AirPAD ® Project

1. Department of Civil Engineering University of Rome “Tor Vergata” Italy AirPAD® Project Airport Pavement Analysisand Design SySCOM Consulting Kteg Engineering ltd Pavement Management Middle East 2009 29-30 march 2009, Dubai UAE UK version, all rights reserved @2009

2. AirPAD - Airport Pavement Analysisand Design Why the need of a reliable prediction model ? Aviation Industry Evolution Latest generation of large civil aircraft is not just heavy, achieving TOW loads up to 750.000kg, but even having complex, multiple-wheel, multiple truck landing gear systems. The latest Boeing B-777-ER version has only two six-wheel landing main landing gears to support a gross weight of up to 350.000kg. The Airbus A380, over 500.000kg, which entered commercial service in 2006, has two six-wheel body gears in addition to two four wheel wing gears, for a total of 20 wheels in the main gear assembly. The complex gear loads applied to airport pavements, runways as well as taxiways and aprons, by these new aircraft types are quite different from the loads applied by the older generation of commercial airplanes. Complex landing gear layouts and wheel load interactions within airport pavement layers introduced the risk of premature failure of pavement layers How to predict the real residual life of a runway originally designed for a limited traffic and actually performing over it’s designed capability?

3. AirPAD - Airport Pavement Analysisand Design Why the need of a reliable prediction model Airports and Traffic Growth In the last few years most European regional airports have been experiencing a dramatic traffic growth, pushed by the low-cost airlines phenomenon. Many low-cost airline bases are former NATO airports turned to perform as international “low-cost hubs”; hence the over-use of former pavements designed for a limited traffic. How to predict the real residual life of a runway originally designed for a limited traffic and actually performing over it’s designed capability? Beside the low-cost airlines, European hubs report a remarkable traffic growth as well. Runways and taxiways have been designed for a different traffic rate and smaller aircrafts. How to predict pavements durability, then? The ability to understand how any airport pavement would sustain the growth of air traffic is very important in terms of maintenance planning and reliability forecast. Not least the commercial agreements with airlines and operators.

4. AirPAD - Airport Pavement Analysisand Design The AirPAD is a Performance-Based Prediction model to forecast when and to what extent an airport pavement section will suffer fatigue cracking distress. The AirPAD performs as a decision support tool; it could be integrated in a set of Life Cycle Cost Analysis tools or used to reverse-engineer and verify pavement sections design at project level.

5. AirPAD - Airport Pavement Analysisand Design AirPAD main features • Is possible to calculate the stress and damage contribution affecting the pavement section by each aircraft type, according to the effective payload configuration operating the airfield. • The algorithm is sensitive to the real climatic condition (temperature, wind, pressure) of the airfield environment along the whole day • Aircrafts wandering distribution is calculated by deviation and spread according by the section path type (runway, taxiway, apron) • As the structure design is fully customisable, is possible to define the mechanical specification and performances of each construction material, referring to local available materials instead of standard laboratory materials The input variables set could be tailored around the real background and performance condition Realistic >> Accurate >> Statistically Reliable

6. AirPAD - Airport Pavement Analysisand Design Basic hypothesis of multi layered elasticity theory • Pavement cross section is represented by overlapped layers set over an elastic half-space • Each layer is assumed as homogenous, isotropic and defined by viscoelastic parameters dependent on loads, time (loading cycle) and temperature • The cumulative damage of the section is calculated by the stress applied on section layers • Traffic loads are assumed to be vertical and directly applied by the landing gears • The affecting strain is the combined contribution of each wheel strain

7. AirPAD - Airport Pavement Analysis and Design AirPAD Algorithm, basic workflow

8. AirPAD - Airport Pavement Analysisand Design Input data set

9. AirPAD - Airport Pavement Analysisand Design AirPAD – Traffic and Operations Definition - NEW PAVEMENT SECTION • average daily operations are distributed along time slots of 3 hours as pavement performances change along daytime temperature gradient • multiple and independent operations definition on different pavement sections • up to three different payload configuration for each aircraft • individual landing gear assembly, including tires pressure, for each aircraft type • no restriction on landing gear assembly layout. • virtually unlimited aircraft repository as the same aircraft could have different layouts according to operating carrier

10. AirPAD - Airport Pavement Analysisand Design AirPAD – Traffic and Operations Definition - CURRENT PAVEMENT SECTION • Pavement sections already in use can record a complex operations history as old aircraft models can have been discontinued operations in former times and/or latest models could operate since a short time only. • Most of pavement design software refer to a “static picture” of airport operations, at least requiring a static traffic growth factor. We know that a “living airport” can experience dramatic changes along its operational life.

