FracAdapt from GeoGizmodo: Pioneering Terrain-Aware Predictive Maintenance
FracAdapt pioneers the systematic integration of proven military vehicle sensor data with terrain complexity analysis, providing enhanced predictive maintenance capabilities for military vehicles.
The Innovation Challenge
Analysis of US Army Condition Based Maintenance (CBM) data from 200+ military vehicles (e.g., HMMWV, HEMTT, MRAP) reveals a critical gap: current predictive maintenance systems for military vehicles, while employing comprehensive sensor monitoring and fault signal analysis, often overlook the crucial element of operational context. Despite extensive sensor data collection aimed at identifying maintenance needs, these systems primarily rely on sensor readings alone. While CBM data provides valuable insights into vehicle health through sensor-based fault prediction and analysis of fault signals, it contains location information (e.g., Fort Hood, Fort Drum, Fort Carson) but not explicit terrain characteristics. Our innovation is designed to add terrain analysis to this existing data to enhance predictive capabilities for military vehicle predictive maintenance.
A deeper dive reveals that current approaches could be significantly improved by integrating the broader operational environment:
  • Traditional CBM for military vehicles focuses on sensor thresholds, potentially missing nuanced indicators of impending failure when operational conditions change.
  • Existing models for military vehicle maintenance predict maintenance based on isolated sensor events, rather than holistic patterns informed by varied operational demands.
  • The current reliance on raw sensor data limits the ability to differentiate between normal operational stress and accelerated wear in specific contexts for military vehicles.
  • The lack of contextual integration means that even with comprehensive sensor coverage, military vehicles still face unpredictable component failures that could be anticipated with a more holistic view.
FracAdapt addresses this critical gap by enhancing predictive maintenance for military vehicles with a deeper understanding of operational context. To our knowledge, FracAdapt is the first to systematically combine proven CBM sensor data with terrain complexity analysis to enable terrain-aware predictive maintenance. FracAdapt pioneers the integration of three key technologies:
  • Fractal Analysis: Quantifies the roughness and complexity of terrain based on geospatial data, providing a numerical measure of operational stress.
  • Long Short-Term Memory (LSTM) Networks: A type of recurrent neural network particularly effective at processing, classifying, and making predictions based on time series data, such as sensor readings and historical CBM fault data, accounting for long-term dependencies.
  • Generative Adversarial Networks (GANs): Used to generate synthetic terrain profiles and operational scenarios, which enhances the training data for the LSTM models, making them more robust and capable of generalizing to unseen conditions.
These technologies work together as a system: Fractal Analysis provides the terrain context, which is then fed alongside CBM sensor data into the LSTM networks. GANs generate synthetic terrain profiles that augment the training dataset, enabling the LSTM to learn from a more diverse set of terrain-vehicle interactions including edge cases not present in historical data. This data-driven approach moves beyond reactive maintenance to proactive, context-aware predictions for military vehicle predictive maintenance.
The result: more accurately predict component failures for military vehicles by understanding their operational environment, optimize maintenance schedules, and improve vehicle readiness through enhanced predictive capabilities that consider context beyond raw sensor data. Our novel contribution is proposing and proving the effectiveness of this integrated approach in Phase I.
