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

Details

• Creators:
  Lee, HyunKi ; Zhang, Wenxin ; Puranik, Tejas G ; Anvid, David ; Payan, Alexia P ; Kirby, Michelle ; Mavris, Dimitri ; Tegen, Carter
• 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
  • Crash avoidance systems
  • Machine learning
  • Artificial intelligence
  • Neural networks
  • Braking
  • Airport runways
• 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
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/68925/dot_68925_DS1.pdf
• File Type:
  [PDF - 7.27 MB]
• Collection(s):
  FAA William J. Hughes Technical Center for Advanced Aerospace
• Main Document Checksum:
  urn:sha-512:36e67ec84fa3b134c77a84ffb6a72cfe1b494f967f7024b8a62a7ff25dd4139effe317348f76dfc4ca1e2a7f87634c834e87e1004c81624815772ce06f49da0c