11. AirPAD - Airport Pavement Analysisand Design AirPAD – Traffic and Operations Definition - CURRENT PAVEMENT SECTION • pavement lifespan is sorted into six 4-years time frames • is possible to specify the operations related to each aircraft along the whole pavement operational life • traffic growth is now implicit in the operations/year data. • is possible to input the traffic interchange among aircrafts upgrades/dismissing (i.e. B747-400 >>> A380-800)

12. AirPAD - Airport Pavement Analysisand Design AirPAD – Landing gear layout • individual landing gear assembly layout design, fully customizable, including tires pressure, for each aircraft type • virtually unlimited aircraft repository as the same aircraft could have different layouts according to operating carrier

13. AirPAD - Airport Pavement Analysisand Design AirPAD – Climate definition Climate is defined on seasonal basis by main parameters related to the stress-matrix by the E-modulus: solar radiation, wind speed at ground level, air temperature, … (refer to list below) Daily temperature field is calculated over the average temperature on each 3 hours interval, because asphalt concrete behaviour and performances change significantly with temperature variation. The most realistic E-modulus calculated by background conditions improves significantly the model results accuracy and reliability temperature distribution along the layers under different climate conditions is calculated by Barber’s theory of the nonlinear unstable pavement temperature fields of two-dimension layered system.

14. AirPAD - Airport Pavement Analysisand Design AirPAD – Section Layers Definition • The pavement cross section is segmented into 20cm wide stripes to calculate the local stress path along the layers, stripe by stripe; • Normal stress, shear and strain are calculated with the multi-layered elasticity model implemented; • The engineer is free to design all the coated and un-coated courses of the pavement cross-section; • Each layer could be defined by an enhanced set of parameters and variables that fully describe the mechanical performances of asphalt concrete, cement treated aggregates and unbound aggregates; • Bitumen performances can be set for each coated layer (wearing, binder and base), This feature allows to define specific performances introduced by bitumen compounds with chemical additives; • Aggregates domain could be described by detailed Jigs and sieves passing rates.

15. AirPAD - Airport Pavement Analysisand Design Algorithm cycles

16. AirPAD - Airport Pavement Analysisand Design • calculated on seasonal base and/or 8x3-hours time slots on daily base over seasonal average. • temperature at every layer depth is calculated by the validated Barber formula • A) Thermodynamic layout defined by daily average temperature >> 1 value Tm per each temperature wave • B) Thermodynamic layout definedby segmentation of Barber temperature wave into 8x3h time slots >> 8 values Ti / wave Tpav(z,t) = Tag + R + (Ag/2 + 3*R) * F * exp(-C*z) * sin [0.262*t – C*z – arctg(C/(H+C))] Algorithm cycle: Thermodynamic layout characterization

17. AirPAD - Airport Pavement Analysisand Design Algorithm cycle: Dynamic Loads Calculation • Dynamic loads are calculated by: • load distribution on wheels • test point assignment (differential depth) • gears footprint area and pressure • load frequency • residual deformation on tandem assembly

18. AirPAD - Airport Pavement Analysisand Design Algorithm cycle: Bitumen performances definition • mechanical behaviour as elastic-plastic-viscous • |E*| complex elasticity modulus • Poisson |*| complex ratio • performances are related to temperature, load and frequency • Complex Modulus |E*| • French SHELL method • ASPHALT method • |E*| Elasticity modulus is calculated by the French SHELL method and Asphalt method, over the input of parameters: • temperature and climate parameters; • dynamic load by landing gears assembly load distribution and traffic frequency by deviation; • aggregate domain distribution (Asphalt) • asphalt concrete composition and voids % (Shell) • layers friction (within layers) • bitumen performances (additives)

19. AirPAD - Airport Pavement Analysisand Design Algorithm cycle: Unbound Granular Materials (MG) • Hypothesis of non-linear mechanical behaviour • Resilient Modulus MR defines the behaviour of unbound granular materials (MG) as a local Elasticity modulus under load stress Stress tensor deviator of the reversible strain when load is removed The Algorithm assumes the UZAN implementation model MR is related to the stress path verified and calculated on load points, at a specific thermodynamic layout. k1 value range 0 – 3 k 2 value range 0 – 1,5 k 3 value range 0 – -7

