Download presentation

Presentation is loading. Please wait.

Published byCamron Roose Modified about 1 year ago

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

2
AirPAD - Airport Pavement Analysis and Design Pavement Friction is a Safety Concern The runway surface condition is a critical safety concern as it’s influenced by friction, above all, during adverse weather conditions and consequently influencing the braking distance and the directional control of aircrafts. Airport Operators must grant a Minimum Friction Level (MFL) on runways as it’s a matter of prior safety concern. Pavement Management Middle East 2009 – march 2009, Dubai UAE The FSF (Flight Safety Foundation) reports that 20% of casualties (1995-2007) are related to overruns and veer-offs accidents (excursion accidents) occurring during takeoff or landing phases. Statistically, turboprops suffer a higher risk of veer-offs while jets a higher risk of overruns. 20% of casualties happen during the 2% of flight time

3
AirPAD - Airport Pavement Analysis and Design Pavement Management Middle East 2009 – march 2009, Dubai UAE Airport Operator Minimize Service Disruption Traffic Growth Increase Pavement Lifespan Plan Maintenance Operations Minimize Costs APMS Airport Pavement Management Systems Overview Current Conditions Develop Maintenance Strategies GRANT SAFETY Key Tools: Monitoring methodologies Performances evaluation Prediction models

4
AirPAD - Airport Pavement Analysis and Design Coefficient of Friction (COF) COF represent the degree of skid resistance (hor.) provided by a pavement to the load (vert.) of the aircraft gear Runway friction deteriorates with the coverages, which represent the number of times when a particular point on the pavement is expected to be stressed as a result of aircraft operations. Coverages are function of aircraft type (configuration of the landing gear: wheelbase of main gear, number and spacing of wheels), the width of tire- contact area, and cross-section distribution of the aircraft wheel-path (wandering) along the guideline marking (pavement centreline). Pavement Management Middle East 2009 – march 2009, Dubai UAE

5
AirPAD - Airport Pavement Analysis and Design Actual Methods and Models ** Test Devices and measurement procedures harmonization are still and open issue. Pavement Management Middle East 2009 – march 2009, Dubai UAE Runway friction testing is performed by making four (4) test runs along the length of the runway - two (2) on each side of centerline at offsets of 3 m (in the aircraft main gear wheelpaths), recording one reading point every 100 m. The depth of water placed in front of the test tire by the SFT's self-wetting system is usually 0.5 mm thick. The test speed is 65 km/h (40 mph).

6
AirPAD - Airport Pavement Analysis and Design Pavement Management Middle East 2009 – march 2009, Dubai UAE Actual Methods and Models Through this procedure, areas suffering a low friction index could not be properly identified, then missing appropriate maintenance planning.

7
AirPAD - Airport Pavement Analysis and Design Actual Methods and Models Performances Models performance models are mostly developed by statistical regression on multi- year historical field measurements and observations. Pavement Management Middle East 2009 – march 2009, Dubai UAE Critical Issues Discontinued historical databases Different methods for monitoring pavement conditions along the years Poor amount of historical data available Lack of accuracy to perform a multi-year analysis in the early years of a new section.

8
AirPAD - Airport Pavement Analysis and Design AirPAD Friction Model requirements Single friction measurement session performed by a high number of reading points along the runway length and cross section (3400) Pavement Management Middle East 2009 – march 2009, Dubai UAE Detailed aircraft operations repository data (cumulative traffic; traffic spectrum) Independent from previous data repositories Improved accuracy by higher test points density Easy to be collected Accurate aircraft type distribution

9
AirPAD - Airport Pavement Analysis and Design AirPAD Friction Model Algorithm Pavement Management Middle East 2009 – march 2009, Dubai UAE

