Development and Calibration of AASHTO LRFD Specifications for Structural Supports for Highway Signs, Luminaires, and Traffic Signals (2014) / Chapter Skim
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A-1 Calibration Report CONTENTS Section 1. Introduction .................................................................
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A-2 Section 7. Reliability Analysis .....................................................
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A-3 Appendix Context The research for NCHRP Project 10-80 required several integrally linked activities: • Assessment of existing literature and specifications, • Organization and rewriting the LRFD-LTS specifications, • Calibration of the load and resistance factors, and • Development of comprehensive examples illustrating the application of LRFD-LTS specifications� The purpose of this appendix is to provide the details regarding the calibration process and results� This appendix is intended for those who are especially interested in the details of the process� The draft LRFD-LTS specifications are being published by AASHTO� In addition, Appendix C provides a series of example problems that illustrate the application of the LRFD-LTS specifications� Scope The LRFD-LTS specifications consider the loads for design presented in Table 1-1� The combinations considered are based on either judgment or experience and are illustrated in Table 1-2� The proposed load factors are shown� The Strength I limit state for dead load only (Comb� 1) was calibrated� The Strength I limit state for dead load and live load was considered a minor case and may control only for components that support personnel servicing the traffic devices (Comb� 2)
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A-4 model is provided in Section 5� Sections 2 to 5 provide the necessary prerequisite information for conducting the reliability analysis in Section 7 and calibrating the strength limit state� Section 8 illustrates the implementation of the reliability analysis for the specifications� Section 6 addresses the reliability analysis for the fatigue limit state for high-mast luminaires� This section may be skipped if the reader is only interested in the strength limit state� Finally, the calibration is summarized in Section 9� Annexes are provided for a variety of data used in this study� Table 1-1. LRFD-LTS loads.
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A-5 Dead Load Parameters Dead load (DC) is the weight of structural and permanently attached nonstructural components� Variation in the dead load, which affects statistical parameters of resistance, is caused by variation of the gravity weight of materials (concrete and steel)
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A-6 statistics are required to determine appropriate probabilitybased load and load combination factors� The cumulative distribution function of wind speed is particularly significant because V is squared� However, the uncertainties in the other variables also contribute to the uncertainty in Pz� The CDFs for the random variables used to derive the wind load criteria that appear in ASCE/SEI 7-10 are summarized in Table 2-2 (Ellingwood, 1981) � Development of Statistical Parameters for Wind Speed The statistical parameters of load components are necessary to develop load factors and conduct reliability analysis� The shape of the CDF is an indication of the type of distribution� For non-hurricane regions, measured gust data were fit using a Fisher-Tippett Type I extreme value distribution (Peterka and Shahid, 1998)
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Figure 2-1. CDFs for annual and MRI 300, 700, and 1,700 years, for Baltimore, Maryland.
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A-8 Figure 2-5. CDFs for annual and MRI 300, 700, and 1,700 years, for St.
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A-9 The most important parameters are the mean, bias factor, and the coefficient of variation� The bias factor is the ratio of mean to nominal� Mean values were taken as an extreme peak gust wind speed from the literature (Vickery et al�, 2010) � Bias factors were calculated as follows: 50 50 50 300 300 300 700 700 700 1700 1700 1700V V V V λ = µ λ = µ λ = µ λ = µ where: m50, m300, m700, m1700 = are wind speeds with MRI = 50 years, 300 years, 700 years, and 1,700 years, respectively, taken from maps included in literature (Vickery et al�, 2010)
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A-10 S E c t I o N 3 Information from ASCE/SEI 7-10 and Available Literature Atmospheric ice loads due to freezing rain, snow, and incloud icing have to be considered in the design of ice-sensitive structures� According to ASCE/SEI 7-10, the equivalent uniform radial thickness t of ice due to freezing rain for a 50-year mean recurrence interval is presented on maps in Figures 10-2 through 10-6 in ASCE/SEI 7-10� The 50-year MRI ice thicknesses shown in ASCE/SEI 7-10 are based on studies using an ice accretion model and local data� The historical weather data were collected from 540 National Weather Service, military, Federal Aviation Administration, and Environment Canada weather stations� The period of record of the meteorological data is typically 20 to 50 years� At each station, the maximum ice thickness and the maximum wind-on-ice load were determined for each storm� Based on maps in ASCE/SEI 7-10, the ice thickness zones in Table 3-1 can be defined� These ice thicknesses should be used for Risk Category II� For other categories, thickness should be multiplied by the MRI factor� For Risk Category I, it is required to use MRI = 25 years, and for Risk Category III and IV, it is required to use MRI = 100 years� The mean recurrence interval factors are listed in Table 3-2� Using the mean recurrence interval factor for each zone, the ice thicknesses for