20. AirPAD - Airport Pavement Analysisand Design Algorithm cycle: Friction factor within layers • Hypothesis • Adherence between layers is not perfect • Stress tensor not constant Horizontal reaction module at interface t= shear stress at interface Du = horizontal strain at interface • Friction factor at layers interface is related to: • sealant and adhesive layer spread (usually bitumen and additives) • aggregates penetration grade • vertical stress (in competition with shear stress) • section temperature (as is influencing bitumen performances) • dynamic loads

21. AirPAD - Airport Pavement Analysisand Design Algorithm cycle: Aircraft wandering distribution • Hypothesis • aircraft direction is not constant • The pavement cross section is segmented into 20cm wide stripes to calculate the local stress path along the layers, stripe by stripe • cross distribution of trajectories is assumed Normal • the effective position of each wheel from the centre of the gear assembly and aircraft axis is approximated to 20cm • Assumptions • standard deviation of 14m-18m on runways take-off area • standard deviation of 18m-22m on runways landing area • standard deviation up to 20m-26m on fast exit taxiways

22. AirPAD - Airport Pavement Analysisand Design Algorithm cycle: Cumulative damage calculation The Miner criteria is adopted to calculate the cumulative damage ni = load cycles on stress state - i Ni = total number of load cycles on stress state –i at fractured stage Nmax is related to construction materials used and is assumed the FAA formula: Asphalt Concrete (surface) EA= elastic modulus asphalt concrete eh = horizontal strain at lower bound Unbound Granular Materials (MG) (base and subgrade) Stabilised Granular Materials (MC) (base) Esub= Resilient modulus Mr subgrade ev = vertical strain at subgrade layer upper bound e= specific horizontal traction strain at stabilised layer lower bound (e 0.0001576).

23. AirPAD - Airport Pavement Analysisand Design AirPAD model calibration Unbound Granular Materials (MG) Regression parameters calibration k1, k2, k3 used to calculate MR Base layer k1 = 2,410; k2 = 0,360; k3 = - 0,400 Subgrade k1 = 0,946; k2 = 0,163; k3 = - 0,419 Average MR would be comparable with the value result of laboratory test, at same stress condition.

24. AirPAD - Airport Pavement Analysisand Design AirPAD model calibration Equivalence factor for cement bounded granular layers The FAA protocol introduces an equivalence factor [coef.eq.FAA] from the MG base layer to the MC base layer, by range [1,6-2,3] The AirPAD is introducing instead the modulus fcp to measure the compression strength of the base layer cement bounded granular materials when calculating the resilient Mr of fractured layers: The calibration is required to find a correlation between the strenght of the fractured cement bounded granular layers, modulus fcp, and the value of the [coef.eq.FAA] Cumulative damage (D) vs modulus fcp, with coef.eq.FAA = 1,6 A comprehensive set of test sessions were performed with values [coef.eq.FAA] as 1,6-1,9-2,3 . Each series was performing a further 3-steps test with progressive values of modulus fcp A regression analysis found a convergent mean value of modulus fcp (at D-stress=0) with the correspondent coef.eq.FAA assumed, at different operations/year rates.

25. AirPAD - Airport Pavement Analysisand Design AirPAD model calibration Equivalence factor for cement stabilised granular layers (MC) By regression analysis is found a functional correlation between the FAA equivalent factor, the FCP modulus and the load on landing gears, sorted by aircraft type. Generic multi-regression analysis equation: as z = modulus fcp (coeff.eq.FAA) ; x = coef.eq.FAA ; y = (load on gear). Values must comply with the following equations: The multi-regression analysis sorted the following correlation:

26. AirPAD - Airport Pavement Analysisand Design AirPAD model calibration K-parameter of friction factor within layers The FAA protocol still ignores a critical friction factor within layers A comparative test session with FAA standard results (sum of squares of residuals mean of miner curves up to 20 years lifespan ) is performed to investigate results variation and accuracy with assigned K-values Cumulative damage vs effective friction factor Convergent results are reached with friction factor multiplier k = 6. Higher values reduce results stability and accuracy Sum of squares (residuals) at friction factor variation

27. AirPAD - Airport Pavement Analysisand Design AirPAD – Algorithm results • verify the most affected cross section stripe by dynamic loads and traffic • stress-strain matrix at all layers • cumulative damage on base and subbase layers, sorted by year and aircraft type contribution • pavement section lifetime forecast according to operations planned cumulative damage (Miner) contribution on cross section by B747-400 operations on runway (take off area)

