Exploring Data Analysis Methods from Simplified Viscoelastic Continuum Damage Fatigue Testing Andrew Croteau, Math Department, The Founders Academy, Manchester, NH Katie Haslett, Department of Civil Engineering, University of New Hampshire, Durham, NH Introduction Analysis Approach Statistical Analysis Reflective Cracking: Prominent distress in asphalt concrete overlays. It occurs due to traffic or thermally-driven movements at joints and cracks in the underlying pavement. Fatigue Cracking: An asphalt pavement distress most often instigated by failure of the surface due to traffic loading. Example: SPWEA440E Mixture Identify inside or outside break Determine trim locations using Nf (number of cycles to failure) Original Nf Nf – 500 Cycles Nf – 1000 Cycles Nf – 1 % of original Nf value Nf – 3 % of original Nf value In this particular mixture, there are 5 different replicates. The replicates in green indicate that there was an inside break and the red replicates indicate an outside break. T-Test performed on 3 different groups of data using a significance level of α = 0.05 What is a t-test? A test used to find evidence of a significant difference between population means (2-tailed t-test). If p < .05, there is a statistical significant difference. If p > .05, there is no statistical significant difference. Inside Break Only: Gr values were not sensitive to data manipulation methods using given the significance level. Dr values were sensitive to data manipulation (500 cycles, 1000 cycles and 3% methods), resulting changes to p-value significance outcomes. Outside Break Only: Gr values were sensitive to data manipulation methods, however, all methods included in this study did not alter the ranking of mixture fatigue performance. Dr values showed slight sensitivity to data manipulation when using a set number of cycles (500 or 1000 cycles trimmed), however were not significantly different when using a percent base manipulation approach (1% or 3% trimmed). Inside Break (Original data) and Outside Break (Trimmed data): Gr values were not sensitive to data manipulation given the significance level. Dr values were sensitive to data manipulation, where 1% method had the least impact and 3% had the highest impact. Study Objectives: Analyze common performance criteria determined from S-VECD fatigue testing using varying data manipulation methods (set number of cycles and a percentage of cycles to failure method). Investigate the impact of data manipulation methods on fatigue performance of mixtures using statistical analysis. Replicate P2B example of results after data trimming: Method Nf GR log(Nf) log(GR) Cum. (1-C) DR Orginial 71710 1.78 4.86 0.25 15566 0.22 500 71160 1.79 4.85 15448 1000 70620 1.81 0.26 15324 1% 70980 1.80 15406 3% 69540 1.84 4.84 15067 Mixture Replicate ID Nf Original Gr Dr SPWEA440E P3B 13360 149.82 0.50 P2A 44100 5.29 0.28 P2B 71710 1.78 0.22 P3-A 29210 35.03 0.46 P1-C 25060 30.35 0.44 Background and Methodology Materials 8 different mixtures with varying properties Aggregate size: NMAS 4.75 mm, 9.5 mm, 12.5 mm and 19 mm Binder type and content: PG 58-28 and PG 58-34 Design air void level: 3 to 5 % Reclaimed asphalt pavement (RAP): 0 to 25 % Simplified Viscoelastic Continum Damage (S-VECD) Fatigue Test Direct tension cyclic fatigue Testing standard: AASHTO TP 107 Test temperatures: ((PGHT-PGLT)/2)-3 ◦C Number of specimens: Typically 4 (1 at each strain level) Outcome: Damage Characteristic Curve (DCC) Results Conclusions For replicates were inside breaks were identified, the data manipulation did not have a significant impact on distinguish of mixture fatigue performance using either criterion. Results from ranking tables and t-tests reveal that the Dr performance criteria is more sensitive to data manipulation compared to Gr. Ranking of mixture fatigue performance using Gr criteria remained constant while ranking based on Dr varied. It is recommended that a percentage based (1%) data trimming method of the total number of cycles to failure be implemented rather then using a set value of cycles (500 or 1000) when an outside failure occurs. This is partially due to the nature of testing at different strain levels (effecting the total number of cycles until failure) and sensitivity results from statistical analysis. Inside Break vs Outside Break Literature Sources https://standards.globalspec.com/std/13054534/AASHTO%20TP%20107 Pavement Interactive https://www.pinterest.com/pin/166070304986940527/ https://engineering.purdue.edu/~ncaupg/Activities/2008/presentations/Al-Qadi%20-%20Reflective%20Crack%20Control.pdf Yizhuang Wang & Y. Richard Kim (2017) Development of a pseudo strain energy-based fatigue failure criterion for asphalt mixtures, International Journal of Pavement Engineering, DOI:  Ranking of mixtures for outside break replicates only Gr Ranking Mixture Name Original 500 Cycles 1000 Cycles 1% 3% SPWED440E 7 SPWED430I 4 SPWEB450E 5 SPWEC440E 3 SPWEB430E 2 SPWEB340C 1 SPWEA440E 8 SPWEB440E 6 Dr Ranking Mixture Name Original 500 Cycles 1000 Cycles 1% 3% SPWED440E 6 SPWED430I 7 SPWEB450E 2 1 SPWEC440E 3 SPWEB430E SPWEB340C 4 5 SPWEA440E 8 SPWEB440E Performance Criteria Gr : The rate of change of the averaged released pseudo strain energy throughout the entire loading history until failure. Dr : The average reduction in pseudo stiffness up to failure. Acknowledgments This research was supported with funding from the National Science Foundation’s grant #1711781 Thank you to Dr. Stephen Hale, Allison Wasiewski and my colleagues of the R.E.T.E. program for your help and guidance. Thank you to Dr. Eshan Dave, Dr Jo Sias, Katie Haslett, Runhua Zhang and everyone in the Asphalt Pavement group for allowing me to take part in research with you. Ranking of mixtures based on Gr performance criteria remains constant Ranking of mixtures based on Dr performance criteria vary slightly Higher Gr and Dr are desirable. Also, the higher the Nf value, the better the resistance to cracking.