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Title Sensitivity Analysis of 2002 Design Guide Rigid Pavement Distress Prediction Models

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June 2006Design Guide: UCPRC-DG-2006-01 Sensitivity Analysis of 2002 Design Guide Rigid Pavement Distress Prediction ModelsAuthors:Venkata Kannekanti and John HarveyThis work was completed as part of Partnered PavementResearch Program Strategic Plan Element 4.1:“Development of the First Version of a Mechanistic-Empirical Pavement Rehabilitation, Reconstruction and New Pavement Design Procedure for Rigid andFlexible Pavements(pre-Calibration of AASHTO 2002)”PREPARED FOR:California Department of TransportationDivision of Research and Innovation Office of Roadway Research PREPARED BY: University of California Pavement Research Center Berkeley and DavisDOCUMENT RETRIEVAL PAGE Report No: UCPRC-DG-2006-01 Title: Sensitivity Analysis of 2002 Design Guide Rigid Pavement Distress Prediction Models Authors: Venkata Kannekanti and John HarveyPrepared for:California Department of Transportation Division of Research and Innovation Office of Roadway ResearchFHWA No.:F/CA/DG/2006/48Date:June 2006,Final version September 2006Client Reference Number: UCPRC-DG-2006-01 Status:FinalVersion No.:3Abstract:The AASHTO 2002 Design Guide (2002DG) has been calibrated using LTPP sections throughout the nation but with very few sections from the state of California. This created the need to validate the models in 2002DG and recalibrate them if needed so that they may be used for pavement design and rehabilitation in California. In order to validate the design guide, a three-stage process has been identified: bench testing or sensitivity analysis, verification using accelerated pavement testing data, and verification using field data. The study presented in this report includes performing sensitivity analysis of the rigid part of 2002DG.Sensitivity analysis helps to check the reasonableness of the model predictions, to identify problems in the software, and to help understand the level of difficulty involved in obtaining the inputs. The reasonableness of the model predictions is checked by varying key design variables including traffic volume, axle load distribution, climate zone, thickness, shoulder type, joint spacing, load transfer efficiency, PCC strength, base type, and subgrade type. The chosen factorial resulted in approximately 8,500 simulations. The software outputs are transverse cracking, faulting, and IRI. A couple of related sensitivity studies have also been undertaken to study the effect of variables including surface absorptivity and coefficient of thermal expansion, which were not included in the primary sensitivity analysis.Results from all the simulations showed that almost all of the cases produce reasonable values for transverse cracking, faulting, and IRI. The transverse cracking model is sensitive to coefficient of thermal expansion, joint spacing, shoulder type, PCC thickness, and traffic volume. The faulting values are sensitive to dowels, shoulder type, climate zone, PCC thickness and traffic volume. However, there are cases for which model predictions disagree with prevailing knowledge in pavement engineering. This study also revealed some problems associated with the software.Keywords: Mechanistic-empirical design; Sensitivity analysis; Axle loads; Climate; Pavement distress; Portland cement concrete; Sensitivity analysis; Soil types; Thermal expansion; Thickness; Traffic volumeProposals for implementation: It is proposed to develop sample design catalogs for rigid pavements based on the DG2002 software coupled with Caltrans experience. Final designs should not be based exclusively on performance predictions from the current version of the software.Related documents: Transportation Research Board Annual Meeting 2006 Paper #06-1893 (accepted for publication at the Transportation Research Records Journal)Signatures:Venkata Kannekanti First AuthorErwin KohlerTechnical ReviewC. Scheffy, original.D. Spinner, revised materialEditorJohn HarveyPrincipalInvestigatorMichael SamadianCaltrans ContractManagerDISCLAIMERThe contents of this report reflect the views of the authors who are responsible for the facts and accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the State of California or the Federal Highway Administration. This report does not constitute a standard, specification, or regulation.TABLE OF CONTENTSList of Figures (vii)List of Tables (ix)Executive Summary (x)1. Introduction (1)1.1NCHRP 1-37a Project Background (1)1.2Objectives of the Study (2)1.3Scope of the Report (2)2. Experiment Design (3)2.1Traffic Inputs (4)2.1.1Two-way Annual Average Daily Truck Traffic (AADTT) (4)2.1.2Traffic Volume Adjustment Factors (4)2.1.3Axle Load Distribution Factors (5)2.1.4General Traffic Inputs (5)2.2Climate (5)2.3Pavement Design Features (7)2.4Drainage and Surface Properties (7)2.5Pavement Structure (7)2.6Layer Properties (8)2.6.1PCC Slab (8)2.6.2Asphalt Concrete Base (8)2.6.3Cement Treated Base (9)2.6.4Aggregate Subbase (9)2.6.5Subgrade (9)2.7Difficulty in Obtaining Sufficient Input Data (9)2.7.1Traffic (9)2.7.2Climate (10)2.7.3Pavement Design Features (10)2.7.4Drainage and Surface Properties (10)2.7.5PCC Layer Properties (10)2.7.6Cement Treated Bases (11)2.7.7Asphalt Concrete Bases (11)2.7.8Unbound Materials (Aggregate Base and Subgrade) (11)3. Results and Analysis (12)3.1Effect of Variables on Transverse Cracking (13)3.1.2Effect of Joint Spacing on Cracking (14)3.1.3Effect of Climate on Cracking (15)3.1.4Effect of Traffic Volume (16)3.1.5Effect of Subgrade Type (16)3.1.6Effect of Slab Thickness (18)3.1.7Effect of Base Type (18)3.1.8Effect of Load Spectra (18)3.1.9Effect of Dowels (20)3.1.10Effect of PCC Flexural Strength (20)3.1.11Summary (20)3.2Effect of Variables on Faulting (23)3.3Effect of Variables on IRI (28)3.4Comparison of IRI Models from 2002DG and Ripper Study (36)3.5Effect of Coefficient of Thermal Expansion on Transverse Cracking, Faulting, and IRI (38)3.6Summary of Sensitivity Analysis Results (40)4. Anomalies in the Predictions (43)4.1Shortwave Surface Absorptivity (43)4.2Cases for which Thinner Pavement Sections Perform Better Than Thicker Sections (47)4.2.1Cases for which Structures with 7-in. Slabs Perform Better Than Those with 8-in. Slabs (47)4.2.2Cases for which Structures with 8-in. Slabs Perform Better Than Those with 9-in. Slabs (47)4.2.3Cases for which Structures with 9-in. Slabs Perform Better Than Those with 10-in. Slabs (47)4.2.4Cases for which Structures with 10-in. Slabs Perform Better Than Those with 12-in. Slabs (48)4.3Cases for which Structures with Asphalt Shoulders Perform Better Than Structures with TiedShoulders (48)4.4Cases for which Structures with Asphalt Shoulder Perform Better Than Structures with WidenedTruck Lanes (48)4.5Cases for which Tied Shoulder Structures Perform Better Than Structures with Wide Truck Lanes (49)4.6Subgrade (49)4.7High K-value of Subgrade (56)5. Limitations and Bugs in the Software (57)5.1Inability to Reproduce Results (57)5.2Base Properties (57)5.3Aggregate Type (59)5.4Climate Data (59)5.5Running the Software in Batch Mode (60)5.7Other Problems (61)6. Conclusions (64)7. References (66)8. Appendix A: Screen Shots from the Software (67)LIST OF FIGURESFigure 1. Axle load spectra from WIM located in a rural area (Site No. 2, Redding, SHA I-5) (6)Figure 2. Axle load spectra from a WIM located in an urban area (Site No. 39, Redlands, SBD SR-30) (6)Figure 3. Pavement structure used for the sensitivity study (8)Figure 4. Key to understanding the plots (13)Figure 5. Effect of shoulder type on transverse cracking (14)Figure 6. Effect of joint spacing on transverse cracking (15)Figure 7. Effect of climate region on transverse cracking (16)Figure 8. Effect of traffic volume on transverse cracking (17)Figure 9. Effect of subgrade type on transverse cracking (17)Figure 10. Effect of PCC thickness on transverse cracking (19)Figure 11. Effect of base type on transverse cracking (19)Figure 12. Effect of load spectra on transverse cracking (20)Figure 13. Effect of PCC flexural strength on transverse cracking (21)Figure 14. Relative effect of all variables on transverse cracking (22)Figure 15. Effect of dowels on faulting (23)Figure 16. Effect of climate region on faulting (24)Figure 17. Effect of PCC thickness on faulting (24)Figure 18. Effect of traffic index (TI) on faulting (25)Figure 19. Effect of shoulder type on faulting (25)Figure 20. Effect of joint spacing on faulting (26)Figure 21. Effect of base type on faulting (26)Figure 22. Effect of subgrade on faulting (27)Figure 23. Effect of load spectra on faulting (27)Figure 24. Effect of PCC flexural strength on faulting (28)Figure 25. Relative effect of all variables on faulting (29)Figure 26. Effect of PCC thickness on IRI (31)Figure 27. Effect of shoulder type on IRI (32)Figure 28. Effect of traffic index (TI) on IRI (32)Figure 29. Effect of dowels on IRI (33)Figure 30. Effect of joint spacing on IRI (33)Figure 31. Effect of load spectra on IRI (34)Figure 32. Effect of base type on IRI (34)Figure 33. Effect of subgrade type on IRI (35)Figure 34. Effect of PCC