10
AirPAD - Airport Pavement Analysis and Design Pavement Management Middle East 2009 – march 2009, Dubai UAE Hypothesis and Algorithm steps: 1.Cumulative number of equivalent coverages (CECs) in the runway cross section points are calculated from input data (aircraft annual movements, traffic spectra and construction time); 2.Friction tests are ran along several longitudinal paths in a single test session; 3.Experimental results of friction tests are analysed for localizing homogeneous sections; 4.Runway is sorted into 4 zones and referring to mean friction value of the homogeneous sections (zone 1 touchdown, zone 2 aircraft deceleration by air- brakes and reversers, zone 3 aircraft deceleration by wheel brakes and zone 4 approach to taxiway exit); 5.A critical window (CW) is defined and referring to homogeneous sections and the lowest IFI value are identified in the four runway zones; 6.Friction values in the CW are plotted vs. CECs; 7.Regression analysis is performed to calculate the deterioration model function;

11
AirPAD - Airport Pavement Analysis and Design Pavement Management Middle East 2009 – march 2009, Dubai UAE AirPAD Friction Model Algorithm Most of deterioration models take into account simply the traffic volume (i.e. number of vehicle passes or aircraft departures), the degradation of pavement surface characteristics is greatly related to the number and intensity of shear stress applied by wheels to the pavement. In the AFM Algorithm the Friction Deterioration is functional to Equivalent Coverages ECs ECs concept ECs = ( nr. of coverage ) * ( equivalency factor ) Coverages: is the number of times a specific point on the pavement is stressed by a wheel Equivalency factor: normalize the different damage contribution produced by different aircraft types and landing gears assemblies (i.e. multiple; main and nose gears), as shear stress magnitude and frequency would change accordingly. As coverages are statistically distributed over the runway cross section, the ECs could be assumed as an independent variable by evidence >>> the friction deterioration model could be then developed by a single test session

12
AirPAD - Airport Pavement Analysis and Design Pavement Management Middle East 2009 – march 2009, Dubai UAE ECs Evaluation Number of coverages on a point is calculated by the following formula (main gear - right)

13
AirPAD - Airport Pavement Analysis and Design Pavement Management Middle East 2009 – march 2009, Dubai UAE The contact area is supposed to be elliptic, thus “a” is the semi-minor axis ECs Evaluation where q r indicates the load on single wheel and p is the inflating pressure. As q r assumes very different values on landing and take-off operations (i.e. two different values of semiminor axis a), two R(x,y) i, functions have to be written:

14
AirPAD - Airport Pavement Analysis and Design Pavement Management Middle East 2009 – march 2009, Dubai UAE ECs Evaluation The cumulative number of coverages on a point located at a distance y from the runway centerline is: As suggested by FAA the lateral distributions of aircraft could be much more nearly represented by normal distribution functions; average and standard deviation of lateral distribution calculated from available and consolidated experimental data.

15
AirPAD - Airport Pavement Analysis and Design Pavement Management Middle East 2009 – march 2009, Dubai UAE ECs Evaluation Coverages produced by different gears have to be multiplied by an Equivalency Factor to normalize the friction damage contribution. Damage is related to magnitude and frequency of shear stress >> 3 Equivalency Factors are defined example of lateral distribution of number of coverages and effect of different equivalency factors

16
AirPAD - Airport Pavement Analysis and Design Pavement Management Middle East 2009 – march 2009, Dubai UAE Friction Measurements Tests speed 60 km/h >> To correct the measurement errors due to speed, we refer to AICR standards: S is slip velocity, in our case it is test velocity; S 0 is a constant that is related to macro-texture of the pavement; S 0 = a + b* T x T x = MPD obtained by laser prophilometer, a=14.235 b=89.719 By the need to calculate the IFI (60) independently to the test device used (repeatability), a set of parameters are introduced according to AIPCR standards: IFI (60) = A + B * CLF (60) + C * T x A = 0.00226 ; B = 1.00762 ; C = 0 The IFI calculated are used to define homogeneous sections by Cumsum and LCPC methods. Cumsum method: we chose the minimum difference between two adjacent sections and the minimum length of a section. Then we apply some hypothesis tests to a quantity depending on parameter examined fixing a level of confidence. LCPC method: as it is an iterative method measurements are split in sub-series with statistically constant values.