different MRIs were calculated and are presented in Table 3-3� In addition, ice accreted on structural members, components, and appurtenances increases the projected area of the structures exposed to wind� Wind load on this increased projected area should be used in design of ice-sensitive structures� Figures 10-2 through 10-6 in ASCE/SEI 7-10 include 3-s gust wind speeds that are concurrent with the ice loads due to freezing rain� Table 3-4 summarizes the 3-s gust for different localizations across the United States� As opposed to ice thickness, 3-s concurrent gust speed does not have a multiplication factor for different risk categories� The values on the map are the same for each risk category� The statistical parameters for 3-s concurrent gust speed can be taken as an average of statistical parameters of wind speed� Development of Statistical Parameters for Uniform Radial Ice Thickness The statistical parameters of load components are necessary to develop load factors and conduct reliability analy sis� The shape of the CDF is an indication of the type of distribution� Extreme ice thicknesses were determined from an extreme value analysis using the peak-over-threshold method and generalized Pareto distribution (GPD) (Hosking and Wallis, 1987, and Wang, 1991)
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A-11 able literature, the sample results of annual extremes were found� These data were plotted on normal probability paper to find the most important parameters, such as the mean, bias factor, and coefficient of variation� Bias factor is the ratio of the mean to nominal� The nominal value was taken from Table 3-3, depending on the zone and risk category� The first group of sample results was found in CRREL Report 96-2 (Jones, 1996) � The results include uniform equivalent radial ice thicknesses hind-cast for the 316 freezing-rain events in 45 years that occurred at Des Moines, Iowa, between 1948 and 1993 (see Figure 3-3)
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A-12 Table 3-4. 3-s gust speed concurrent with the ice loads.
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k = 0.10, α = 0.165, θ = 0.0 k = 0.10, α = 0.220, θ = 0.0 k = 0.10, α = 0.275, θ = 0.0 k = 0.10, α = 0.330 θ = 0.0 Figure 3-2. (Continued)
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A-14 Figure 3-4. Uniform radial ice thickness calculated using historical weather data-three station in Indiana, from the simple model.
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A-15 Figure 3-7. CDF of uniform radial ice thickness recorded at the Des Moines airport and simulation results for 100-year extremes.
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A-16 Figure 3-10. CDF of uniform radial ice thickness recorded at Grissom AFB and simulation results for 100-year extremes.
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A-17 Figure 3-13. CDF of uniform radial ice thickness recorded in Lafayette and simulation results for 100-year extremes.
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A-18 100 = 0.78 in.
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A-19 Information from ASCE/SEI 7-10 and Available Literature Ice accreted on structural members, components, and appurtenances increases the projected area of the structures exposed to wind� The projected area will be increased by adding t to all free edges of the projected area� Wind load on this increased projected area is to be applied in the design of ice-sensitive structures� Figures 10-2 through 10-6 in ASCE/ SEI 7-10 include the equivalent uniform radial thickness t of ice due to freezing rain for a 50-year MRI and 3-s gust wind speeds that are concurrent with the ice loads due to freezing rain� The amount of ice that accretes on a component is affected by the wind speed that accompanies the freezing rain� Wind speeds during freezing rain are typically moderate� However, the accreted ice may last for days or even weeks after the freezing rain ends, as long as the weather remains cold� Table 4-1 summarizes the 3-s gust for different locations across the United States� As opposed to ice thickness, 3-s concurrent gust speed does not have a multiplication factor for different risk categories� Values on the map are the same for each risk category� The statistical parameters for 3-s concurrent gust speed can be taken as an average of the statistical parameters of wind speed� It is often important to know the wind load on a structure both during a freezing-rain storm and for as long after the storm as ice remains on the structure� The projected area of the structure is larger because of the ice accretion, so at a given wind speed the wind load is greater than it could be on a bare structure� The wind load results are useful for identifying the combination of wind and ice in each event that causes the largest horizontal load� This combination is independent of drag coefficient as long as it can be assumed to be the same for both the pole-ice accretion and the icicle� Possible Combination of Uniform Radial Ice Thickness and Concurrent 3-s Gust Speeds Based on Figures 10-2 through 10-6 from ASCE/SEI 7-10, 24 different combinations of ice thickness and concurrent wind speed were identified� All possible combinations are marked in Table 4-2 as highlighted cells, as shown here: - Possible combination - Not foundThe response of traffic sign supports (given example) was calculated using a complex interaction equation for load combination that produces torsion, shear, flexure, and axial force [Equation C-H3-8, AISC Steel Construction Manual (AISC, 2010)
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A-20 calculated using the interaction equation [Equation C-H3-8, AISC Steel Construction Manual (AISC, 2010)
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A-21 Figure 4-1. Values of the interaction equation at the critical section as a function of wind speed on ice -- arm.