flexural strength on IRI (35)Figure 35. Effect of climate region on IRI (36)Figure 36. Relative effect of all variables on IRI (37)Figure 37. Comparison of IRI models from 2002DG and Ripper study (39)Figure 38. Effect of COTE on transverse cracking (40)Figure 39. Effect of COTE on faulting (41)Figure 40. Effect of surface absorptivity on transverse cracking, an example (44)Figure 41. Effect of surface absorptivity on transverse cracking (45)Figure 42. Effect of surface absorptivity on faulting (45)Figure 43. Effect of surface absorptivity on transverse cracking compared to other variables (46)Figure 44. Effect of surface absorptivity on faulting compared to other variables (46)Figure 45. Pavement structure used to study the effect of soil type (50)Figure 46. Faulting for different subgrade types, undoweled pavements (52)Figure 47. Faulting for different subgrade types, doweled pavements (52)Figure 48. Cracking for different subgrade types, both doweled and undoweled pavements (53)Figure 49. Effect of subgrade on DE for undoweled pavements according to 2002DG (55)Figure 45. Base Properties input screen shots (58)Figure 46. Structure window screen shot (59)Figure 47. Error message when Ukiah climate file was used (60)Figure 48. Batch file window (61)Figure 49. Error message (61)Figure 50. Debug mode (62)Figure 51. Screen shot showing that inputs could not be entered (63)Figure 52. Error message when PCC thickness is chosen as 10 in (63)Figure A1. General Traffic Inputs (Number Axles/Truck tab) (67)Figure A2. General Traffic Inputs (Axle Configuration tab) (68)Figure A3. General Traffic Inputs (Wheelbase tab) (69)Figure A4. JPCP Design Features (70)Figure A5. PCC Material Properties (Thermal properties tab) (71)Figure A6. PCC Material Properties (Mix properties tab) (72)Figure A7. PCC Material Properties (Strength tab) (73)Figure A8. Asphalt Material Properties (Asphalt Mix tab) (74)Figure A9. Asphalt Material Properties (Asphalt Binder tab) (75)Figure A10. Asphalt Material Properties (Asphalt General tab) (76)Figure A11. Cement/Lime Stabilized Material (Cement Stabilized option) (77)Figure A12. Unbound Layer #3 (Strength Properties tab, A-1-a option) (78)Figure A13. Unbound Layer #3 (ICM tab) (79)Figure A14. Unbound Layer #4 (Strength Properties tab, CH material option) (80)Figure A15. Unbound Layer #4 (Strength Properties tab, SP material option) (81)Figure A16. Unbound Layer #4 (ICM tab, CH material option) (82)Figure A17. Unbound Layer #4 (ICM tab, SP material option) (83)LIST OF TABLESTable 1Variables and Factor Levels Used for Sensitivity Analysis of 1-37a (3)Table 2Two-way AADTT at Both WIM Stations for All Three TI Values* (4)Table 3Annual Average Weather Data for the Three Climate Regions Used in the Study (7)Table 4Experiment Design to for Study of the Effect of the Coefficient of Thermal Expansion (COTE) (39)Table 5Mean Results of Sensitivity Analysis for Each Variable and Factor Level (42)Table 6Experiment Design for Study of the Effect of Surface Absorptivity (43)Table 7Key Inputs Used to Study the Effect of Soil Type (50)Table 8Performance of Structures with Different Soil Types and without Dowels (51)Table 9Performance of Structures with Different Soil Types and with Dowels (51)Table 10DE Estimation Based on EverFE Runs (55)Table 11Default P200 Values Used in the Study (55)Table 12Effect of P200 on Faulting, Cracking, and IRI for Undoweled Pavements (56)EXECUTIVE SUMMARYThe AASHTO 2002 Design Guide (2002DG) has been calibrated using Long Term Pavement Performance (LTPP) sections scattered throughout the nation but with very few sections from the state of California. This created the need to validate the models in 2002DG and recalibrate them if needed so that they may be used for pavement design and rehabilitation in California. In order to validate the design guide, a three-stage process has been identified: bench testing or sensitivity analysis, verification using accelerated pavement testing data, and verification using field data. The study presented in this report includes performing sensitivity analysis of the rigid part of 2002DG.Sensitivity analysis helps to check the reasonableness of the model predictions, to identify problems in the software and to help understand the level of difficulty involved in obtaining the inputs. The reasonableness of the model predictions is checked by varying key design variables including traffic volume, axle load distribution, climate zone, thickness, shoulder type, joint spacing, load transfer efficiency, PCC strength, base type, and subgrade type. The chosen factorial resulted in approximately 8,500 simulations. The software outputs are transverse cracking, faulting, and IRI. A couple of related sensitivity studies have also been undertaken to study the effect of variables including surface absorptivity and coefficient of thermal expansion, which were not included in the primary sensitivity analysis.Results from all the simulations showed that almost all of the cases produce reasonable values for transverse cracking, faulting, and IRI. The transverse cracking model is sensitive to coefficient of thermal expansion, joint spacing, shoulder type, PCC thickness, and traffic volume. The faulting values are sensitive to dowels, shoulder type, climate zone, PCC thickness and traffic volume. However, there are cases for which model predictions disagree with prevailing knowledge in pavement engineering. This study also revealed some problems associated with the software.1.INTRODUCTIONThe AASHO (American Association of State Highway Officials) road test was performed in 1958. It has been over 40 years since empirical-based pavement design procedures were developed based on the AASHO road test. Few changes were made to these procedures over the years despite the many limitations of the test. Some of the limitations of the AASHO road test are:•One climate region•Limited traffic•One vehicle type•One subgrade typeBecause of the limitations of the empirical procedures based on the road test, the AASHTO (American Association of State Highway and Transportation Officials) Joint Task Force on Pavements (JTFP) took the initiative to develop a new pavement design guide. The JTFP proposed that the new design guide should be based on well-established mechanistic-empirical models and utilize more comprehensive data sets, such as Long Term Pavement Performance (LTPP) data. The JTFP’s initiative resulted in NCHRP Project 1-37a.1.1NCHRP 1-37a Project BackgroundThe objective of NCHRP 1-37a is to develop a pavement design tool based on mechanistic-empirical principles. The resulting pavement design tool, called the 2002 Design Guide (2002DG), is intended to be user-friendly software for analysis and design of new, reconstructed, and rehabilitated flexible, rigid, and composite pavements. The 2002 Design Guide is a result of coordinated effort of NCHRP Project Panel C1-37 and AASHTO JTFP. The models in the design guide were calibrated using data from LTPP sections from all over the nation. However, very few sections from California were used for calibration of the models in 2002DG.AASHTO recommends that each state validate and, if necessary, recalibrate the models using the climate, traffic, and materials data more representative of each state. The validation process adopted in California consists of three steps:•Bench testing or sensitivity analysis,•Validation using accelerated pavement testing data, and•Validation using field data.The models will be recalibrated using California field data if validation results show serious discrepancies between the observed distresses and the distresses predicted by the models. The study presented in this report concentrates only on the sensitivity analysis of the software. The following section explains the objectives of the sensitivity analysis.1.2Objectives of the StudyThe objectives of the study presented herein are:1.Evaluate the reasonableness of rigid pavement design models in 2002DG for California traffic andclimate conditions.2.Estimate the level of difficulty in using 2002DG design procedures for designing new rigidpavements in California.3.Identify any problems or bugs evident in the software.The reasonableness of the model predictions are checked by varying key design variables like traffic volume, axle load distribution, climate zone, thickness, shoulder type, joint spacing, load transfer efficiency, PCC strength, base type, and subgrade type. The software was run for all combinations of these key variables and the results from cases were compared.1.3Scope of the ReportThe experiment design used for sensitivity analysis is explained in Chapter 2. Chapter 2 also discusses the inputs used to run the sensitivity analysis, the source of these inputs, and the level of difficulty in obtaining the inputs to run the software.The results of sensitivity analysis are discussed in Chapter 3. Various plots summarizing the effects of different variables on transverse cracking, faulting and IRI are presented.Chapter 4 describes cases in the sensitivity analysis where results disagree with the prevailing knowledge in pavement engineering.Chapter 5 discusses the problems and bugs associated with