NTSB CAROL · Event
Event CHI99LA028
Registry · N35302
FAA Aircraft Registry record.
Make / Model
CESSNA 172I
Engine
LYCOMING 0-320 SERIES (180 hp)
Seats / Engines
4 seats · 1 engine
Last airworthiness date
19680328
ADS-B equipped
Yes — Mode-S A3F265
Registrant of record
WILLIAM BENJAMIN LLC
Source: FAA Aircraft Registry (releasable master file).
Aircraft involved
Probable cause & findings
the student pilot's failure to maintain directional control of the airplane. Factors associated with the accident were the student's lack of experience and the road which the airplane contacted.
Factual narrative
On November 13, 1998, at 1030 central standard time, a Cessna 172I, N35302, operated by a student pilot collided with the terrain following a loss of control while landing on runway 13 (5,000' x 75') at the Jacksonville Municipal Airport, Jacksonville, Illinois. The student pilot was not injured. The airplane was substantially damaged. The 14 CFR Part 91 solo instructional flight was operating in visual meteorological conditions without a flight plan. The flight originated at the Jacksonville Municipal Airport, at 1010 cst. The student pilot stated that the airplane bounced during his third touch and go landing. The airplane veered off the left side of the runway into the grass. The student reported he was letting the airplane slow when it contacted a road (asphalt) which led to the VOR on the airport. The road was higher than the grass and the nose gear collapsed. Substantial damage to the firewall resulted from the nose gear collapse. The airplane bounced as the student pilot made his third touch and go landing. It veered off the left side of the runway and contacted an asphalt road which led to the VOR on the airport. The nose gear collapsed which subsequently damaged the firewall. Source: NTSB Aviation Accident Database (Pre-2008 Archive) Retrieved: 2026-02-12
Verbatim from NTSB's published report. Source file
NTSB_1998_CHI99LA028.txt.
Findings + structured fields enriched from FAA avall.mdb.
Full investigation docket on
data.ntsb.gov ↗.
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Related research
What the literature says.
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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