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A-22 Table 4-3. Values of response at the critical section on an arm calculated using interaction equation.
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A-23 Table 4-4. Values of response at the critical section on a pole calculated using interaction equation.
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steel (lb/ft3)
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A-25 Table 4-7. Example 1.
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A-26 S E c t I o N 5 Statistical Parameters of Resistance Load carrying capacity is a function of the nominal value of resistance, Rn, and three factors: material factor, m, representing material properties, fabrication factor, f, representing the dimensions and geometry, and professional factor, p, representing uncertainty in the analytical model: R R m f pn= i i i The statistical parameters for m, f, and p were considered by various researchers, and the results were summarized by Ellingwood et al� (1980) based on material test data available in the 1970s� The actual strength in the structure can differ from structure to structure, but these differences are included in the fabrication and professional bias factors (lf and lp)
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A-27 Parameters Cov Static yield strength, flanges 1.05 0.10 Static yield strength, webs 1.10 0.11 Young's modulus 1.00 0.06 Static yield strength in shear 1.11 0.10 Tensile strength of steel 1.10 0.11 Dimensions, f 1.00 0.05 Table 5-1. Statistical parameters for material and dimensions (Ellingwood et al., 1980)
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A-28 S E c t I o N 6 Background The previous AASHTO Standard Specifications for Structural Supports for Highway Signs, Luminaires, and Traffic Signals (AASHTO, 2009) requires for certain structures to be designed for fatigue to resist wind-induced stresses� Accurate load spectra for defining fatigue loadings are generally not available or are very limited� Assessment of stress fluctuations and the corresponding number of cycles for all wind-induced events (lifetime loading histogram)
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A-29 In addition, the statistical parameters are determined by fitting a straight line to the lower tail of the CDF. The most important parameters are the mean value, standard deviation, and coefficient of variation.
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A-30 Figure 6-2. Stress range versus number of cycles for Category A
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A-31 Figure 6-4. Stress range versus number of cycles for Category C
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A-32 Figure 6-7. Stress range versus number of cycles for Category E'.
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A-33 Figure 6-8. Stress range versus number of cycles for Category Et.
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A-34 Figure 6-10. Number of cycles at CAFL for Category B
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A-35 Figure 6-13. Number of cycles at CAFL for Category E
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Figure 6-14. Number of cycles at CAFL for Category E'.
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A-37 Figure 6-16. CDF for Category D
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A-38 Figure 6-17. CDF for Category E
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A-39 Figure 6-18. CDF for Category E'.
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A-40 The statistical parameters of resistance were developed in the previous section and load model is presented in NCHRP Report 718: Fatigue Loading and Design Methodology for High-Mast Lighting Towers. Resistance, R, demonstrates characteristics of normal distribution, and the basic statistical parameters, which are required for reliability analysis, were developed based on the straight line fitted to the lower tail� The load data provided in NCHRP Report 718 show very little variation� Moreover, even a coefficient of variation equal to 10% does not change the reliability index significantly� Distribution of fatigue resistance definitely has a dominant effect on the entire limit-state function� For special cases, such as a case of two normal-distributed, uncorrelated random variables, R and Q, the reliability index is given by: 2 2 R Q R Q β = µ − µ σ + σ To calculate the reliability index, the specific fatigue category and total load on the structure are used� The data presented in NCHRP Report 718 are summarized in Table 6-5� (Test site and stream gage abbreviations are as presented in NCHRP Report 718.)
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A-41 Table 6-5. Summary of load based on NCHRP Report 718.
