NTSB CAROL · Event
Event ATL05CA070
Aircraft involved
Probable cause & findings
The student pilot's failure to maintain direction control, which resulted in a loss of control and collision with a berm.
Factual narrative
On March 30, 2005 at 1615 eastern standard time, a Cessna 152, N714TK, registered to and operated by Long Aviation collided with a berm at Goose Creek Airport, North Carolina. The training flight was operated under the provision of Title 14 CFR Part 91 with no flight plan filed. Visual Meteorological conditions prevailed at the time of the accident. The airplane was substantially damaged and the student pilot was uninjured. The flight departed Monroe, North Carolina March 30, 2005 at 1600. According to the pilot, while returning from a cross-country flight, he established an approached to runway 4 at Goose Creek Airport for a full-stop landing. During the landing roll, the airplane veered off the right side of the runway 4 and collided with a berm. The pilot did not report any mechanical or flight control malfunction prior to the accident. Examination of the airplane revealed that the airplane came to rest approximately 1800 feet from the approach end of runway 4. The right stabilizer was broken off from the empennage. The right wing tip was buckled, and the nose wheel was buckled under the fuselage. According to the pilot, while returning from a cross-country flight, he established an approach to runway 4 at Goose Creek Airport for a full-stop landing. During the landing roll, the airplane veered off the right side of runway 4 and collided with a berm. The pilot did not report any mechanical or flight control malfunctions prior to the accident. Examination of the airplane revealed that the airplane came to rest approximately 1800 feet from the approach end of runway 4. The right stabilizer was broken off from the empennage. The right wing tip was buckled, and the nose wheel was buckled under the fuselage. Source: NTSB Aviation Accident Database (Pre-2008 Archive) Retrieved: 2026-02-12
Verbatim from NTSB's published report. Source file
NTSB_2005_ATL05CA070.txt.
Findings + structured fields enriched from FAA avall.mdb.
Full investigation docket on
data.ntsb.gov ↗.
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Related research
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Academic papers and agency reports matching this event's aircraft type or causal vocabulary (loss of control). Sourced from NASA NTRS, NTSB Safety Studies, FAA CAMI, AOPA Air Safety Institute, Embry-Riddle Scholarly Commons, arXiv, and the Semantic Scholar academic graph.
- Embry-Riddle Scholarly Commons 2025 · Journal article (JAAER)
A Scoping Review of Aviation Loss of Control Inflight Research
Loss of control – inflight (LOC-I) contributes to aircraft accidents at unacceptably high rates. Significant industry efforts and research have aimed to improve LOC-I prevention, detection, and recove…
- SKYbrary (Eurocontrol) 2024 · SKYbrary article
Loss of Control In-Flight (LOC-I) — SKYbrary Knowledge Base
SKYbrary comprehensive knowledge-base entry on Loss of Control In-Flight — definitions, contributing factors, accident case studies (Air France 447, Colgan 3407), and prevention strategies.
- NTSB Aircraft Accident Reports 2022 · Accident report
Loss of Control on Takeoff in Icing Conditions — Citation 560XL
Cessna Citation 560XL fatal takeoff icing accident, March 2018. Investigation of a Citation 560XL loss-of-control takeoff accident in icing conditions.
- Semantic Scholar 2021 · Article (Aviation)
ANALYSIS OF GENERAL AVIATION FIXED-WING AIRCRAFT ACCIDENTS INVOLVING INFLIGHT LOSS OF CONTROL USING A STATE-BASED APPROACH
Inflight loss of control (LOC-I) is a significant cause of General Aviation (GA) fixed-wing aircraft accidents. The United States National Transportation Safety Board’s database provides a rich source…
- NASA NTRS 2021 · Presentation
Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection
Abstract—We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft.
- NASA NTRS 2021 · Conference Paper
Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection
We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft.
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