the software.Conclusions from the sensitivity analysis are presented in Chapter 6.2.EXPERIMENT DESIGNSome important variables that affect the pavement design software were selected and the software was run for several factor levels for the selected variables. The variables selected for the sensitivity study and the factor levels used are shown in Table 1. To the extent possible, the variables and factor levels were chosen to represent the practices adopted by the California Department of Transportation (Caltrans).Table 1 Variables and Factor Levels Used for Sensitivity Analysis of 1-37aVariable FactorLevels1 Axle Load Spectra (2) Urban Rural2 Traffic Volume (3) TI: 12 13 163 Climate Region (3) South Coast (Los Angeles) Valley (Sacramento) Mountain (Reno)14 PCC Thickness (5) 7 in.8 in.9 in.10 in.12 in.5 Base Type (2) Asphalt Concrete Base Cement Treated Base6 Subgrade Type (2) High Plasticity Clay (CH) Poorly graded sand (SP)7 Dowels (2) Dowels No Dowels8 Shoulder Type (3) Asphalt Shoulders Tied Shoulders Widened Truck Lane9 Joint Spacing (2) 15 ft.19 ft.10 Strength 2 (2) 626 psi 700 psiTotal Number of Cases: 8,6401Reno though in Nevada, has climate similar to high desert and mountain climate zones of California and has good climate data and so is used in this study.228-day PCC flexural strength.All cases were run with a reliability level of 50% and a design life of 30 years. A detailed discussion of the inputs and the sources of inputs are presented in the next section. The software allows a hierarchical approach to enter the inputs at three levels. Level 1 inputs yield accurate results, but the inputs require lot of lab and field testing and consume more time and resources. Level 2 inputs are obtained from agency databases or estimated through correlations. Level 3 inputs are default values or typical averages for the project location and materials used.2.1Traffic InputsMost of the traffic inputs are derived from Caltrans weigh-in-motion (WIM) data. WIM data at two locations (Urban and Rural) with three volumes of traffic, in the form of Traffic Index (TI) have been used for this study. Urban locations have more Class 5 trucks (short trailers) than Class 9 trucks (long trailers) and rural locations have more Class 9 trucks.The urban location used for this analysis is WIM station 02 located on I-5 at Redding. The rural location is represented by WIM station 39 on SR-30 at Redlands. The three traffic volumes (TI values of 12, 13, and 16) correspond to approximately 11 million, 22 million, and 126 million ESALs, respectively.2.1.1Two-way Annual Average Daily Truck Traffic (AADTT)The AADTT corresponding to the two locations and traffic spectra are given in Table 2. AADTT information for each TI is estimated using WIM data. TI is first converted into axles. AADTT is calculated based on the average number of axles per truck for that site.Table 2 Two-way AADTT at Both WIM Stations for All Three TI Values*Spectra\TI121316Rural (WIM 02) 990 1968 11256Urban (WIM 39) 1766 3462 19820 *E rratum: Due to a misinterpretation, AADTTs in the table actually represent TIs of 11.4, 12.4, and 15.3 for rural spectra and TIs of 11.6, 12.6, and 15.5 for urban spectra. This correction has no effect on the observations/conclusions of this report.2.1.2Traffic Volume Adjustment FactorsTraffic volume adjustment factors are used to determine AADTT within each hour of the day for each month and for each truck class. This determination requires the following:•Hourly truck distribution factors•Vehicle class distribution factors•Monthly adjustment factorsEach of these factors is obtained from the WIM data. In addition to traffic volume adjustment factors, the expected growth rate must be entered for the AADTT. In this study, growth rate is assumed to be zero because all the truck traffic is uniformly distributed for the entire design life. This assumption has little effect on the results because Miner’s law, which assumes a linear damage rate with traffic repetitions, is used for damage accumulation in the distress prediction models. The only sensitivity of the results would be due to PCC strength gain effects.2.1.3Axle Load Distribution FactorsThe normalized axle load distributions used in this study are determined from the WIM data. The axle load distribution is entered for single axles, tandem axles, tridem axles, and quad axles. Urban and rural locations have significantly different axle load distributions. The axle load distributions for both the locations chosen are shown in Figures 1 and 2. The 2002 Design Guide requires the axle load distribution factors for each month and for each class of vehicle, however, Figures 1 and 2 give the axle load distribution frequency for all truck classes combined and for all months. Nevertheless, the plots show the basic difference in axle load distribution at the two locations. Very few trucks with quad axles operate in California, so the axle load distribution factors for quad axles are assumed to be zero.2.1.4General Traffic InputsThis category of inputs include information like mean wheel location, traffic wander standard deviation, design lane width, wheel base information, tire dimensions, and tire inflation pressures. Default values have been used for all of the general traffic inputs. The screen shots of the inputs used are shown in Appendix A. Other information in this category includes the number of axle types per truck class and axle configuration, which were obtained from WIM data and are also presented in Appendix A.2.2ClimateAll of the necessary climate information at any given location can be generated by simply selecting the weather station near the location of pavement construction. The three climate regions used for the sensitivity analysis are:•South Coast (Los Angeles)•Valley (Sacramento)•Mountain/High Desert (Reno)Table 3 shows the differences in temperatures and precipitation for the three climate regions. These values are obtained from Climate Database for Integrated Model (CDIM) software version 1.0, which is based on daily and hourly weather data in the western half of the United States. The Mountain/High Desert climate zone will be addressed as mountain climate zone for the rest of the report.One other climate input required for analysis is the depth of the water table. A default value of 30 feet is assumed for all the three climate regions.0.020.040.060.080.10.120.140.160.180.2051015202530354045Load Range A x l e L o a d F r e q u e n c yFigure 1. Axle load spectra from WIM located in a rural area (Site No. 2, Redding, SHA I-5).0.020.040.060.080.10.120.140.160.180.2051015202530354045Load Range A x l e L o a d F r e q u e n c yFigure 2. Axle load spectra from a WIM located in an urban area (Site No. 39, Redlands, SBDSR-30).Table 3 Annual Average Weather Data for the Three Climate Regions Used in the Study Weather Data\Climate Region Los Angeles Sacramento RenoLowest Air Temperature (ºC) 3 -3.3 -17.4Highest Air Temperature (ºC) 36.5 41.4 38.4Freezing Index (ºC–Days) 0 0 119Number of Freeze-Thaw Cycles 0 3 116Total Yearly Precipitation (mm) 325 446 193Total Yearly Snowfall (mm) 0 1 6272.3Pavement Design FeaturesPavement design features include joint spacing, shoulder type, load transfer efficiency, and PCC-base interface. Joint spacing of 15 and 19 feet were used for this study. Three different shoulder types have been considered: asphalt shoulders, widened truck lane, and tied shoulders. A default load transfer of 40% between the slab and the shoulder is assumed for tied shoulders. Wide truck lanes are 14 feet wide, two feet wider than the normal width. Two cases of load transfer efficiencies are considered, doweled and undoweled. For doweled pavements, the diameter of dowels is 1.5 in. and dowel spacing is 12 in. The permanent curl/warp effective temperature difference is assumed to be –10ºF (with the top of the slab cooler than the bottom of the slab). The joint sealant type is assumed to be silicone. It is assumed that there is no bonding between the base and the PCC slab. Erodibility Index of the base is assumed to be 3, meaning that the base material is erosion resistant.A screen shot of the JPCP Design Features input window is shown in Appendix A.2.4Drainage and Surface PropertiesThis category of inputs includes surface shortwave absorptivity, infiltration, drainage path length, and pavement cross slope. The default value used in the software for surface shortwave absorptivity is 0.85 and this value is used for calibrating the models in the software. However, in this study surface absorptivity is assumed to be 0.65, based on a study conducted by Lawrence Berkeley National Laboratory, which indicated that new rigid pavements have surface absorptivity of 0.65.(1) Default values are used for drainage parameters. Values assumed for infiltration, drainage path length, and pavement cross slopes are 10%, 12 ft., and 2% respectively.2.5Pavement StructureThe assumed pavement structure is a PCC slab of one of several thicknesses (7, 8, 9, 10, or 12 in.), 6 inches of cement treated base or asphalt concrete base, 6 inches of aggregate subbase, and CH or SP subgrade. Figure 3 shows the pavement structure used for the study.。