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A-42 0 1 2 3 4 5 0 10 20 30 40 50 60 R el ia bi lit y In de x, β Years CA-A CH_3 CA-X CH_5 IAN-A (MT)
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01 2 3 4 5 6 7 8 0 10 20 30 40 50 60 R el ia bi lit y In de x, β Years KS-A CH_2 KS-X CH_6 Figure 6-23. Reliability index versus time for Category C, with truncation level > 1.0 ksi.
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A-44 0 1 2 3 4 5 0 10 20 30 40 50 60 R el ia bi lit y In de x, β Years IAS-A CH_2 IAS-X CH_1 ND-A CH_1 ND-X CH_5 E OKNE-X CH_5 OKSW-A CH_8 SD-A CH_6 SD-X CH_8 CJE-A (FR)
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A-45 LRFD Reliability Analysis -- Flexure The calibration between ASD and LRFD is based on the calibration of ASCE/SEI 7-05 50-year V50 wind speed and ASCE/ SEI 7-10 700-year V700 wind speed� The ASCE/SEI 7-10 wind speed maps for a 700-year wind are calibrated to the ASCE/ SEI 7-05 50-year wind speed where the difference between LRFD design wind load factors (ASCE/SEI 7-05 gW = 1�6 vs� ASCE/SEI 7-10 gW = 1�0) is equal to (V700/V50)
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A-46 To standardize the comparisons between ASD and LRFD, and for any specified year of wind, all analyses and comparisons are based on the total nominal moment for the LRFD 700-year total applied moment equal to 1�0: 1�07001M M MT D= + = and, the dead load moment can be represented by: 1 700M MD = − The calibration and comparison varies M700 from 1�0 to 0�0, while MD varies from 0�0 to 1�0 so that the total applied nominal moment at the ASCE/SEI 7-10 700-year load remains 1�0� The total applied nominal moments for ASD and other LRFD year wind speeds are adjusted to be equivalent to the ASCE/ SEI 7-10 700-year wind speed load case� Given that the nominal moment from wind for any year wind can be determined by: 700 2 700M V V MWT T = where: VT = wind speed for any year T wind speed, and MWT = nominal wind moment at any year T, and the total applied nominal moment becomes: 1 700 700 2 7002M M M M V V MT D WT T( ) = + = − + where MTz = total applied nominal moment at any year T wind speed� To determine the mean wind moment for the reliability analyses, the mean moment at the 50-year wind speed is determined from the ASCE/SEI 7-10 wind speed relation: 0�36 0�10ln 12 50V T VT [ ]
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A-47 Referring back to the basis that all comparisons are equated with a total ASCE/SEI 7-10 applied nominal moment of: 1700M MD + = and using that the nominal dead load moment and mean dead load moment are: 1 1 700 700 M M M M D D D ( ) = − = λ − where: lD = bias factor for dead load moment� The mean load effect on the structure becomes: 150 700 2 2 700Q M M M MD D P V X( )
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A-48 Table 7-2. Global inputs.
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A-49 Considering that the design V50 may differ from V50 = (lV) 2V700, a bias factor, lDesign, is introduced, and: 50 2 50 700 2 700 2 2 700DesignM V V M MDesign Design V= λ = λ λ 50 50 DesignV V Designλ = The total ASD design moment, MT3, consistent with MD + M700 = 1�0, becomes: 150 700 2 2 7003M M DesignM M MT D Design V( )
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A-50 The equations for determining the reliability indices are identical to those used for the LRFD cases� Implementation For the four regions, the ASD reliability analyses require additional inputs� Inputs for ASD are: • Importance factors Ilow = 0�87, Imed = 1�00, and Ihigh = 1�15; • Shape factor SF = Zx/Sx = 1�30 for a circular section; and • Wind overstress factor OSF = 4/3 = 1�333� The results for the Midwest and Western Region ASCE/SEI 7-05 medium importance Imed = 1�00 are shown in Table 7-6� The LRFD-required nominal strength is shown for direct comparison� For the Midwest and Western Region for low importance Ilow = 0�87, the results are as shown in Table 7-7� Notice that the total nominal moment, MT3, does not change, but the total design moment MD + M50I changes with the importance factor, resulting in different required nominal strength Rn� Similarly for high importance, the required nominal strength Rn increases as shown in Table 7-8 for the Midwest and Western Region� Table 7-6. Results for the Midwest and Western Region for medium importance.