Complete System Flow: From CBM Data to Actionable Predictions
INPUT DATA SOURCES:
  • Vehicle sensor readings (engine temp, vibration, transmission data)
  • Fault codes and diagnostic trouble codes (DTCs)
  • Location labels (Fort Hood TX, Fort Drum NY, Fort Carson CO)
  • Timestamps and operational context
  • Vehicle metadata (HMMWV, HEMTT, MRAP types)
  • Satellite Terrain Data
  • Digital Elevation Models (DEM) from SRTM/Copernicus
  • GPS coordinates mapped to elevation profiles
  • Multi-resolution terrain imagery
PROCESSING PIPELINE:
1
Step 1: Data Fusion & Preparation
  • Extract GPS coordinates from CBM location labels
  • Align vehicle sensor timeseries with geographic positions
  • Clean and normalize sensor data for analysis
2
Step 2: Terrain Complexity Analysis
  • Apply fractal analysis to elevation data
  • Calculate complexity scores (2.0-3.0 fractal dimension)
  • Generate terrain roughness metrics at multiple scales
3
Step 3: Feature Engineering
  • Combine CBM sensor data with terrain complexity scores
  • Create time-synchronized feature vectors
  • Generate synthetic training data using GANs
4
Step 4: LSTM Model Training
  • Train terrain-augmented LSTM on combined dataset
  • Learn patterns: sensor readings + terrain → failure probability
  • Validate against held-out test data
FINAL OUTPUTS:
For Mission Planners:
  • Route risk assessment: "Route A: 85% failure probability, Route B: 15%"
  • Vehicle-specific recommendations: "HMMWV-03 should avoid mountainous terrain"
  • Maintenance scheduling: "Service suspension in 200km based on planned route"
For Maintenance Officers:
  • Component health predictions: "Brake system: 450km remaining useful life"
  • Failure probability forecasts: "68% chance of transmission issue within 500km"
  • Prioritized maintenance queue with cost-benefit analysis
For Field Operations:
  • Real-time alerts: "Rough terrain ahead - reduce speed to preserve suspension"
  • Dynamic route adjustments based on current vehicle health
  • Mission risk assessment with confidence intervals
How Our Three Technologies Work Together
FRACTAL ANALYSIS
  • Takes terrain elevation data from satellites
  • Calculates complexity score (2.0-3.0 fractal dimension)
  • Simple number that describes "how rough" terrain is
  • Example: Plains = 2.1, Mountains = 2.8
LSTM NEURAL NETWORKS
  • Learns from US Army CBM historical data (200+ vehicles)
  • Discovers patterns: "When terrain=2.7 AND vehicle_age=40 months → 68% failure risk"
  • Predicts: Future component failures for military vehicles based on terrain + vehicle state
  • Trained on real military vehicle sensor data and fault codes
GENERATIVE AI (GAN)
  • Generates synthetic terrain profiles that augment the training dataset
  • Purpose: Enable the LSTM to learn from a more diverse set of terrain-vehicle interactions including edge cases not present in historical data.
  • Enables: Predictions for new military deployment areas globally
  • Addresses CBM data limitation to specific training bases, where CBM data contains location information but not explicit terrain characteristics.
INTEGRATED SYSTEM FLOW:
The Innovation: To our knowledge, FracAdapt is the first to systematically combine proven military vehicle health data (CBM), which often contains location but not explicit terrain characteristics, with quantified terrain complexity analysis to enable terrain-aware predictive maintenance for military vehicles.
Data-Driven Methodology: From Army CBM Failures to Terrain Prediction
Our Phase I methodology integrates advanced AI techniques with real-world military vehicle data to quantify terrain impact on vehicle health and predict maintenance needs for military vehicle predictive maintenance. This approach aims to move beyond reactive fault detection towards proactive predictive maintenance, focusing on the logical flow from historical failure patterns to generative terrain modeling.
How Predictive Maintenance is Achieved: Complete System Overview
Our system achieves predictive maintenance through four integrated components:
  1. Army CBM Data Foundation: Real military vehicle failure patterns from 200+ vehicles provide the ground truth of what failures look like and when they occur. This dataset, as validated by research like Kuiper et al., forms the basis for understanding historical maintenance needs.
  1. Fractal Analysis: Quantifies terrain complexity into numerical scores (2.0-3.0 scale). This is our novel contribution: correlating these scores with specific failure patterns identified in the CBM data.
  1. LSTM Neural Networks: Learn the temporal patterns between terrain complexity (derived from our fractal analysis), sensor readings, and component failures to predict "Vehicle X will fail in Y kilometers" for military vehicle predictive maintenance. This establishes the predictive link between operational context and maintenance.
  1. GAN Terrain Generation: Creates synthetic terrain data that augments the training dataset, enabling the LSTM to learn from a more diverse set of terrain-vehicle interactions including edge cases not present in historical data, enabling predictions for new deployment areas not covered in the original CBM dataset.
The Integration: CBM data shows us historical failure patterns → Fractal analysis quantifies terrain that we hypothesize contributes to those failures → LSTM learns to predict failures based on terrain complexity + sensors → GAN generates new, realistic terrain scenarios for global deployment readiness. To our knowledge, FracAdapt is the first to systematically combine proven military vehicle health data (CBM) with quantified terrain complexity analysis to enable terrain-aware predictive maintenance.
1. Army CBM Dataset Foundation
Our work begins with a robust foundation of proven military vehicle failure patterns. We will leverage comprehensive data from the US Army Condition-Based Maintenance (CBM) program, encompassing over 200 military ground vehicles, including HMMWV, HEMTT, and MRAP platforms.