17
AirPAD - Airport Pavement Analysis and Design Pavement Management Middle East 2009 – march 2009, Dubai UAE Friction Deterioration Model By the evidence of experimental data on a new pavement, friction increases slowly at first stage. Is reasonable to explain this behaviour by the removal of exceeding superficial bitumen by early operations. Following this first period, by increasing of the operations, friction fades to lower and lower to an asymptotic value ( 0,08 rubber-wet steel) Concerning the most appropriate regression analysis model to be used, most popular models have been considered: linear, logarithmic, exponential, incremental (HDM), sigmoidal. The model is using either a logarithmic and a shifted exponential regression analysis model

18
AirPAD - Airport Pavement Analysis and Design Case Study: Ciampino Airport of Rome “G.B. Pastine” Pavement Management Middle East 2009 – march 2009, Dubai UAE Airport of Rome “G.B.PAstine” Runway Code 15-33 Runway length: 2207 m Runway width: 47 m

19
AirPAD - Airport Pavement Analysis and Design Pavement Management Middle East 2009 – march 2009, Dubai UAE Case Study: Friction test procedure An improved experimental test procedure was used instead of the normalized prEN 13036-2 in order to increase reading points density from 10 to 50 tests/km, arranged in 3 phases: 1.braked wheel over 5 m 2.wheel is prevented from rotating along a distance of 10 m and the average friction by the pavement on the tire is measured 3.brakes released and after 5 m the wheel returns to the starting position The difference “Δ” between min and max friction values over each measurement zone checks the measurements stability; if Δ > 0.2 then the measurement is rejected |--- 5m ----|----------- 10m ------|---- 5m ---| Tests speed 60 km/h The friction tests were carried out by the French device Adhéra, designed to measure the Longitudinal Friction Coefficient (LFC) with a slip ratio of 100%. This device has been used by most important experimental studies concerning harmonization [1and 2], though seldom used in airport environments.

20
AirPAD - Airport Pavement Analysis and Design Pavement Management Middle East 2009 – march 2009, Dubai UAE Case Study: Friction test procedure The model suggested by PIARC was applied for correcting small variation in speed and to calculate the International Friction Index (IFI) by the LFC measurements; As the PIARC model requires a scan of the surface texture, the Mean Profile Depth (MPD) values were measured by a laser device (Greenwood laser - update frequency 64 kHz, height resolution 0.0 1mm). The tests were carried out in only three days (26 and 27 June and 27 July 2007): 3400 measurement points over 20 path-alignments, moving from Runway Head 15 to 33;

21
AirPAD - Airport Pavement Analysis and Design Pavement Management Middle East 2009 – march 2009, Dubai UAE Friction MeasurementsCase Study: Traffic Volume and Operations The number of aircraft operations in G.B. Pastine Rome Airport increased dramatically in recent years. In the traffic spectra there are about 70 aircraft type, yet the 80% of traffic operations are related to 12 aircraft type only, as main operators are Low Cost Airlines ( Ryanair, Wizz, Easyjet, …. ). By the functional relationships previously introduced, the maximum number of CECs are placed at a distance of -3 m and 3 m offset by the center line of the runway

22
AirPAD - Airport Pavement Analysis and Design Pavement Management Middle East 2009 – march 2009, Dubai UAE The experimental survey allowed to map the friction index on the runway and to confirm the functional relationship between aircraft operations and surface friction deterioration; Minimum values of friction were localized in the second half of the runway (from 700 to 1400 m) at a distance of 2 to 4 m offset the centerline, where usually aircrafts brake and coverage is maximum. Friction values obtained along every test path were analyzed to identify homogeneous stripes and areas. Case Study: measurement results

23
AirPAD - Airport Pavement Analysis and Design Pavement Management Middle East 2009 – march 2009, Dubai UAE Case Study: measurement results As mentioned, two segmentation methods were used to IFI measurement series gauged on the path at a distance of 5m offset the runway centerline: the cumsum method, recommended by the AASHTO Guide, and the LCPC method. Both Methods led to comparable segmentation results (if same min. section length is set);