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A-51 The importance factors directly change the required nominal resistances� Because the mean load Q and its variation does not change (not shown in these tables and the same as in the LRFD tables) , this difference in required nominal resistances changes the reliability indices b accordingly� Calibration and Comparison Using the proposed flexure load and resistance factors, and with the statistical properties incorporated into the reliability analyses, the plots in Figure 7-1 compare the reliability indices for the four regions between current ASD design procedures and the proposed LRFD procedures� The Minimum Beta plots represent the minimum indices over the four regions� Similarly, the Average Beta plots show the averages over the four regions� For the LRFD 300-year, 700-year, and 1,700-year wind speed cases, the equivalent ASD designs use Ilow = 0�87, Imed = 1�00, and Ihigh = 1�15 importance factors, respectively� The proposed LRFD procedures result in comparable but more consistent reliability over the range of designs� For lowimportance structures (using 300-year wind speeds)
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A-52 0.00 1.00 2.00 3.00 4.00 00.511.5 Be ta M Wind/M Total Average Beta - 700 Year LRFD ASD0.00 1.00 2.00 3.00 4.00 00.511.5 Be ta M Wind/M Total Minimum Beta - 700 Year LRFD ASD 0.00 1.00 2.00 3.00 4.00 00.511.5 Be ta M Wind/M Total Minimum Beta - 300 Year LRFD ASD 0.00 1.00 2.00 3.00 4.00 00.511.5 Be ta M Wind/M Total Average Beta - 300 Year LRFD ASD 0.00 1.00 2.00 3.00 4.00 00.511.5 Be ta M Wind/M Total Average Beta - 1700 Year LRFD ASD 0.00 1.00 2.00 3.00 4.00 00.511.5 Be ta M Wind/M Total Minimum Beta - 1700 Year LRFD ASD Figure 7-1. Minimum and average reliability indices.
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A-53 Figure 7-3. Required resistance ratios.
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A-54 700 Year Wind V700 115 T 700 V50 91.00991 Theory BIASX 0.8241 V700/V700 1.00 (V700/V700)
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A-55 Average Beta plots show the averages over the four regions� For the LRFD 300-year, 700-year, and 1,700-year wind speed cases, the equivalent ASD designs use Ilow = 0�87, Imed = 1�00, and Ihigh = 1�15 importance factors, respectively� The proposed LRFD procedures result in comparable but more consistent reliability over the range of designs for torsion compared to the flexural analyses� The discussion on flexure in terms of comparisons with ASD and required strengths also applies to torsion� LRFD Flexure-Shear Interaction Monte Carlo simulation (using a spreadsheet) was used to verify target reliability when there is a presence of moment and torsion� It was assumed that the flexural moment comprised dead and wind load moment, and that the torsion was from wind load only� This would be consistent with a traffic signal mast arm and pole structure� The interaction design equation limit is in the form: 1�0 1 2 M M R T T D D W W n W W T n γ + γ φ + γ φ ≤ At the optimum design, the interaction is equal to 1�0� Thus, there is a combination of a certain amount of flexure and a certain amount of torsion that results in an optimum design� Using the flexure and torsion analyses shown previously, where the design capacity fMn = [gD1MD + gWMW]
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A-56 designs) , this can be represented by the percentages a and b shown as: 1�0 1 2 2 ( )
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A-57 Figure 7-5. Monte Carlo–generated probability density for flexure only.
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A-58 S E c t I o N 8 Setting Target Reliability Indices The statistical characterization of the limit-state equation and the associated inputs are presented in the preceding sections� The reliability indices are computed based on the current ASD practice and the LRFD-LTS specifications� The comparisons made and presented previously are based on the recommended load and resistance factors� These factors are illustrated for the 700-year wind speeds (MRI = 700 years) � This MRI is for the typical structure; however, some consideration is warranted for structures that are located on travelways with low ADT and/or that are located away from the travelway, whereby failure is unlikely to be a traveler safety issue� Similarly, consideration is also warranted for structures that are located on heavily traveled roads where a failure has a significant chance of harming travelers and/ or suddenly stopping traffic, creating an event that causes a traffic collision with the structure and likely chain-reaction impacts of vehicles� Ultimately, judgment is used to set the target reliability indices for the different applications� This is often based on typical average performance under the previous design specifications (i�e�, ASD)
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A-59 The resulting indices are reasonable for the various applications, and the load and resistance factor were accordingly set� The load factors are summarized in Table 8-6� The resistance factors f for the primary limit states are illustrated in Table 8-7� For brevity, not all are illustrated� The resistance factors are provided in the individual material resistance sections� The resistance factor for service and fatigue limit states is 1�0� Sensitivities The previous discussion outlines the results of assignment of load and resistance factors and the resulting reliability indices� It is useful to illustrate the sensitivities of these assignments to the resulting reliability indices� The minimum and average values for all regions are used to demonstrate by varying the dead load, wind load, and resistance factors for steel flexure strength and extreme limit states� Note that an increase in resistance factor f decreases the reliability index b� An increase in load factor g increases b� The typical traffic signal structures have load ratios in the region of one-half, while high-mast poles have very little dead load effect and ratios that are nearer to unity� In Table 8-8, the area contained within the dotted lines indicates the region that is of typical interest� Table 8-1. MRI related to structure location and consequence of failure.