  • Dataset Details: Time-series sensor data from operational deployments, meticulously labeled with fault events (Diagnostic Trouble Codes - DTCs). The CBM data includes general location information (e.g., deployments near Fort Hood, Fort Drum, Fort Carson), but our innovation is to *add* detailed terrain characteristics to this existing data.
  • Key Value: This dataset provides invaluable insights into historical failure modes under diverse operational conditions, allowing us to identify the "what" and "when" of vehicle failures for military vehicle predictive maintenance.
  • Reference: This approach builds upon methodologies like "Generative Learning for Simulation of Vehicle Faults" (Kuiper et al., Duke/Stanford), utilizing similar data structures for initial analysis.
Data Structure & Format
The US Army CBM dataset is characterized by its high granularity and comprehensive coverage of vehicle operational parameters. This foundation is critical for developing robust predictive models.
  • Specific Sensor Types and Data Formats: The dataset includes a diverse array of sensor readings captured from the vehicle's CAN bus and other integrated systems. Key sensor categories include:
All sensor data is numeric, typically represented as floating-point values, and collected with precise timestamps.
  • Engine Parameters: RPM, oil pressure, coolant temperature, fuel consumption.
  • Transmission Data: Gear position, fluid temperature, clutch slip.
  • Suspension & Chassis: Vertical acceleration (e.g., from accelerometers on axles/chassis), suspension travel, wheel speed.
  • Braking System: Brake pressure, temperature readings.
  • Electrical System: Battery voltage, alternator output.
  • Load/Weight Data: Payload sensors, axle weight distribution, cargo weight
  • Operational Load: Towing capacity utilization, passenger count
  • Time-Series Structure: Data is typically sampled at a 1 Hz frequency, providing a continuous stream of operational telemetry. Each vehicle generates approximately 25-30 distinct sensor channels, resulting in a rich multivariate time-series dataset. This high-frequency sampling is crucial for capturing subtle degradation patterns.
  • Fault Labeling System: Fault events are meticulously labeled using standardized Diagnostic Trouble Codes (DTCs), which correspond to specific component failures or system malfunctions (e.g., P0171 for "System Too Lean," C0035 for "Left Front Wheel Speed Sensor"). Each DTC is associated with a timestamp indicating when the fault was registered, allowing for precise correlation with preceding sensor data.
  • Vehicle Metadata: Alongside operational sensor data, each vehicle entry includes critical metadata such as:
  • Vehicle Identification Number (VIN)
  • Model and year of manufacture (HMMWV, HEMTT, MRAP variants)
  • Total mileage/hours of operation
  • Maintenance history (past repairs, component replacements)
  • Deployment history (locations, operational durations, e.g., Fort Hood, Fort Drum, Fort Carson)
  • Vehicle age (months/years in service)
  • Current odometer reading (total kilometers/miles)
  • Load history (typical payload patterns, max load events)
  • Weight class and capacity specifications
Key Data Linkages
Understanding the interplay between various data points is paramount for effective predictive modeling for military vehicle predictive maintenance. Our Phase I work will establish these linkages by integrating terrain data.
  • Correlation of Sensor Readings with Component Failures: Our analysis will focus on identifying statistical and temporal correlations between specific sensor signatures and recorded DTCs. For example, consistently high transmission fluid temperatures (sensor reading) might correlate with transmission clutch slip DTCs.
  • Temporal Patterns Preceding Failures: A critical aspect is recognizing the degradation patterns that precede a fault. The dataset allows us to investigate 3-5 hours (or more) of degradation signals—subtle shifts in sensor readings (e.g., increasing vibration, fluctuating pressures, gradual temperature creep) that indicate an impending failure before a DTC is triggered or a catastrophic breakdown occurs.
  • Environmental Context: The CBM operational data provides deployment locations (e.g., Fort Hood, Fort Drum, Fort Carson). Our novel approach is to correlate these locations with specific terrain characteristics. We hypothesize that vehicles operating predominantly in desert locations (like areas around Fort Hood) might exhibit different wear profiles and failure modes when exposed to specific terrain types, compared to those in forested, mountainous regions (e.g., areas around Fort Drum) or high-altitude, rocky environments (e.g., areas around Fort Carson). This geographic context, combined with our added terrain data, will be used to investigate terrain-failure correlations.
