A study of airport pavement-aircraft interaction using wavelet analysis.

This study investigates airport pavement roughness and aircraft-pavement interaction in order to identify those pavement roughness attributes affecting aircraft response and to examine whether traditional roughness indices adequately capture these roughness attributes.

The methodology for accomplishing these objectives is briefly outlined below.

Five runway profiles from various geographic locations are used as input for the analysis and range in length from approximately 2,200 to 3,700 m.

The Federal Aviation Administration's software program ProFAA is used to calculate five traditional roughness indices (the Straightedge Index, Boeing Bump Index, International Roughness Index, Profile Index, and RMS Bandpass Filter) for each of the profiles.

These traditional indices are calculated for both full runway lengths as well as various distinct pavement profile subsections.

Computer simulation through the proprietary software APRas(c) is used to predict the response of a Boeing 737 travelling at constants speeds of both 20 and 45 knots for each of the five runway profiles.

The aircraft responses considered are vertical acceleration at the pilot station and center of gravity as well as the pavement load at the nose and main landing gears.

These responses are output in the distance domain and are entered into the commercially available MATLABRTM software for performing wavelet analysis.

Peak dynamic aircraft response is also used for analysis and is considered as the 95th percentile of the total aircraft response.

Wavelet theory is currently used in signal processing but is gaining popularity in the field of pavements.

Pavement profiles and simulated aircraft responses plotted in the distance domain resemble signals and can be analyzed using wavelet decomposition.

In this study, each of the five runway profiles and associated B737 aircraft response "signals" are decomposed.

The results from the twelve-level wavelet decomposition allow for the computation of energy, which is a quantification of signal variation.

The energy values are normalized for each profile by the runway length and number of sampling points.

The runway profile and dynamic aircraft response normalized energy values are statistically compared through Pearson correlations to the traditional roughness indices for both full runway lengths and runway subsections.

In addition, peak aircraft responses are compared to peak roughness indices through Pearson correlation.

Overall, the traditional roughness indices appear to adequately capture roughness events over the smaller pavement subsections, while poorly capturing roughness events over the full runway lengths.