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仿真算法知识点总结

仿真算法知识点总结

仿真算法知识点总结一、简介仿真算法是一种通过生成模型和运行模拟来研究系统或过程的方法。

它是一种用计算机模拟真实世界事件的技术，可以用来解决各种问题，包括工程、商业和科学领域的问题。

仿真算法可以帮助研究人员更好地理解系统的行为，并预测系统未来的发展趋势。

本文将对仿真算法的基本原理、常用技术和应用领域进行总结，以期帮助读者更好地了解和应用仿真算法。

二、基本原理1. 离散事件仿真（DES）离散事件仿真是一种基于离散时间系统的仿真技术。

在离散事件仿真中，系统中的事件和状态都是离散的，而时间是连续变化的。

离散事件仿真通常用于建模和分析复杂系统，例如生产线、通信网络和交通系统等。

离散事件仿真模型可以用于分析系统的性能、验证系统的设计和决策支持等方面。

2. 连续仿真（CS）连续仿真是一种基于连续时间系统的仿真技术。

在连续仿真中，系统中的状态和事件都是连续的，而时间也是连续的。

连续仿真通常用于建模和分析动态系统，例如电力系统、控制系统和生态系统等。

连续仿真模型可以用于分析系统的稳定性、动态特性和系统参数的设计等方面。

3. 混合仿真（HS）混合仿真是一种同时兼具离散事件仿真和连续仿真特点的仿真技术。

混合仿真可以用于建模和分析同时包含离散和连续过程的系统，例如混合生产系统、供应链系统和环境系统等。

混合仿真模型可以用于分析系统的整体性能、协调离散和连续过程以及系统的优化设计等方面。

4. 随机仿真随机仿真是一种基于概率分布的仿真技术。

在随机仿真中，系统的状态和事件都是随机的，而时间也是随机的。

随机仿真通常用于建模和分析具有随机性质的系统，例如金融系统、天气系统和生物系统等。

随机仿真模型可以用于分析系统的风险、概率特性和对策选择等方面。

5. Agent-Based ModelingAgent-based modeling (ABM) is a simulation technique that focuses on simulating the actions and interactions of autonomous agents within a system. This approach is often used for modeling complex and decentralized systems, such as social networks, biologicalecosystems, and market economies. In ABM, individual agents are modeled with their own sets of rules, behaviors, and decision-making processes, and their interactions with other agents and the environment are simulated over time. ABM can be used to study the emergent behavior and dynamics of complex systems, and to explore the effects of different agent behaviors and interactions on system-level outcomes.三、常用技术1. Monte Carlo方法蒙特卡洛方法是一种基于随机模拟的数值计算技术。