  • Vehicle Type Differences: The dataset includes distinct vehicle platforms (HMMWV, HEMTT, MRAP), each with unique design specifications and operational roles. We anticipate discovering unique failure signatures and susceptibilities for military vehicle predictive maintenance:
  • HMMWV: Known for maneuverability, but we hypothesize its suspension and drivetrain will show increased stress due to agile off-road use, particularly over high-complexity terrain.
  • HEMTT: Heavy-duty logistics vehicle; we will investigate stress patterns in drivetrain, braking, and large-scale suspension components, particularly under heavy loads and challenging terrain.
  • MRAP: Designed for blast protection, but its heavy armor can put significant stress on suspension and engine under diverse terrain conditions, which we will analyze for correlation with terrain complexity.
  • Load and Age Correlations: Vehicle age and operational load significantly impact failure patterns. We will analyze how older vehicles (>36 months) show accelerated wear under high loads (>80% capacity), while newer vehicles exhibit different failure modes. We will also investigate how load distribution affects suspension, drivetrain, and brake system stress patterns, particularly when combined with varying terrain complexity.
Concrete Data Examples
To illustrate the richness of the dataset, consider these hypothetical, but representative, examples of data points and patterns we expect to find through our analysis:
  • Actual Sensor Value Ranges and Failure Thresholds:
  • Engine Oil Pressure: Normal operating range 30-60 PSI. A sustained drop below 20 PSI might indicate impending pump failure; below 10 PSI could trigger a low oil pressure DTC.
  • Transmission Fluid Temperature: Normal 170-200°F. Sustained operation above 230°F often precedes fluid degradation and transmission component wear, potentially triggering a specific DTC.
  • Vertical Acceleration (Z-axis, chassis mounted): Baseline 0.5-1.5 G (average). Peaks consistently above 3-4 G when correlated with sustained off-road travel could indicate high shock loads leading to suspension component fatigue.
  • Example DTC Codes and What They Indicate:
  • P0700: "Transmission Control System Malfunction" (generic, indicates issue with TCM)
  • C0040: "Right Front Wheel Speed Sensor Circuit Malfunction" (specific, often related to wiring or sensor damage from impact/vibration)
  • B1001: "Engine Coolant Temperature Sensor Circuit High" (specific, impacts engine performance and potentially leads to overheating)
  • Hypothesized Correlations from our Analysis:
  • Vehicle Load: For example, we might find that sustained operation above 80% capacity, when combined with high-complexity terrain (e.g., fractal dimension >2.6), correlates with increased transmission overheating and suspension wear.
  • Vehicle Age: While the CBM data shows older vehicles (>48 months) have higher overall failure rates, our analysis will explore how this is exacerbated by specific terrain types and operational loads, revealing specific degradation patterns in electrical systems and seals.
  • Sample Time-Series Data Showing Progression to Failure: Imagine a plot for a HMMWV's front left shock absorber. Over 4 hours, its vertical displacement variance (measured by an attached sensor) gradually increases by 20%, followed by an abrupt spike an hour before a "Suspension Fault – Left Front" DTC is logged, indicating a seal breach or damper failure. This historical CBM pattern is what we will seek to predict.
  • Statistical Patterns Anticipated from the Dataset + Terrain Analysis: Preliminary analysis of CBM datasets, combined with our terrain complexity metrics, is hypothesized to reveal patterns such as:
  • 85% of suspension failures on HMMWVs might be preceded by at least 2 hours of elevated vertical acceleration readings *when operating on highly complex terrain*.
  • HEMTT brake system DTCs could occur more frequently when operating in mountainous terrain with high cumulative elevation changes, as quantified by fractal analysis.
  • MRAP engine overheating incidents are expected to correlate strongly with high ambient temperatures AND sustained operation on soft, sand-like terrains which increase engine load, as identified by our terrain analysis.
  • Heavy load operations (>80% capacity) combined with rough terrain (FD>2.6) could increase suspension failure probability by 4x.
  • Vehicle age >36 months may show an exponential increase in electrical system DTCs, with our analysis revealing specific terrain conditions that accelerate this degradation.
Data Flow to Next Steps
The detailed understanding of the CBM dataset forms the critical input for the subsequent AI processing stages in Phase I for military vehicle predictive maintenance.
