Machine Learning Approach for the Evaluation of Degraded Wheel Braking Performance on Contaminated Runways

Lee, HyunKi; Zhang, Wenxin; Puranik, Tejas G; Anvid, David; Payan, Alexia P; Kirby, Michelle; Mavris, Dimitri; Tegen, Carter; Georgia Institute of Technology

doi:10.21949/1528236

Advanced Search

Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

Advanced Search
Custom Query

All these words:

For very narrow results

This exact word or phrase:

When looking for a specific result

Any of these words:

Best used for discovery & interchangable words

None of these words:

Recommended to be used in conjunction with other fields

Language:

Dates

Publication Date Range:

to

Document Data

Title:

Document Type:

Library

Collection:

Series:

People

Author:

Clear All

Query Builder

Query box

Clear All

For additional assistance using the Custom Query please check out our Help Page

i

Machine Learning Approach for the Evaluation of Degraded Wheel Braking Performance on Contaminated Runways

2023-08-01
By Lee, HyunKi ; Zhang, Wenxin ; Puranik, Tejas G ; ...

[PDF-7.27 MB]

Select the Download button to view the document

This document is over 5mb in size and cannot be previewed

English

Details You May Also Like

Details:

Creators:

Lee, HyunKi ; Zhang, Wenxin ; Puranik, Tejas G ; Anvid, David ; Payan, Alexia P ; ... More +

Lee, HyunKi ; Zhang, Wenxin ; Puranik, Tejas G ; Anvid, David ; Payan, Alexia P ; Kirby, Michelle ; Mavris, Dimitri ; Tegen, Carter Less -
Corporate Creators:

Georgia Institute of Technology
Corporate Contributors:

United States. Department of Transportation. Federal Aviation Administration. William J. Hughes Technical Center
Subject/TRT Terms:

[+]

Aircraft Airport Runways Artificial Intelligence Braking Crash Avoidance Systems Machine Learning Neural Networks
Publication/ Report Number:

DOT/FAA/TC-23/43
DOI:

https://doi.org/10.21949/1528236
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report
Corporate Publisher:

United States. Department of Transportation. Federal Aviation Administration. William J. Hughes Technical Center
Abstract:

One of the most challenging issues in the airline community has been the safe operations of aircraft during the landing phase of flight. Several entities agree that runway excursion is the most frequent type of landing incident or accident, and studies on this topic have shown that a significant contributor is the condition of the runway surface at the time of landing, as well as the potential for degraded braking performance.
Format:

PDF
Collection(s):

FAA Technical Library
Main Document Checksum:

[+]

urn:sha256:63ae7d0ea49eda3dee85bed654a70c8939efffe2760acf84dc98beb427903407
Download URL:

https://rosap.ntl.bts.gov/view/dot/68925/dot_68925_DS1.pdf
File Type:

No Additional Files

More +

Machine Learning Approach for the Evaluation of Degraded Wheel Braking Performance on Contaminated Runways

Details:

Supporting Files

You May Also Like