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A-60 Table 8-3. Reliability indices for the West Coast.
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A-61 Table 8-6. Load factors (same as Table 3.4.1 in the proposed LRFD-LTS specifications)
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Table 8-8. Sensitivity of the reliability index to load and resistance factors.
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= 0.95 dead-only = 1.25 dead = 1.1 wind = 1.0 = 1.0 dead-only = 1.25 dead = 1.1 wind = 1.0 = 0.85 dead-only = 1.25 dead = 1.1 wind = 1.0 Table 8-8. (Continued)
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A-64 S E c t I o N 9 Judgment must be employed in the calibration regarding the performance of existing structures under the current specifications and setting the target reliability index b for the LRFD-LTS specifications� The LRFD-LTS specifications were calibrated using the standard ASD-based specifications as a baseline� The variabilities of the loads and resistances were considered in a rigorous manner� The wind loads have higher variabilities than the dead loads� Therefore, a structure with high wind-tototal-load ratio will require higher resistance and associated resistances compared to ASD� This was shown to be on the order of a 10% increase for high-mast structures� For structures with approximately one-half wind load (e�g�, cantilever structures) , on average the required resistance will not change significantly� It is important to note that resistance is proportional to section thickness and proportional to the square of the diameter [i�e�, a 10% resistance increase may be associated with a 10% increase in thickness (area)
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A-65 Annex A Annual 300 Year 700 Year 1,700 Year n Mean Cov Max Mean Cov Mean Cov Mean Cov 1 Birmingham, Alabama 34 46.6 0.139 62.3 76.0 0.085 80.0 0.082 84.0 0.080 2 Prescott, Arizona 17 52.2 0.169 66.0 92.0 0.096 98.0 0.091 104.0 0.089 3 Tucson, Arizona 30 51.4 0.167 77.7 89.0 0.096 95.0 0.091 101.0 0.089 4 Yuma, Arizona 29 48.9 0.157 65.1 83.0 0.093 88.0 0.089 93.0 0.084 5 Fort Smith, Arkansas 26 46.6 0.150 60.7 78.0 0.091 83.0 0.085 87.0 0.082 6 Little Rock, Arkansas 35 46.7 0.206 72.2 90.0 0.111 96.0 0.106 103.0 0.102 7 Denver, Colorado 27 49.2 0.096 62.3 70.0 0.073 73.0 0.069 76.0 0.066 8 Grand Junction, Colorado 31 52.7 0.102 69.9 76.0 0.073 80.0 0.069 84.0 0.065 9 Pueblo, Colorado 37 62.8 0.118 79.2 95.0 0.079 100.0 0.075 105.0 0.071 10 Hartford, Connecticut 38 45.1 0.151 66.8 75.0 0.090 80.0 0.085 84.0 0.080 11 Washington, D.C. 33 48.3 0.135 66.3 78.0 0.085 82.0 0.082 86.0 0.078 12 Atlanta, Georgia 42 47.4 0.195 75.5 88.0 0.102 94.0 0.097 100.0 0.092 13 Macon, Georgia 28 45.0 0.169 59.7 79.0 0.100 84.0 0.095 89.0 0.088 14 Boise, Idaho 38 47.8 0.111 61.9 71.0 0.078 74.0 0.073 78.0 0.070 15 Pocatello, Idaho 39 53.3 0.128 71.6 84.0 0.079 88.0 0.075 92.0 0.071 16 Chicago, Illinois 35 47.0 0.102 58.6 68.0 0.075 72.0 0.070 75.0 0.066 17 