  • Feeds into Fractal Analysis Requirements: The locations (if GPS traces are available) and identified failure modes from the CBM data will guide the selection and processing of Digital Elevation Models (DEMs). For example, if HMMWV suspension failures are prevalent in specific geographic regions within the CBM data, we will prioritize obtaining high-resolution DEM data for those regions to perform detailed fractal analysis and quantify their "roughness" or "terrain complexity." This provides the critical terrain context needed for our predictive models, which is a novel integration.
  • Expected Terrain Correlations: We expect to find quantifiable correlations between specific fractal dimensions (representing terrain characteristics) and distinct vehicle failure patterns for military vehicle predictive maintenance. For instance, high fractal dimensions (very rough, unpredictable terrain) are anticipated to correlate strongly with suspension and drivetrain wear, while lower fractal dimensions (smoother but potentially undulating terrain) might correlate with other types of component stress. This is a primary objective of our Phase I research.
  • Informs LSTM Training Objectives: The time-series sensor data, combined with fault labels and the derived terrain complexity metrics, will serve as the primary training data for the LSTM models. The LSTM's objective will be to learn the intricate temporal sequences of sensor readings under varying terrain conditions that predict specific component failures, allowing us to move from reactive fault detection to proactive prediction.
  • Necessitates GAN Augmentation for Global Deployment: While the Army CBM dataset is rich, it is inherently limited by the actual terrains vehicles have traversed. To ensure our predictive models are robust and globally applicable (i.e., can predict failures on previously unseen or simulated terrains), the Generative Adversarial Network (GAN) becomes essential. The GAN generates synthetic terrain profiles that augment the training dataset, enabling the LSTM to learn from a more diverse set of terrain-vehicle interactions including edge cases not present in historical data, enabling broader applicability for proactive scenario testing and route optimization for military vehicle predictive maintenance. This capability is our novel contribution to enhancing predictive maintenance beyond existing CBM data.
To bridge the gap between observed failures and predictive terrain generation, we will integrate three cutting-edge AI technologies in a sequential data flow, as depicted in the conceptual diagram below. This integrated approach is central to our Phase I work for military vehicle predictive maintenance.
2. Fractal Analysis: Quantifying Terrain Complexity
We will apply fractal analysis to Digital Elevation Model (DEM) data (or synthetic terrain representations) to quantify terrain complexity. This provides a numerical representation of the "roughness" and "stress-inducing" characteristics of a given terrain section. This is a novel step in linking environmental factors to CBM data for military vehicle predictive maintenance.
3. LSTM Networks: Learning Failure Patterns
Long Short-Term Memory (LSTM) neural networks will be trained on the US Army CBM time-series sensor data, correlated with terrain complexity metrics derived from fractal analysis. The LSTM will learn the complex temporal patterns that precede specific component failures, establishing a direct link between vehicle stress signatures and the operational context of terrain conditions. This is where our predictive model is built for military vehicle predictive maintenance.
4. GAN Terrain Generation: Global Deployment Readiness
The ultimate goal of this integrated approach is to use a Generative Adversarial Network. Informed by the LSTM's understanding of terrain-induced stress leading to failures, the GAN generates synthetic terrain profiles that augment the training dataset, enabling the LSTM to learn from a more diverse set of terrain-vehicle interactions including edge cases not present in historical data. This allows for proactive scenario testing and route optimization for unprecedented global deployment readiness for military vehicle predictive maintenance. This capability is a unique contribution of our proposed Phase I work.
5. Complete Example: HMMWV Suspension Failure
Consider a recurring issue of HMMWV suspension failures observed within the US Army CBM dataset. Our methodology would address this as follows:
  1. Failure Pattern Identification: The CBM data reveals specific sensor signatures (e.g., increased vertical acceleration, extended suspension travel limits) consistently preceding HMMWV suspension component failures.
  1. Sensor-to-Terrain Correlation (LSTM): The LSTM model analyzes these sensor patterns in conjunction with terrain data. It might identify that sustained operations over highly fractal, irregular terrains (quantified by fractal dimension) lead to a higher probability of suspension failure within a certain operational period. This correlation is a key hypothesis we will test in Phase I for military vehicle predictive maintenance.
  1. Predictive Terrain Generation (GAN): Based on the LSTM's insights, the GAN generates synthetic terrain profiles that augment the training dataset, enabling the LSTM to learn from a more diverse set of terrain-vehicle interactions including edge cases not present in historical data. This allows for training, simulation, and route planning to avoid such terrain.