'subrank'软件包说明说明书

'subrank'软件包说明说明书

Package‘subrank’April5,2023Type PackageTitle Computes Copula using Ranks and SubsamplingVersion0.9.9.3Date2023-04-06Author Jerome ColletMaintainer Jerome Collet<***********************>DescriptionEstimation of copula using ranks and subsampling.The main feature of this method is that simu-lation studies show a low sensitivity to dimension,on realistic cases.License GPL(>=3)LazyLoad yesNeedsCompilation yesRepository CRANDate/Publication2023-04-0517:50:05UTCR topics documented:subrank-package (2)corc (3)corc0 (4)desscop (5)desscoptous (6)estimdep (7)predictdep (8)predonfly (9)simany (11)simnul (12)Index1412subrank-packagesubrank-package Computes Copula using Ranks and SubsamplingDescriptionEstimation of copula using ranks and subsampling.The main feature of this method is that simula-tion studies show a low sensitivity to dimension,on realistic cases.DetailsThe DESCRIPTIONfile:Package:subrankType:PackageTitle:Computes Copula using Ranks and SubsamplingVersion:0.9.9.3Date:2023-04-06Author:Jerome ColletMaintainer:Jerome Collet<***********************>Description:Estimation of copula using ranks and subsampling.The main feature of this method is that simulation st License:GPL(>=3)LazyLoad:yesNeedsCompilation:yesIndex of help topics:corc Function to estimate copula using ranks andsub-samplingcorc0Function to estimate copula using ranks andsub-sampling,minimal version.desscop Discrete copula graph,a two-dimensionalprojectiondesscoptous Discrete copula graph,ALL two-dimensionalprojectionsestimdep Dependence estimationpredictdep Probability forecastingpredonfly Probability forecastingsimany Test statistic distribution under anyhypothesissimnul Test statistic distribution under independencehypothesissubrank-package Computes Copula using Ranks and SubsamplingTaking a sample,its dimension,and a sub-sample size,allows to estimate a discretized copula.Thisobject has interesting features:convergence to copula,robustness with respect to dimension.Author(s)Jerome ColletMaintainer:Jerome Collet<***********************>Exampleslon<-31a<-2.85x<-rnorm(lon)y=a*x^2+rnorm(lon)tablo=as.data.frame(cbind(x,y))c=corc(tablo,c(1,2),8)desscop(c,1,2)corc Function to estimate copula using ranks and sub-samplingDescriptionTakes a sample,its dimension,a sub-sample size,and returns a discrete copula.Usagecorc(dataframe,varnames,subsampsize,nbsafe=5,mixties=FALSE,nthreads=2) Argumentsdataframe a data frame,containing the observationsvarnames the name of the variables we want to estimate the dependence betweensubsampsize the sub-sample sizenbsafe the ratio between the number of sub-samples and the cardinality of the dis-cretized copula.mixties if TRUE,put equal weight on tied values,using random permutationsnthreads number of number of threads,assumed to be strictly positive.For"full throttle"computations,consider using parallel::detectCores()Valuecop an array representing the discretized copulaties the number of sub-samples with a tiensubsampreal the effective number of sub-samples drawnvarnames the name of the variables studiednnm the number of observations without missing valuesAuthor(s)Jerome ColletExampleslon<-30a<-2x<-rnorm(lon)y=a*x^2+rnorm(lon)datatable=as.data.frame(cbind(x,y))c=corc(datatable,c("x","y"),8)csum(c$cop)corc0Function to estimate copula using ranks and sub-sampling,minimalversion.DescriptionMinimal version of function corc.Usagecorc0(datavector,sampsize,dimension,subsampsize,nboot,u,mixties=FALSE,nthreads=2) Argumentsdatavector a vector,containing the observationssampsize the sample sizedimension the sample dimensionsubsampsize the sub-sample sizenboot the number of sub-samples(must be big)u a random seed,integermixties if TRUE,put equal weight on tied values,using random permutationsnthreads number of number of threads,assumed to be strictly positive.For"full throttle"computations,consider using parallel::detectCores()Valuethe number of hits for each vector of ranks,plus2last values of the vector:number of ties and number of sub-samples really used.Author(s)Jerome Colletdesscop5 Exampleslon<-30a<-2.85x<-rnorm(lon)y=a*x^2+rnorm(lon)c=corc0(c(x,y),lon,2,8,1e5,75014)cc0=c(1203,1671,1766,959,1586,1715,1803,1205,1260,1988,2348,1917,3506,2045,1340, 1093,2694,2757,2233,1085,2322,1793,1569,1263,1709,1747,1512,1308,1778,1354, 1184,1097,2487,2730,2112,1100,2435,2033,1572,1093,1369,1722,1462,1015,1228, 1419,1776,1852,1009,1097,1179,1323,1595,1316,1477,2628,889,1178,1981,4000, 35,840,2091,4467,0,27405)set.seed(75013)lon=30dimension=3sssize=4c0==corc0(rnorm(lon*dimension),lon,dimension,sssize,1e5,75014)desscop Discrete copula graph,a two-dimensional projectionDescriptionDraws a discrete joint probability,for2variables,using bubblesUsagedesscop(copest,xname,yname,normalize=FALSE,axes=TRUE)Argumentscopest the estimated copula(the whole structure resulting from corc)xname the name of the variable we want to put on the horizontal axisyname the name of the variable we want to put on the vertical axisnormalize if TRUE,the smallest probability is rescaled to0,and the largest to1axes if TRUE,puts the name of the variables on the axesAuthor(s)Jerome Collet6desscoptousExampleslon<-31a<-2.85x<-rnorm(lon)y=a*x^2+rnorm(lon)tablo=as.data.frame(cbind(x,y))c=corc(tablo,c("x","y"),8)desscop(c,"x","y")tablo=as.data.frame(cbind(x=rep(0,each=lon),y=rep(0,each=lon)))c=corc(tablo,c("x","y"),8,mixties=TRUE)desscop(c,"x","y")desscoptous Discrete copula graph,ALL two-dimensional projectionsDescriptionDraws a discrete joint probability,for2variables,using bubblesUsagedesscoptous(copest,normalize=FALSE)Argumentscopest the estimated copula(the whole structure resulting from corc)normalize if TRUE,the smallest probability is rescaled to0,and the largest to1Author(s)Jerome ColletExampleslon<-31a<-2.85x<-rnorm(lon)y=a*x^2+rnorm(lon)z=rnorm(lon)tablo=as.data.frame(cbind(x,y,z))c=corc(tablo,c("x","y","z"),8)desscoptous(c)estimdep7 estimdep Dependence estimationDescriptionFrom a set of observations,builds a description of the multivariate distributionUsageestimdep(dataframe,varnames,subsampsize,nbsafe=5,mixties=FALSE,nthreads=2)Argumentsdataframe a data frame containing the observationsvarnames the name of the variables we want to estimate the multivariate distribution subsampsize the sub-sample sizenbsafe the ratio between the discretized copula size and the number of sub-samples mixties if TRUE,put equal weight on tied values,using random permutationsnthreads number of number of threads,assumed to be strictly positive.For"full throttle"computations,consider using parallel::detectCores()Valuethe description of the dependence,it is an object with the following parts:cop the array representing the discretized copulamargins the matrix representing the margins,estimated using kernel density estimation varnames the names of the variablesAuthor(s)Jerome ColletExampleslon=3000plon=3000subsampsize=20##############x=(runif(lon)-1/2)*3y=x^2+rnorm(lon)z=rnorm(lon)donori=as.data.frame(cbind(x,y,z))depori=estimdep(donori,c("x","y","z"),subsampsize)knownvalues=data.frame(z=rnorm(plon))8predictdepprev<-predictdep(knownvalues,depori)plot(prev$x,prev$y,xlim=c(-2,2),ylim=c(-2,5),pch=20,cex=0.5)points(donori[,1:2],col= red ,pch=20,cex=.5)knownvalues=data.frame(x=(runif(lon)-1/2)*3)prev<-predictdep(knownvalues,depori)plot(prev$x,prev$y,xlim=c(-2,2),ylim=c(-2,5),pch=20,cex=0.5)points(donori[,1:2],col= red ,pch=20,cex=.5)knownvalues=data.frame(y=runif(plon,min=-2,max=4))prev<-predictdep(knownvalues,depori)plot(prev$x,prev$y,xlim=c(-2,2),ylim=c(-2,5),pch=20,cex=0.5)points(donori[,1:2],col= red ,pch=20,cex=.5)predictdep Probability forecastingDescriptionFrom a set of incomplete observations,and a description of the dependence,provides simulated values of the unknown coordinates.It is also possible to simulate unconditionally,with empty observations.Usagepredictdep(knownvalues,dependence,smoothing=c("Uniform","Beta"),nthreads=2) Argumentsknownvalues in case of conditional simulation,a matrix containing incomplete observations, the known coordinates being the same for all observations.If no variable namein knwonvalues appears in dependence$varnames,then the simulation is un-conditional.dependence the description of the dependence we want to use to forecast,as built by function estimdepsmoothing the smoothing method for input and output ranks.nthreads number of number of threads,assumed to be strictly positive.For"full throttle"computations,consider using parallel::detectCores()Valuethe matrix of the completed observationsAuthor(s)Jerome ColletExampleslon=100plon=100subsampsize=10shift=0noise=0knowndims=1x=rnorm(lon)y=2*x+noise*rnorm(lon)donori=as.data.frame(cbind(x,y))depori=estimdep(donori,c("x","y"),subsampsize)##knownvalues=data.frame(x=rnorm(plon)+shift)prev<-predictdep(knownvalues,depori)##plot(prev$x,prev$y,xlim=c(-2,2),ylim=c(-2,5),pch=20,cex=0.5)points(donori[,1:2],col= red ,pch=20,cex=.5)##knownvalues=data.frame(x=rnorm(plon)+shift)prev<-predictdep(knownvalues,depori,smoothing="Beta")##plot(prev$x,prev$y,xlim=c(-2,2),ylim=c(-2,5),pch=20,cex=0.5)points(donori[,1:2],col= red ,pch=20,cex=.5)#souci normal si|shift|>>1knownvalues=data.frame(z=rnorm(plon)+shift)prev<-predictdep(knownvalues,depori)##plot(prev$x,prev$y,xlim=c(-2,2),ylim=c(-2,5),pch=20,cex=0.5)points(donori[,1:2],col= red ,pch=20,cex=.5)predonfly Probability forecastingDescriptionFrom two sets of observations,first one of complete observations and second one of incomplete observations,provides simulated values of the unknown coordinates.Usagepredonfly(completeobs,incompleteobs,varnames,subsampsize,nbpreds=1,mixties=FALSE, maxtirs=1e5,complete=TRUE,nthreads=2)Argumentscompleteobs the set of complete observations.incompleteobs the set of incomplete observations.varnames the modeled variables.subsampsize the sub-sample size.nbpreds the number of predictions for each incomplete observation.mixties if TRUE,should put equal weight on tied values,using random permutations(not yet implemented)maxtirs the maximum number of sub-samples,to stop the computation even if they did not provide nbpreds predictions for each incomplete observation.complete If TRUE,predictions are completed with incomplete observationsnthreads number of number of threads,assumed to be strictly positive.For"full throttle"computations,consider using parallel::detectCores()Valuethe matrix of the completed observationsAuthor(s)Jerome ColletExampleslon=100plon=30subsampsize=10x=rnorm(lon)y=2*x+rnorm(lon)*0donori=as.data.frame(cbind(x,y))##knownvalues=data.frame(x=rnorm(plon))prev<-predonfly(donori,knownvalues,c("x","y"),subsampsize,100)##plot(prev$x,prev$y,pch=20,cex=0.5,ylim=range(c(prev$y,donori$y),na.rm=TRUE),xlim=range(c(prev$x,donori$x))) points(donori[,1:2],col= red ,pch=20,cex=.5)lon=3000mg=20dimtot=4rayon=6genboules<-function(lon,a,d){ss<-function(vec)simany11 {return(sum(vec*vec))}surface=matrix(nrow=lon,ncol=d,data=rnorm(lon*d))rayons=sqrt(apply(surface,1,ss))surface=surface/rayonsreturn(matrix(nrow=lon,ncol=d,data=rnorm(lon*d))+a*surface)}##############donori=genboules(lon,rayon,dimtot)donori=as.data.frame(donori)dimconnues=3:dimtotvalconnues=matrix(nrow=1,ncol=length(dimconnues),data=0)valconnues=as.data.frame(valconnues)names(valconnues)=names(donori)[3:dimtot]prev<-predonfly(donori,valconnues,names(donori),subsampsize,100)boule2=genboules(plon,rayon,2)plot(boule2[,1:2],xlab= X1 ,ylab= X2 ,pch=20,cex=.5)plot(prev$V1,prev$V2,xlab= X1 ,ylab= X2 ,pch=20,cex=.5)simany Test statistic distribution under any hypothesisDescriptionSimulates the test statistic,under independenceUsagesimany(sampsize,dimension,subsampsizes,sampnum,nbsafe=5,nthreads=2,fun=NULL,...) Argumentssampsize sample sizedimension sample dimensionsubsampsizes vector of sub-sample sizessampnum number of samplesnbsafe the ratio between the number of sub-samples and the cardinality of the dis-cretized copula.nthreads number of number of threads,assumed to be strictly positive.For"full throttle"computations,consider using parallel::detectCores()fun the function describing the dependence....optional arguments to funValuelrs the distances with independent caselrs2mean the distances with theoretical value,given dependence funscarcities the proportions of non-reached vector ranksDistTypes a recall of the list of the distance types:"KL","L2","L1","APE"Author(s)Jerome ColletExamplesdepquad<-function(lon,dd,a){x<-rnorm(lon)y0<-a*x^2y<-y0+rnorm(lon)reste=rnorm((dd-2)*lon)return(c(x,y,reste))}sims0=simany(101,3,8,50,nbsafe=1)seuils=apply(sims0$lrs,3,quantile,0.95)seuils=matrix(ncol=4,nrow=50,seuils,byrow=TRUE)sims1=simany(101,3,8,50,nbsafe=1,fun=depquad,a=0.5)apply(sims1$lrs[,1,]>seuils,2,mean)simnul Test statistic distribution under independence hypothesisDescriptionSimulates the test statistic,under independenceUsagesimnul(sampsize,dimension,subsampsizes,sampnum,KL=TRUE,nbsafe=5,nthreads=2) Argumentssampsize sample sizedimension sample dimensionsubsampsizes vector of sub-sample sizessampnum number of samplesKL if TRUE,returns the Kullback-Leibler divergence with the independent case,if FALSE,the L2distance.There is no re-normalization,contrary to what happensfor simany.nbsafe the ratio between the number of sub-samples and the cardinality of the dis-cretized copula.nthreads number of number of threads,assumed to be strictly positive.For"full throttle"computations,consider using parallel::detectCores()Valuelrs the distances with independent casescarcities the proportions of non-reached vector ranksAuthor(s)Jerome ColletExampleslibrary(datasets)#plot(swiss)c=corc(swiss,1:3,8)cRV=sum(c$cop*log(c$cop),na.rm=TRUE)+3*log(8)sims=simnul(47,3,8,100)pvalue=mean(RV<sims$lrs)pvalueRVsummary(sims$lrs)Indexcorc,3,5,6corc0,4desscop,5desscoptous,6estimdep,7,8predictdep,8predonfly,9simany,11simnul,12subrank(subrank-package),2subrank-package,214。