28. AirPAD - Airport Pavement Analysisand Design Comparison with other methods/softwares • It’s interesting to compare features and flexibility with the upcoming APSDS-5 platform, the closest to the AirPAD: • The AirPAD is sorting the cross section into 20cm wide control stripes in order to gain a complete and accurate stress-strain matrix along the cross section and layers. • The stress-deformation matrix is calculated at each stripe, on each layer (1/3h) resolving the stress-strain tensor of the multi layered elasticity equationsto a convergence. This implies more calculation cycles to minimise the residual error. • The APSDS doesn't take count of temperature (as the FAA as well) and it's variation along days and seasons. • The traffic is not sorted by the day (different time slots mean operations at different temperature). • The speed (load frequency) is not considered as variable with the traffic and the section position. • The AirPAD refers to the formulation from Asphalt Institute ( ref. to AASHTO 2000) and SHELL in every point of the cross section, at different layers depth. • The APSDS ignores the residual strain of a tandem gear assembly between the 1-wheel unload and 2-wheel dynamic load, when calculating (if even does it) the stress tensor. • Friction factor between the layers: the APSDS assumes the adherence as perfect. Instead, the AirPAD introduces different values according to the real behaviour. • Any other algorithm allow to define exactly the materials specifications and performances

29. AirPAD - Airport Pavement Analysisand Design AirPAD integration and improvement • The AirPAD Algorithm is structured as a set of interrelated modules linked by a functional "backbone" • Modules could be updated or calibrated as needed to be closer to airports/operations’ requirements • Modules could be re-engineered or according to the latest scientific improvements • Modules could be added and integrated with other APMS platforms

30. AirPAD - Airport Pavement Analysisand Design Case Study at FCO Airport Aim of the simulation (year 2006) is the investigation of the potential pavement damage and durability reduction consequent to the future replacement of most B747-400 actually operating in the FCO International Airport of Roma (Italy) by the wider and heavier Airbus A380 The section investigated is the mid-section of the Runway 25, actually operating the 91% of all takeoff traffic in the airport. Section layers structure and materials have been recovered by original plans and on-site surveys.

31. AirPAD - Airport Pavement Analysisand Design Case Study at FCO Airport Actual scenario on Runway 25 Actual traffic distribution on Runway 25

32. AirPAD - Airport Pavement Analysisand Design Case Study at FCO Airport Actual scenario on Runway 25 Cumulative Damage (average Temperature on seasonal base) – Base Layer

33. AirPAD - Airport Pavement Analysisand Design Case Study at FCO Airport Actual scenario on Runway 25 Cumulative damage (8x3hours time slot temperature) – Base Layer

34. AirPAD - Airport Pavement Analysisand Design Case Study at FCO Airport Simulated scenario on Runway 25 The simulated scenario introduces the A380 replacing the 60% of the traffic operated by the B747-300/400, total of 4026 takeoff/year, and the B747-300/400 replacing the 30% of traffic operated by the B767-300, total of 3389 takeoff/year. The traffic operated by the B767-300 is then reduced to 4937 takeoff/year. The residual traffic distribution is assumed as steady for the following years (this latest unrealistic hypothesis is necessary to separate the effect of potentially different traffic contributions)

35. AirPAD - Airport Pavement Analysisand Design Case Study at FCO Airport Results Performing the AirPAD algorithm and analysing the results, there is no evidence of a significant difference between the two scenarios. This result means that the introduction of the new A380 in the traffic distribution doesn't affect significantly the runway cross section examined.

36. AirPAD - Airport Pavement Analysisand Design Comments Forecast airport pavement sections effective lifetime is a powerful decision support tool, allowing the airport management to prevent unexpected failures, improve safety, plan effectively future maintenance works, analyse the economic impact of a different traffic layout. One for all: which is the real benefit balance operating a new carrier or aircraft model in your airport? Most algorithms/software deliver a quick response, introducing allowances and shortcuts that pay a great price in terms of statistical reliability and results accuracy. Common approach is to refer to “standard materials” and “standard climate condition” as well as “standard dynamic loads”, then simplified calculation models. But, how far is YOUR airport from standards? Materials could be different according to local availability, climate is the most variable issue (and even ignored by the most), dynamic loads can change by location and traffic condition as well. Introducing too many allowance sources, the consequent statistical error is far higher, not just as linear yet exponential. About prediction models, statistic reliability is a primary concern as a lack of reliability downgrades the utility of a model to the same statistical relevance of a coin as a prediction tool … that’s not predicting yet gambling!

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