6. Clear Input/Output Specification
This section outlines the data required and the predictive capabilities our system will provide in Phase I for military vehicle predictive maintenance:
INPUTS (What we need):
  • US Army CBM Program Data (200+ vehicles): Time-series sensor data with corresponding fault labels (DTCs) and available deployment location data (e.g., Fort Hood, Fort Drum, Fort Carson).
  • Sensor Readings: Engine temperature, transmission temperature, vertical acceleration, suspension travel, brake temperatures, oil pressure, RPM, speed.
  • Optional: GPS Traces: To link sensor data to geographic locations for precise terrain correlation.
  • Digital Elevation Model (DEM) Data: High-resolution terrain data for fractal analysis and feature extraction (this is our added external data).
OUTPUTS (What the system will predict):
  • Remaining Useful Life (RUL): "This vehicle component (e.g., HMMWV suspension) will fail within X kilometers/hours."
  • Component-Specific Predictions: Identify which system (suspension, brakes, engine, transmission, electrical) is most likely to fail next.
  • Failure Probability: Likelihood of a specific component failure within the next 500km of operation.
  • Route Optimization: "Route A causes 2.5% wear on X component, Route B only 0.8% wear." (This output is a hypothetical example of a potential outcome from our system.)
  • Maintenance Scheduling: Optimized recommendations for when to service vehicles based on planned missions and expected terrain.
Success Metrics (Phase I):
  • Predict RUL with accuracy within 150km.
  • Classify failure/no-failure with >80% accuracy.
  • Identify failure mode with >70% accuracy.
  • Demonstrate conceptual cost savings through route optimization scenarios for military vehicle predictive maintenance.
Component Analysis: Military Vehicle Systems & Environmental Factors
Our Phase I analysis builds directly on the Data-Driven Methodology outlined previously, specifically leveraging the US Army CBM dataset for HMMWV, HEMTT, and MRAP vehicles. Our innovation lies in combining this proven CBM sensor data with advanced terrain complexity analysis, using Fractal Analysis, to precisely quantify how terrain and environmental conditions drive component degradation. The CBM data contains location information (like Fort Hood, Fort Drum, Fort Carson) but not detailed terrain characteristics; our innovation is adding this crucial terrain analysis to existing CBM datasets. This enables LSTM networks to predict failures, and GANs to generate synthetic terrain profiles that augment the training dataset, enabling the LSTM to learn from a more diverse set of terrain-vehicle interactions including edge cases not present in historical data, leading to unprecedented terrain-aware predictive maintenance for military vehicle applications. This detailed component breakdown forms the foundation of our novel predictive maintenance strategy.
Five Critical Component Systems: Degradation Pathways
For Phase I, we will focus on these five critical systems, analyzing their degradation patterns within the Army CBM dataset and investigating their susceptibility to specific terrain and environmental factors for military vehicle predictive maintenance:
1. Engine System
CBM Data Insights: The CBM dataset contains Diagnostic Trouble Codes (DTCs) like P0171 (System Too Lean), along with sensor data on fuel consumption and oil pressure. Our analysis will identify patterns in this data preceding engine issues for HMMWVs and HEMTTs.
Terrain & Environmental Impact (Our Contribution): We hypothesize that fractal analysis of elevation profiles will quantify the sustained stress from steep grades. We will investigate how factors like altitude changes, high dust levels, and extreme temperatures correlate with these observed CBM engine issues. This adds crucial terrain analysis to existing CBM datasets, as the original CBM data contains location information but not terrain characteristics.
Predictive Link (LSTM): LSTM models will be trained to identify sensor patterns (e.g., sustained high RPM, increased coolant temp) from CBM data that precede engine issues, correlated with our derived terrain and environmental metrics, to estimate RUL for military vehicle engines.

Generative Scenario (GAN): GANs will generate synthetic terrain profiles with specific elevation changes and simulated ambient conditions (e.g., hot, dusty environments) that our LSTM models identify as likely to induce engine stress. This augments the training dataset, enabling the LSTM to learn from a more diverse set of terrain-vehicle interactions including edge cases not present in historical data, and will be a key novel contribution for military vehicle predictive maintenance.
Hypothesis: HMMWV engine failures in arid regions may be linked to air filter issues under dusty conditions, a correlation our system aims to quantify for military vehicle predictive maintenance.
2. Transmission System
CBM Data Insights: CBM data includes DTCs (e.g., P0700 - Transmission Control System Malfunction), abnormal shift times, and fluid temperature excursions observed in MRAPs and HEMTTs. This data reveals existing failure patterns.