DIN EN 1714-2002 英文

DIN EN 1714-2002 英文

European Standard EN 1714:1997 + A1:2002 has the status of a DIN Standard.National forewordThis standard has been prepared by CEN/TC 121.The responsible German body involved in its preparation was the Normenausschuss Materialprüfung (Ma-terials Testing Standards Committee), Technical Committee Ultraschallprüfung.AmendmentsThis standard differs from the October 1997 edition in that it has been editorially revised.Previous editionsDIN 54125: 1989-01; DIN EN 1714: 1997-10.ICS 25.160.40Zerstörungsfreie Prüfung von Schweißverbindungen –Ultraschallprüfung von Schweißverbindungen (enthält Änderung A1:2002)Ref.No.DIN EN 1714:2002-09English price group 13Sales No.111303.03DEUTSCHE NORMSeptember 2002EN 1714{EN comprises 25 pages.©No part of this standard may be reproduced without the prior permission ofDIN Deutsches Institut für Normung e.V., Berlin. Beuth Verlag GmbH , 10772Berlin, Germany,has the exclusive right of sale for German Standards (DIN-Normen).Non-destructive testing of weldsUltrasonic testing of welded joints(includes Amendment A1)English version of DIN EN 1714:1997 + A1:2002SupersedesOctober 1997 edition.N o r m e n -D o w n l o a d -B e u t h -A l s t o m P o w e r G e n e r a t i o n A G -K d N r .5281078-L f N r .2366787001-2004-04-19 14:01N o r m e n -D o w n l o a d -B e u t h -A l s t o m P o w e r G e n e r a t i o n A G -K d N r .5281078-L f N r .2366787001-2004-04-19 14:01English versionICS 25.160.40Management Centre: rue de Stassart 36, B-1050 BrusselsEuropean Committee for Standardization Comit é Europ éen de Normalisation Europ äisches Komitee f ür NormungNon-destructive testing of weldsUltrasonic testing of welded joints(includes Amendment A1)©2002.CEN –All rights of exploitation in any form and by any means reserved worldwide for CEN national members.Ref. No. EN 1714:1997+A1:2002EContr ôle non destructif des assem-blages soud és – Contr ôle par ultra-sons des assemblages soud és (Amendement A1 inclus)Zerst örungsfreie Pr üfung vonSchwei ßverbindungen – Ultraschall-pr üfung von Schwei ßverbindungen (enth ält Änderung A1:2002)This European Standard was approved by CEN on 1997-08-02. Amendment A1was approved by CEN on 2002-05-01.CEN members are bound to comply with the CEN/CENELEC Internal Regulations which stipulate the conditions for giving this European Standard the status of a national standard without any alteration.Up-to-date lists and bibliographical references concerning such national standards may be obtained on application to the Management Centre or to any CEN member.The European Standards exist in three official versions (English, French, German).A version in any other language made by translation under the responsibility of a CEN member into its own language and notified to the Management Centre has the same status as the official versions.CEN members are the national standards bodies of Austria, Belgium, the Czech Republic, Denmark, Finland, France, G ermany, G reece, Iceland, Ireland, Italy,Luxembourg, Malta, the Netherlands, Norway, Portugal, Spain, Sweden, Switzer-land, and the United Kingdom.ÈÉËEN 1714 October 1997+A1M ay 2002N o r m e n -D o w n l o a d -B e u t h -A l s t o m P o w e r G e n e r a t i o n A G -K d N r .5281078-L f N r .2366787001-2004-04-19 14:01Page 2EN 1714:1997+A1:2002PageForeword to EN 1714:1997 . . . . . . . . . . . . . . . 2Foreword to EN 1714:1997/A1:2002 . . . . . . 21Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32Normative references . . . . . . . . . . . . . . . . . 33Definitions and symbols . . . . . . . . . . . . . . . 34General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45Information required prior to testing . . . . 45.1Items to be specified . . . . . . . . . . . . . . . . . 45.2Specific information required prior totesting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45.3Written test procedure . . . . . . . . . . . . . . . . 56Requirements for personnel andequipment . . . . . . . . . . . . . . . . . . . . . . . . . . . 56.1Personnel qualifications . . . . . . . . . . . . . . . 56.2Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . 56.3Probe parameters . . . . . . . . . . . . . . . . . . . . 56.3.1Frequency . . . . . . . . . . . . . . . . . . . . . . . . 56.3.2Angles of incidence . . . . . . . . . . . . . . . . . 56.3.3Adaption of probes to curvedscanning surfaces . . . . . . . . . . . . . . . . . .57Testing volume . . . . . . . . . . . . . . . . . . . . . . . 68Preparation of scanning surfaces . . . . . . . 69Parent metal testing . . . . . . . . . . . . . . . . . . 610Range and sensitivity setting . . . . . . . . . 710.1General . . . . . . . . . . . . . . . . . . . . . . . . . . . 710.2Reference level . . . . . . . . . . . . . . . . . . . . . 7Page10.3Evaluation levels . . . . . . . . . . . . . . . . . . . . 810.4Transfer correction . . . . . . . . . . . . . . . . . . 810.5Signal-to-noise ratio . . . . . . . . . . . . . . . . 811Testing levels . . . . . . . . . . . . . . . . . . . . . . . 812Testing technique . . . . . . . . . . . . . . . . . . . 912.1General . . . . . . . . . . . . . . . . . . . . . . . . . . . 912.2Manual scan path . . . . . . . . . . . . . . . . . . . 912.3Testing for imperfections perpendicularto the test surface . . . . . . . . . . . . . . . . . .912.4Location of indications . . . . . . . . . . . . . . 912.5Evaluation of indications . . . . . . . . . . . . . 912.5.1General . . . . . . . . . . . . . . . . . . . . . . . . . . 912.5.2Maximum echo amplitude . . . . . . . . . . 912.5.3Indication length . . . . . . . . . . . . . . . . . . 1012.5.4Indication height . . . . . . . . . . . . . . . . . . 1012.5.5Characterization of imperfections . . . . 1013Test report . . . . . . . . . . . . . . . . . . . . . . . . . 1013.1General . . . . . . . . . . . . . . . . . . . . . . . . . . . 1013.2General data . . . . . . . . . . . . . . . . . . . . . . . 1013.3Information relating to equipment . . . . . . 1013.4Information relating to testingtechnique . . . . . . . . . . . . . . . . . . . . . . . . .1013.5Results of testing . . . . . . . . . . . . . . . . . . .11Annex A (normative)Testing levels forvarious types of welded joints . . . . . . . . 11Annex ZA (informative)Clauses of thisEuropean Standard addressing essential requirements or otherprovisions of EU directives . . . . . . . . . . . 25ContentsForeword to EN 1714:1997This European Standard has been prepared by CEN/TC 121 ‘Welding ’, the Secretariat of which is held by DS.This European Standard has been prepared under a mandate given to CEN by the Commission of European Communities and the European Free Trade Association and supports essential requirements of the relevant EC Directive. For relationship with this directive, see Annex ZA.This European Standard shall be given the status of a national standard, either by publication of an identical text or by endorsement, and conflicting national standards withdrawn, by February 1998 at the latest.In accordance with the CEN/CENELEC Internal Regulations, the following countries are bound to implement this European Standard:Austria, Belgium, the Czech Republic, Denmark, Finland, France, G ermany, G reece, Iceland, Ireland, Italy,Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, and the United Kingdom.Foreword to EN 1714:1997/A1:2002This Amendment to EN 1714:1997 has been prepared by Technical Committee CEN/TC 121 ‘Welding ’, the Secretariat of which is held by DS.This Amendment has been prepared under a mandate given to CEN by the European Commission and the European Free Trade Association, and supports essential requirements of the relevant EC Directive. For rela-tionship with this directive, see Annex ZA.This Amendment shall be given the status of a national standard, either by publication of an identical text or by endorsement, and conflicting national standards withdrawn, by November 2002 at the latest.In accordance with the CEN/CENELEC Internal Regulations, the following countries are bound to implement this European Standard:N o r m e n -D o w n l o a d -B e u t h -A l s t o m P o w e r G e n e r a t i o n A G -K d N r .5281078-L f N r .2366787001-2004-04-19 14:01Page 3EN 1714:1997+A1:2002Austria, Belgium, the Czech Republic, Denmark, Finland, France, G ermany, G reece, Iceland, Ireland, Italy,Luxembourg, Malta, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, and the United Kingdom.1ScopeThis European Standard specifies methods for the manual ultrasonic testing of fusion welded joints in metallic materials equal to and above 8 mm thick which exhibit low ultrasonic attenuation (especially that due to scatter).It is primarily intended for use on full-penetration welded joints where both the welded and parent material are ferritic.The techniques may also be used:–on materials other than those stated,–on partial-penetration welds,–with automated equipment,if required by specification.Where material-dependent ultrasonic values are specified in this standard, they are based on steel having an ultrasonic sound velocity of (5920 t 50) m/s for longitudinal waves, and (3255 t 30)m/s for transverse waves.This is to be taken into account when examining materials with a different velocity.The standard specifies four testing levels, each corresponding to a different probability of detection of imper-fections. Guidance on the selection of testing levels A, B and C is given in Annex A. The requirements of the fourth testing level, which is intended for use in special applications, shall be in accordance with the general requirements of this standard and may only be