⅠc,Ⅱc,Ⅲc andⅣc are 22.34MPa, 16.24MPa, 16.66 MPa and 17.33 MPa, for 40mm×90mm dimension lumber gradeⅠc,Ⅱc,Ⅲc andⅣc grades are 24.72 MPa, 18.60 MPa, 17.59 MPa and 13.14 MPa, for 40mm×140mm dimension lumber gradeⅠc,Ⅱc,Ⅲc andⅣc are 22.82MPa, 20.62MPa and 16.03MPa. Characteristic values of UTS for 40mm×65mm dimension lumber gradeⅠc,Ⅱc,Ⅲc andⅣc are 14.53MPa, 9.93MPa, 10.65MPa and 8.29MPa, for 40mm×90mm dimension lumber gradeⅠc,Ⅱc,Ⅲc andⅣc are 16.14MPa, 13.03MPa, 11.43MPa and 16.63MPa, for 40mm×140mm dimension lumber gradeⅠc,Ⅱc,Ⅲc andⅣc are 14.83 MPa, 10.36 MPa, 8.19 MPa. Characteristic values of UCS for 40mm×65mm dimension lumber gradeⅠc,Ⅱc,Ⅲc andⅣc are 23.61 MPa, 24.81 MPa, 23.28 MPa and 17.60 MPa, for 40mm×90mm dimension lumber gradeⅠc,Ⅱc,Ⅲc andⅣc grades are 28.04 MPa, 22.35 MPa, 22.27 MPa and 18.08MPa, for 40mm×140mm dimension lumber gradeⅠc,Ⅱc,Ⅲc andⅣc are 26.24 MPa, 21.23 MPa, 15.72 MPa.2. The goodness fit of MOR data: 40mm×65mm and 40mm×90mm gradeⅠc has the best goodness fit to two-parameter weibull distribution. GradeⅡc,Ⅲc andⅣc have the best goodness fit to lognormal distribution. 40mm×140mm, gradeⅠc,Ⅲc andⅣc have the best goodness fit to lognormal distribution. The goodness fit of UTS data: have the best goodness fit to lognormal distribution except for 40mm×65mm gradeⅠc. The goodness fit of UCS data: 40mm×65mm,Ⅰc grade has the best goodness fit to normal distribution,Ⅱc,Ⅲc andⅣc grades have the best goodness fit to lognormal distribution. 40mm×90mm gradeⅡc has the best goodness fit to two-parameter weibull distribution; gradeⅢc andⅣc have the best goodness fit to lognormal distribution. 40mm×140mm gradeⅠc, andⅢc have the best goodness fit to lognormal distribution, gradeⅣc has the best goodness fit to two-parameter weibull distribution.3. SRb (size parameter to characterize strength variation due to length and width of members using a fixed test length to depth ratio) of gradeⅠc for MOR is 0.43. SRb of gradeⅢc for MOR is linear related to strength level, SRb=0.27-0.09PMOR. SWt(size parameter to characterize strength variation due to width ) of gradeⅠc for UTS is 0.33, SWt of gradeⅢc is linear related to strength level, SWt=-0.36PUTS+0.23. SAc(size parameter to characterize strength variation due to aspect area) of gradeⅠc for UCS is linear related to strength level, SAc=0.15-0.06 PUCS, SAc of gradeⅢc for UCS is 0.06. 4. MOE is linear related to MOR. Coefficient of determination is 0.46, MOR=0.0039MOE-10.85. Relationship between UTS and MOR can be described by power-type model, UTS=0.34MOR1.16. While MOR is smaller than 48.3MPa, the ratio of tension to bending is 0.63. UCS vs. MOR and MOR vs. UCS relationships regression equations are not inversely related, such as UCS vs. UTS and UTS vs. UCS. To predict using this approach are independent of the choice of the initial variable. Relationship between UCS and MOR can be described by UCS=5.14MOR0.54, MOR=1.66UCS0.09. The relationship between UCS and UTS can be described by UCS=8.33UTS0.48, UTS=0.02UCS1.95.5. Sampling error of three populations (T: 40mm×65mm, 40mm×90mm, 40mm×140mm data of single strength property; A: data of 40mm×90mm dimension lumber single strength property; C: data of 40mm×90mm gradeⅠc dimension lumber single strength property) at 50%, 75%, 95% confidence levels decrease to a constant with sample size increases. On the premise of that the strehgth properties obey the Weibull distribution, the regression equations of the relationship between error and SQRT (1/N) is established. Sample size to estimate characteristic value for three populations are MOR: 142, 127 and 88; UTS: 158, 183 and 104; UCS: 57, 60 and 29.