NTSB CAROL · Event
Event WPR24LA078
Registry · N7250Q
FAA Aircraft Registry record.
Make / Model
CESSNA 172L
Year of manufacture
1972 · 52 years old at event
Engine
LYCOMING 0-320 SERIES (180 hp)
Seats / Engines
4 seats · 1 engine
Last airworthiness date
19720329
ADS-B equipped
Yes — Mode-S A9B886
Registrant of record
SALE REPORTED
Source: FAA Aircraft Registry (releasable master file).
Aircraft involved
Probable cause & findings
Loss of control during the landing roll due to a malfunctioning brake for undetermined reasons.
Factual narrative
On January 18, 2024, about 1240 Pacific standard time, a Cessna 172L, N7250Q, was substantially damaged when it was involved in an accident near Grass Valley, California. The pilot was not injured. The airplane was operated as a Title 14 Code of Federal Regulations Part 91 personal flight. The pilot reported that his flight to Nevada County Airport (GOO), Grass Valley, and the subsequent approach and touchdown were normal. During the landing roll on runway 25, as he applied the brakes to slow down and exit the runway, the airplane veered to the right. The pilot stated that the left brake did not function. The airplane slowed to less than 35 mph as it traveled past the taxiway turn . The pilot stated that the engine was at an idle power setting, and he attempted the use of aerodynamic braking to stop the airplane, which he believed would stop before the end of the runway. The pilot stated that he “got off the brakes,” and attempted to correct back to runway centerline with the application of full left rudder pedal. The airplane continued to veer to the right, exited the right side of the runway, traveled over an embankment, and came to rest inverted. The airplane sustained substantial damage to the wings, vertical stabilizer, rudder, and propeller. A review of photographs provided by the Federal Aviation Administration, showed that all three-landing gear had left impressions in the soft dirt adjacent to the runway. The right main landing gear impression exhibited a more pronounced indention mark in the dirt than the left main and nose landing gears. A postaccident examination of the brake system revealed improper retaining hardware bolts securing the backplates of the left and right brake calipers. Both brakes were manipulated by the rudder pedals and when activated were “firm” and functioned normally. Examination of the nose landing gear oleo strut showed the torque links were not contacting the centering block on the strut housing. The left and right steer rods were attached and had continuity with the left and right rudder pedals. The pilot reported that during the landing roll, he applied the brakes to slow down; however, the left brake did not function properly, and the airplane veered to the right of the runway. The pilot attempted aerodynamic braking and used full left rudder in an attempt to return the airplane to the runway centerline. The airplane continued to the right down an embankment, nosed over, and came to rest inverted. Review of accident site photographs showed impressions from all three-landing gear in the dirt adjacent to the runway, with the right landing gear showing a more pronounced indentation in the dirt. due to heavy braking on the right side it is likely the right brake locked up. A postaccident examination of the brakes revealed improper retaining bolts securing the brake calipers; however, both brakes functioned normally. No other mechanical anomalies were found that would have precluded normal operation. Source: NTSB Aviation Accident Database Retrieved: 2026-02-12
NTSB Findings
Hierarchical cause / factor breakdown from the FAA bulk avdata database. Each finding tagged C (Cause) or F (Factor).
- — Aircraft-Aircraft oper/perf/capability-Performance/control parameters-Directional control-Not attained/maintained
- — Aircraft-Aircraft systems-Landing gear system-Brake-Unknown/Not determined
Verbatim from NTSB's published report. Source file
NTSB_2024_WPR24LA078.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.
Browse the full corpus — academia portal ↗