used when defined by specification.This standard may be used for the assessment of indications, for acceptance purposes, by either of the following methods:1)evaluation based primarily on length and echo amplitude of the signal indication;2)evaluation based on characterization and sizing of the indication by probe movement methods.The methods used shall be specified.2Normative referencesThis European Standard incorporates by dated or undated references provisions from other publications. These normative references are cited at the appropriate places in the text and the publications are listed as follows.For dated references, subsequent amendments to or revisions of any of these publications apply to this European Standard only when incorporated in it by amendment or revision. For undated references, the latest edition of the publication referred to applies:EN 473Qualification and certication of NDT personnel – General principlesEN 583-1Non-destructive testing – Ultrasonic examination – Part 1: General principlesEN 583-2Non-destructive testing – Ultrasonic examination – Part 2: Sensitivity and range setting EN 1330-4Non-destructive testing – Terminology – Part 4: Terms used in ultrasonic testingEN 1712Non-destructive testing of welds – Ultrasonic testing of welded joints – Acceptance levelsEN 1713Non-destructive testing of welds – Ultrasonic testing – Characterization of imperfections in welds EN 12062Non-destructive examination of welds – General rules for metallic materialsEN 25817Arc-welded joints in steel – Guidance on quality levels for imperfections (ISO 5817:1992)3Definitions and symbolsFor the purposes of this standard, the definitions given in EN 1330-4 and EN 12062 apply.For quantities and symbols, see table 1.Indications shall be considered to be either longitudinal or transverse, depending on the direction of their major dimension with respect to the weld axis, x , in accordance with figure 2.N o r m e n -D o w n l o a d -B e u t h -A l s t o m P o w e r G e n e r a t i o n A G -K d N r .5281078-L f N r .2366787001-2004-04-19 14:01Page 4EN 1714:1997+A1:2002Table 1:Quantities and symbolsSymbolQuantityUnit t Thickness of parent material (thinnest part)mm D DSR Diameter of disc-shaped reflector mm D SDHDiameter of side-drilled hole mm l Length of indicationmm h Extension of indication in depth direction mm x Position of indication in longitudinal direction mm y Position of indication in transverse direction mm z Position of indication in depth direction mm l z Projected length of indication in depth direction mm l x Projected length of indication in x -direction mm l y Projected length of indication in y -direction mm pFull skip distancemm4GeneralThe purpose of this standard is to describe general methods of ultrasonic testing, using standard criteria, for the most commonly used welded joints. The requirements specified this standard cover the equipment, prepa-ration, performance of the test and reporting. The parameters specified, in particular those for the probes, are compatible with the requirements of EN 1712 and EN 1713, and are also suitable for use with other acceptance criteria standards. The techniques recommended in this standard are suitable for the detection of weld imper-fections as specified in typical weld acceptance standards. Methods to be used for ultrasonic assessment of indications and acceptance criteria shall be defined by specification.If the agreed acceptance criteria require a more precise determination of the height and nature of the defect (e.g.when fitness-for-purpose criteria are applied), this may necessitate the use of techniques or methods outside the scope of this standard.5Information required prior to testing5.1Items to be specified–Method for setting the reference level–Method to be used for evaluation of indications –Acceptance levels –Testing level–Manufacturing and operation stage(s) at which the testing is to be carried out –Qualification of personnel–Scope of testing for transverse indications –Requirements for tandem testing–Parent metal testing prior to and/or after welding –Whether or not a written test procedure is required –Requirements for written test procedures 5.2Specific information required prior to testingBefore any testing of a welded joint can begin, the operator shall have access to the following essential information:–written test procedure, if required (see 5.3);–type(s) of parent material and product form (i.e. cast, forged, rolled);–manufacturing or operation stage at which testing is to be carried out including any heat treatment;–time and extent of any post-weld heat treatment;N o r m e n -D o w n l o a d -B e u t h -A l s t o m P o w e r G e n e r a t i o n A G -K d N r .5281078-L f N r .2366787001-2004-04-19 14:01Page 5EN 1714:1997+A1:2002–joint preparation and dimension;–requirement for surface condition;–welding procedure or relevant information on welding process;–reporting requirements;–acceptance levels;–scope of testing, including requirements for transverse indications, if relevant;–testing level;–personnel qualification level;–procedures for corrective action when unacceptable indications are revealed.5.3Written test procedureThe definitions and requirements given in this standard will normally satisfy the need for a written procedure.Where this is not the case, or where the techniques described in this standard are not applicable to the welded joint to be examined, additional written procedures shall be used, if required by specification.6Requirements for personnel and equipment6.1Personnel qualificationsPersonnel carrying out testing in accordance with this standard shall be qualified to an appropriate level in accordance with EN 473 or equivalent in the relevant industrial sector.In addition to a general knowledge of ultrasonic weld inspection, they shall also be familiar with testing problems specifically associated with the type of welded joints to be examined.6.2EquipmentAny equipment used in conjunction with this standard shall comply with the requirements of relevant European Standards. Prior to the publication of EN Standards related to the subject, the corresponding national stand-ards may be used.6.3Probe parameters6.3.1FrequencyThe frequency shall be within the 2MHz to 5MHz range and shall be selected to comply with the specified acceptance levels.For initial testing, the frequency shall be as low as possible, within the above range, when the evaluation is carried out according to acceptance levels based on length and amplitude (e.g. EN 1712). Higher frequencies may be used to improve range resolution if this is necessary when using standards for acceptance levels based on the characterization of imperfections (e.g. EN 1713).Frequencies of approximately 1MHz may be used for testing at long sound paths where the material shows above average attenuation.6.3.2Angles of incidenceWhen testing is carried out with transverse waves and techniques that require the ultrasonic beam to be reflected from an opposite surface, care shall be taken to ensure that the incident angle of the beam, with the opposite reflecting surface, is not less than 35° and preferably not greater than 70°. Where more than one probe angle is used, at least one of the angle probes used shall conform with this requirement. One of the probe angles used shall ensure that the weld fusion faces are examined at, or as near as possible to, normal incidence. When the use of two or more probe angles is specified, the difference between the nominal beam angles shall be 10°or greater.Angles of incidence at the probe and opposite reflecting surface, when curved, may be determined by drawing a sectional view of the weld or in accordance with the methods given in EN 583-2. Where angles of incidence cannot be determined as specified by this standard, the test report shall include a comprehensive description of the scans used and the extent of any incomplete coverage caused together with an explanation of the difficulties encountered.6.3.3Adaption of probes to curved scanning surfacesThe gap between test surface and bottom of the probe shoe shall not be greater than 0,5 mm. For cylindrical or spherical surfaces, this requirement will normally be met when the following equation is fulfilled:D ö 15 awhereD is the diameter of the component, in mm;a is the dimension of the probe shoe in the direction of testing, in mm.N o r m e n -D o w n l o a d -B e u t h -A l s t o m P o w e r G e n e r a t i o n A G -K d N r .5281078-L f N r .2366787001-2004-04-19 14:01Page 6EN 1714:1997+A1:2002If this requirement cannot be met the probe shoe shall be adapted to the surface and the sensitivity and range shall be set accordingly.7Testing volumeThe testing volume (see figure 1) is defined as the zone which includes weld and parent material for at least 10mm on each side of the weld, or the width of the heat-affected zone, whichever is greater.In all cases, scanning shall cover the whole testing volume. If individual sections of this volume cannot be covered in at least one scanning direction, or if the angles of incidence with the opposite surface do not meet the requirements specified in 6.3.2, alternative or supplementary ultrasonic techniques or other non-destructive methods shall be agreed upon. This may, in some cases, require removal of any excess weld metal.Supplementary methods may require testing using twin crystal angle beam probes, creeping wave probes,further ultrasonic techniques or any other suitable method (e.g. liquid penetrant, magnetic particle, radio-graphic testing). In