Moline, Illinois 34 54.8 0.141 72.1 89.0 0.086 94.0 0.080 99.0 0.076 18 Peoria, Illinois 35 52.0 0.134 70.2 83.0 0.086 88.0 0.080 92.0 0.076 19 Springfield, Illinois 30 54.2 0.111 70.6 81.0 0.079 85.0 0.075 89.0 0.070 20 Evansville, Indiana 37 46.7 0.130 61.3 74.0 0.079 77.0 0.075 82.0 0.070 21 Fort Wayne, Indiana 36 53.0 0.125 69.0 82.0 0.082 87.0 0.077 91.0 0.074 22 Indianapolis, Indiana 34 55.4 0.200 93.0 103.0 0.105 110.0 0.098 119.0 0.092 23 Burlington, Iowa 23 56.0 0.164 71.9 97.0 0.094 103.0 0.090 110.0 0.085 24 Des Moines, Iowa 27 57.7 0.147 79.9 95.0 0.091 101.0 0.086 107.0 0.081 25 Sioux City, Iowa 36 57.9 0.157 88.1 98.0 0.096 104.0 0.091 111.0 0.085 26 Concordia, Kansas 16 57.6 0.160 73.7 98.0 0.095 104.0 0.092 111.0 0.085 27 Dodge City, Kansas 35 60.6 0.099 71.5 87.0 0.068 91.0 0.064 95.0 0.061 28 Topeka, Kansas 28 54.5 0.150 78.8 91.0 0.095 96.0 0.087 102.0 0.084 29 Wichita, Kansas 37 58.1 0.146 89.5 96.0 0.090 101.0 0.085 107.0 0.080 30 Louisville, Kentucky 32 49.3 0.136 65.7 79.0 0.088 84.0 0.082 88.0 0.078 31 Shreveport, Louisiana 11 44.6 0.121 53.4 69.0 0.078 72.0 0.076 76.0 0.073 32 Baltimore, Maryland 29 55.9 0.123 71.2 87.0 0.080 91.0 0.075 96.0 0.070 33 Detroit, Michigan 44 48.9 0.140 67.6 79.0 0.086 84.0 0.083 89.0 0.078 34 Grand Rapids, Michigan 27 48.3 0.209 66.8 93.0 0.108 99.0 0.102 107.0 0.093 35 Lansing, Michigan 29 53.0 0.125 67.0 83.0 0.082 87.0 0.079 92.0 0.076 Table A1.
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A-66 Annual 300 Year 700 Year 1,700 Year n Mean Cov Max Mean Cov Mean Cov Mean Cov 51 Ely, Nevada 39 52.9 0.117 70.1 80.0 0.078 85.0 0.074 89.0 0.070 52 Las Vegas, Nevada 13 54.7 0.128 70.1 85.0 0.083 90.0 0.079 95.0 0.074 53 Reno, Nevada 36 56.5 0.141 76.6 92.0 0.088 97.0 0.082 103.0 0.077 54 Winnemucca, Nevada 28 50.2 0.142 62.6 82.0 0.088 87.0 0.083 92.0 0.078 55 Concord, New Hampshire 37 42.9 0.195 68.5 80.0 0.105 85.0 0.100 92.0 0.094 56 Albuquerque, New Mexico 45 57.2 0.136 84.8 92.0 0.090 97.0 0.085 102.0 0.080 57 Roswell, New Mexico 31 58.2 0.153 81.6 98.0 0.096 104.0 0.088 110.0 0.085 58 Albany, New Mexico 40 47.9 0.140 68.5 77.0 0.085 82.0 0.078 87.0 0.075 59 Binghamton, New York 27 49.2 0.130 63.8 77.0 0.085 82.0 0.078 86.0 0.075 60 Buffalo, New York 34 53.9 0.132 78.6 85.0 0.086 92.0 0.079 96.0 0.076 61 Rochester, New York 37 53.5 0.097 65.4 77.0 0.069 80.0 0.067 84.0 0.063 62 Syracuse, New York 37 50.3 0.121 67.2 77.0 0.082 82.0 0.075 86.0 0.071 63 Charlotte, N Carolina 27 44.7 0.168 64.6 78.0 0.092 83.0 0.087 88.0 0.082 64 Greensboro, N
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A-67 Annual 300 Year 700 Year 1,700 Year n Mean Cov Max Mean Cov Mean Cov Mean Cov 1 Montgomery, Alabama 28 45.3 0.185 76.7 82.0 0.104 88.0 0.095 94.0 0.090 2 Jackson, Mississippi 29 45.9 0.155 64.4 78.0 0.092 82.0 0.087 87.0 0.082 3 Austin, Texas 35 45.1 0.122 58.0 70.0 0.074 73.0 0.071 77.0 0.067 4 Portland, Maine 37 48.5 0.179 72.8 87.0 0.100 92.0 0.096 99.0 0.089 Table A2. Statistical parameters of wind for Costal Segment 1.