24
AirPAD - Airport Pavement Analysis and Design Pavement Management Middle East 2009 – march 2009, Dubai UAE Case Study: developing the friction deterioration model Runway is split into 4 zones, referring to the zone 1 landing-touchdown, zone 2 aircraft deceleration by air-brakes and reversers, zone 3 aircraft deceleration by wheel brakes and zone 4 approaching to taxiway exit; Each zone is split into 20 measurement path-alignment stripes Each stripe is split into friction mean value segments, accordingly to the segmentation methods previously introduced (cumsum / LCPC)

25
AirPAD - Airport Pavement Analysis and Design Pavement Management Middle East 2009 – march 2009, Dubai UAE Case Study: measurement results Once every stripe is sorted by IFI mean value, one or more critical cross stripes (CCS) window could be identified, referring to homogeneous stripe sections and the lowest IFI mean; A regression analysis could be now performed to estimate the deterioration model function;

26
AirPAD - Airport Pavement Analysis and Design Pavement Management Middle East 2009 – march 2009, Dubai UAE Case Study: critical areas identification Segmentation identified by cumsum method along measurements’ stripe-paths and mean value of friction index in each homogeneous section. Colour is related to IFI deterioration magnitude (red = very poor)

27
AirPAD - Airport Pavement Analysis and Design Pavement Management Middle East 2009 – march 2009, Dubai UAE Case Study: regression analysis Friction mean values IFI could be now plotted vs. CECs; A regression analysis could be now performed to estimate the deterioration model function: IFI = f ( CEF ) According to the Coverage Equivalency Factors hypothesis, three (3) different Equivalency Factors have been assumed: CEF proportional to footprint area CEF proportional to tires inflating pressure CEF proportional to wheel load Two different regression functions have been used: Logarithmic model Shifted Exponential model

28
AirPAD - Airport Pavement Analysis and Design Pavement Management Middle East 2009 – march 2009, Dubai UAE Case Study: regression analysis Results are reported to each zone, for the two regression model and the four CEFs Regression analysis results highlight that both models fit with the experimental data very well, though the logarithmic models is slightly better than the exponential one in the zones where the deterioration is higher (i.e. zone 3). All 4 zones remark very different regression function parameters, advising a positive confirm about splitting the runway length into 4 zones;

29
AirPAD - Airport Pavement Analysis and Design Pavement Management Middle East 2009 – march 2009, Dubai UAE Case Study: regression analysis By experimental evidence, any equivalency factor scores a significant improvement of the regression function accuracy. This evidence could be explained by the basic hypothesis of the CEF as function of the aircraft wheels footprint, thus related to pressure and load; IFI vs number of coverages: shifted exponential regression model and experimental data in zone 3.

30
AirPAD - Airport Pavement Analysis and Design Pavement Management Middle East 2009 – march 2009, Dubai UAE Case Study: regression analysis IFI vs CEF (proportional to wheel load): logarithmic regression model and experimental data in all 4 zones

31
POSITIVE EVIDENCE Case Study confirms that the statistical regression fit very well to the experimental friction measurement data set, and the independent variable (coverages) is virtually free of any Equivalency FactorSection cross distribution of friction values is significantly influenced by aircraft type distribution. SIMPLE INPUT DATA INDEPENDENT VARIABLES AirPAD - Airport Pavement Analysis and Design Pavement Management Middle East 2009 – march 2009, Dubai UAE Case Study: Conclusions The AirPAD Friction Index Model can predict runways pavement deterioration requiring only aircraft operations as inventory data (available from Airport activity statistics) and a single stage extensive friction measurement session along the runway length. Friction performances deterioration is mainly due to shear stress by aircraft wheels. Landing gear assembly types, different peculiar aircraft type loads and coverages distribution over the runway cross section are considered to enhance to model accuracy. As number of coverages is based on statistical probability, it could be considered and as an independent variable in the deterioration model. PREDICTIVE FRICTION INDEX Deterioration models represent an essential tool in Airport Pavement Management Systems (APMS), but their development requires a long time, as based on historical data analysis. The development of predictive deterioration models could be very helpful to carry out performances prediction even on early runway’s operative life.

32
www.airpad.org SySCOM Consulting Kteg Engineering ltd Department of Civil Engineering University of Rome “Tor Vergata” Italy Thank you

Similar presentations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google