selecting alternative or supplementary methods, due consideration should be given to the type of weld and probable orientation of any imperfections to be detected.8Preparation of scanning surfacesScanning surfaces shall be wide enough to permit the testing volume (see figure 1) to be fully covered. Alter-natively the width of the scanning surfaces may be smaller if equivalent coverage of the testing volume can be achieved by scanning from both the upper and the lower surface of the joint.Scanning surfaces shall be even and free from foreign matter likely to interfere with probe coupling (e.g. rust,loose scale, weld spatter, notches, grooves). Waviness of the test surface shall not result in a gap between the probe and test surfaces greater than 0,5mm. These requirements shall be ensured by dressing if necessary.Local variations in surface contour (e.g. along the edge of the weld), which result in a gap beneath the probe of up to 1mm, is only permitted if at least one additional probe angle is employed from the affected side at the weld. This additional scanning is necessary to compensate for the reduced weld coverage that will occur with a gap of this dimension.In all cases, the maximum permitted gap on areas of the test surface from which the evaluation of indications is to be carried out shall be 0,5mm. Scanning surfaces and surfaces from which the sound beam is reflected may be assumed to be satisfactory if the surface roughness, R a , is not greater than 6,3µm for machined surfaces, or not greater than 12,5µm for shotblasted surfaces.9Parent metal testingThe parent metal, in the scanning zone area, shall be examined with straight beam probes prior to or after welding, unless it can be demonstrated (e.g. by testing during the fabrication process) that the angle probe testing of the weld will not be influenced by the presence of the imperfections or high attenuation.Where imperfections are found their influence on the proposed angle beam testing shall be assessed and, if necessary, the techniques adjusted correspondingly. When satisfactory coverage by ultrasonic testing is se-riously affected, by agreement, other inspection methods (e.g. radiography) may need to be considered.N o r m e n -D o w n l o a d -B e u t h -A l s t o m P o w e r G e n e r a t i o n A G -K d N r .5281078-L f N r .2366787001-2004-04-19 14:01Page 7EN 1714:1997+A1:20021Width of testing volume 2Scanning surfaceFigure 1:Example of testing volume to be covered when scanning for longitudinal indications10Range and sensitivity setting10.1GeneralSetting of range and sensitivity shall be carried out prior to each test in accordance with this standard and EN 583-2. Checks to confirm these settings shall be performed at least every four hours and on completion of the test. Checks shall also be carried out whenever a system parameter is changed or changes in the equivalent settings are suspected.If deviations are found during these checks, the corrections given in table 2 shall be carried out.Table 2:Sensitivity and range correctionsSensitivity1Deviations ß 4dBThe setting shall be corrected before testing is continued.2Reduction of the sensitivity > 4 dB Setting shall be corrected and all tests carried out with the equipment over the previous period shall be repeated.3Increase in sensitivity > 4 dBSetting shall be corrected and all recorded indications shall be reassessed.Range1Deviations ß 2% of the range The setting shall be corrected before testing is continued.2Deviations > 2% of the rangeSetting shall be corrected and testing carried out with the equipment over the previous period shall be repeated.10.2Reference levelOne of the following methods for setting reference levels shall be used:–method 1: The reference level is a distance-amplitude-curve (DAC-curve) for a 3 mm side-drilled hole;–method 2: The reference levels for transverse and longitudinal waves using the distance gain size (DGS)system based on a disc-shaped reflector (DSR) are given in tables 3 and 4 respectively;–method 3: The reference level is equal to a DAC curve for a 1 mm deep rectangular notch.–tandem testing: D DSR= 6 mm (for all thicknesses).N o r m e n -D o w n l o a d -B e u t h -A l s t o m P o w e r G e n e r a t i o n A G -K d N r .5281078-L f N r .2366787001-2004-04-19 14:01Page 8EN 1714:1997+A1:2002Table 3:Reference levels for angle beam scanning with transverse waves for method 2 (DGS)Nominal probe Thickness of parent material (mm)frequency (MHz)8 ß t < 1515 ß t < 4040 ß t ß 1001,5 to 2,5–D DSR = 2 mm D DSR = 3 mm3 to 5D DSR = 1 mmD DSR = 1,5 mm–Table 4:Reference levels for longitudinal waves for method 2 (DGS)Nominal probe Thickness of parent material (mm)frequency (MHz)8 ß t < 1515 ß t < 4040 ß t ß 1001,2 to 2,5–D DSR = 2 mm D DSR = 3 mm 3 to 5D DSR = 2 mmD DSR = 2 mmD DSR = 3 mm10.3Evaluation levelsAll indications equal to or exceeding the following shall be evaluated:–methods 1 and 3: Reference level –10 dB (33% DAC);–method 2: Reference level –4 dB, in accordance with tables 3 and 4, respectively;–tandem testing: DDSR= 6 mm (for all thicknesses).10.4Transfer correctionWhere separate blocks are used for establishing reference levels, measurements shall be made of the transferdifferences, between test object and block, at a representative number of locations. Suitable techniques are described in EN 583-2.If the differences are less than 2dB, correction is not required.If the differences are greater than 2dB but smaller than 12dB, they shall be compensated for.If transfer losses exceed 12dB, the reason shall be considered and further preparation of the scanning surfaces shall be carried out, if applicable.Where there are no apparent reasons for high correction values, the attenuation, at various locations on the test object, shall be measured and, where it is found to vary significantly, corrective actions need be considered.10.5Signal-to-noise ratioDuring testing of the weld the noise level excluding spurious surface indications shall remain at least 12dB below the evaluation level. This requirement may be made less stringent subject to specification.11Testing levelsQuality requirements for welded joints are mainly associated with the material, welding process and service conditions. To accommodate all of these requirements this standard specifies four testing levels (A, B, C and D).From testing level A to testing level C an increasing probability of detection will be achieved by an increasing test coverage (e.g. number of scans, surface dressing). Testing level D may be agreed for special application using a written procedure which shall take into account the general requirements of this standard.In general, the testing levels are related to quality levels (e.g. EN 25817). The appropriate testing level may be specified in standards for testing welds (e.g. EN 12062), product standards or other documents.When EN 12062 is specified, the recommended testing levels are as given in table 5.Table 5:Recommended testing levels Testing levelQuality level in EN 25817A CB BC By agreement DSpecial applicationN o r m e n -D o w n l o a d -B e u t h -A l s t o m P o w e r G e n e r a t i o n A G -K d N r .5281078-L f N r .2366787001-2004-04-19 14:01Specific requirements for testing levels A to C are given for various types of joints in Annex A. It should be noted that the joint types shown are ideal examples only and where actual welding conditions or accessability do not conform exactly with those shown, the testing technique shall be modified to satisfy the general requirements of this standard and the specific testing level required. For these cases, a written test procedure shall be prepared.12Testing technique 12.1GeneralUltrasonic testing shall be performed in accordance with EN 583-1, taking the following subclauses into ac-count.12.2Manual scan pathDuring angle probe scanning (as illustrated in figure 1) a slight swivelling movement up to and angle of 10° in either side of the nominal beam direction may be applied to the probe.12.3Testing for imperfections perpendicular to the test surfaceSubsurface planar imperfections perpendicular to the test surface are difficult to detect with single angle probe techniques. For such imperfections specific testing techniques should be considered, particularly for welds in thicker materials. Use of these techniques shall be defined by specification.12.4Location of indicationsThe location of all indications shall be defined by reference to a coordinate system (e.g. as shown in figure 2).A point on the test surface shall be selected as the origin for these measurements.Where testing carried out from more than one surface, reference points shall be established on each surface.In this case, care shall be taken to establish a positional relationship between all reference points used, so that the absolute location of all indications can be established from any nominated reference point.In the case of circumferential welds, this may require the establishment of the inner and outer reference points prior to assembly for welding.1Origin Figure 2:Coordinate system for defining the location of indications 12.5Evaluation of indications 12.5.1General All relevant indications above the evaluation level shall be assessed in accordance with clauses 12.5.2 to 12.5.4.12.5.2Maximum echo amplitude The echo amplitude shall be maximized by probe movement and recorded in relation to the agreed reference level.N o r m e n -D o w n l o a d -B e u t h -A l s t o m P o w e r G e n e r a t i o n A G -K d N r .5281078-L f N r .2366787001-2004-04-19 14:01。