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A-68 Annual 300 Year 700 Year 1,700 Year n Mean Cov Max Mean Cov Mean Cov Mean Cov 1 Fresno, California 37 34.4 0.140 46.5 55.0 0.090 58.0 0.086 62.0 0.080 2 Red Bluff, California 33 52.1 0.141 67.3 85.0 0.089 90.0 0.086 95.0 0.082 3 Sacramento, California 29 46.0 0.223 67.8 92.0 0.112 98.0 0.108 105.0 0.098 4 San Diego, California 38 34.5 0.130 46.6 54.0 0.085 57.0 0.082 60.0 0.080 5 Portland, Oregon 28 52.6 0.196 87.9 99.0 0.104 105.0 0.100 112.0 0.092 6 Roseburg, Oregon 12 35.6 0.169 51.1 62.0 0.095 66.0 0.090 70.0 0.085 7 North Head, Washington 41 71.5 0.141 104.4 116.0 0.088 123.0 0.083 130.0 0.078 8 Quillayute, Washington 11 36.5 0.085 41.9 50.0 0.060 52.0 0.058 54.0 0.056 9 Seattle, Washington 10 41.9 0.080 49.3 57.0 0.060 59.0 0.058 61.0 0.056 10 Spokane, Washington 37 47.8 0.133 64.6 76.0 0.084 80.0 0.077 84.0 0.074 11 Tatoosh Island, Washington 54 66.0 0.106 85.6 97.0 0.073 102.0 0.072 107.0 0.069 Table A8. Statistical parameters of wind for the West Coast.
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A-69 Annex B Table B1. Test results (from Stam et al., 2011)
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A-70 Table B1. (Continued)
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A-71 Arm Sleeve to Pole Connection 16 6.80E+05 2.79E+09 E 3.06E+07 Arm Sleeve to Pole Connection 12 4.30E+05 7.43E+08 E 8.15E+06 Connection Detail Sr [ksi]
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A-72 Pole Base 11.6 5.90E+05 9.21E+08 D 2.68E+06 Pole Base 14 1.00E+05 2.74E+08 D 8.00E+05 Pole Base 14 1.00E+05 2.74E+08 D 8.00E+05 Pole Base 13.5 3.90E+05 9.6E+08 D 2.80E+06 Pole Base 13.5 4.70E+05 1.16E+09 D 3.37E+06 Pole Base 13.5 4.70E+05 1.16E+09 D 3.37E+06 Connection Detail Sr [ksi]
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A-73 Table B2. (Continued)
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Abbreviations and acronyms used without definitions in TRB publications: A4A Airlines for America AAAE American Association of Airport Executives AASHO American Association of State Highway Officials AASHTO American Association of State Highway and Transportation Officials ACI–NA Airports Council International–North America ACRP Airport Cooperative Research Program ADA Americans with Disabilities Act APTA American Public Transportation Association ASCE American Society of Civil Engineers ASME American Society of Mechanical Engineers ASTM American Society for Testing and Materials ATA American Trucking Associations CTAA Community Transportation Association of America CTBSSP Commercial Truck and Bus Safety Synthesis Program DHS Department of Homeland Security DOE Department of Energy EPA Environmental Protection Agency FAA Federal Aviation Administration FHWA Federal Highway Administration FMCSA Federal Motor Carrier Safety Administration FRA Federal Railroad Administration FTA Federal Transit Administration HMCRP Hazardous Materials Cooperative Research Program IEEE Institute of Electrical and Electronics Engineers ISTEA Intermodal Surface Transportation Efficiency Act of 1991 ITE Institute of Transportation Engineers MAP-21 Moving Ahead for Progress in the 21st Century Act (2012) NASA National Aeronautics and Space Administration NASAO National Association of State Aviation Officials NCFRP National Cooperative Freight Research Program NCHRP National Cooperative Highway Research Program NHTSA National Highway Traffic Safety Administration NTSB National Transportation Safety Board PHMSA Pipeline and Hazardous Materials Safety Administration RITA Research and Innovative Technology Administration SAE Society of Automotive Engineers SAFETEA-LU Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (2005)
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Key Terms
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