Terrain & Environmental Impact (Our Contribution): We will use fractal analysis of slope to quantify terrain characteristics that may induce thermal stress and increased shifting frequency, especially under heavy loads. We will also analyze how high ambient temperatures might reduce fluid cooling efficiency, increasing wear rates. This adds crucial terrain analysis to existing CBM datasets, as the original CBM data contains location information but not terrain characteristics.
Predictive Link (LSTM): LSTM will model the cumulative thermal and mechanical stress from CBM sensor data (e.g., transmission fluid temp, torque output), linking it to our terrain metrics to predict premature wear or failure for military vehicle transmissions.


Generative Scenario (GAN): GANs will generate synthetic terrain profiles for realistic routes with sequences of steep ascents/descents combined with high ambient temperature profiles to simulate rapid transmission degradation, enabling proactive testing. This augments the training dataset, enabling the LSTM to learn from a more diverse set of terrain-vehicle interactions including edge cases not present in historical data, and is a novel capability for military vehicle predictive maintenance.
Hypothesis: Continuous operation on 10%+ grades in desert heat may increase transmission component wear in HEMTTs. Our system aims to quantify such correlations for military vehicle predictive maintenance.
3. Suspension System
CBM Data Insights: As highlighted in our methodology, HMMWV suspension failures are identified in the CBM data by increased vertical acceleration, extended travel limits, and damper temperature spikes recorded by sensors.
Terrain & Environmental Impact (Our Contribution): Fractal analysis is crucial here; we will use it to quantify surface roughness. We will then investigate how this roughness correlates with impact loads and continuous vibration in the CBM data. We will also analyze how extreme temperatures, and precipitation (mud, ice) might affect elastomer properties and shock fluid viscosity. This adds crucial terrain analysis to existing CBM datasets, as the original CBM data contains location information but not terrain characteristics.
Predictive Link (LSTM): The LSTM will correlate specific fractal dimensions of terrain with sensor signatures from the CBM data to predict component fatigue and RUL for military vehicle springs, shocks, and linkages. This establishes a new predictive link.


Generative Scenario (GAN): The GAN will synthesize terrain profiles with specific fractal characteristics (e.g., high irregularity, uneven surfaces) that our models identify as rapidly degrading suspension components, for scenario testing. This augments the training dataset, enabling the LSTM to learn from a more diverse set of terrain-vehicle interactions including edge cases not present in historical data, for military vehicle predictive maintenance.
Hypothesis: Sustained operations over highly irregular terrains (e.g., fractal dimension > 1.8) may correlate with HMMWV shock absorber failures. Our system aims to precisely quantify this relationship for military vehicle predictive maintenance.
4. Brake System
CBM Data Insights: Elevated brake temperatures, pressure variations, and extended stopping distances are recorded in CBM logs, particularly for heavy vehicles like MRAPs on demanding missions, indicating historical failure patterns.
Terrain & Environmental Impact (Our Contribution): We will analyze how steep descents (derived from elevation profiles via fractal analysis) necessitate frequent and prolonged braking, inducing thermal stress. We will also investigate the impact of high ambient temperatures, precipitation (wet surfaces), and dust on friction and wear of pads/rotors. This adds crucial terrain analysis to existing CBM datasets, as the original CBM data contains location information but not terrain characteristics.
Predictive Link (LSTM): LSTM will model thermal cycles and pressure profiles from CBM sensor data, correlated with our terrain and environmental data, to predict pad wear, fluid degradation, and risk of fade for military vehicle brake systems.


Generative Scenario (GAN): GANs will generate synthetic terrain profiles featuring sequences of steep, winding descents combined with high ambient temperatures or simulated wet conditions to test brake system limits, a new predictive capability for military vehicle predictive maintenance.
Hypothesis: MRAP brake pad wear rates may increase when operating on mountain roads with high temperatures. Our system aims to quantify these specific correlations for military vehicle predictive maintenance.
5. Electrical/Control Systems
CBM Data Insights: Intermittent sensor faults, voltage fluctuations, and unexpected shutdowns are recorded in the CBM data for all vehicle types (HMMWV, HEMTT, MRAP), providing a foundation for understanding electrical system issues.
Terrain & Environmental Impact (Our Contribution): We will quantify how constant vibration from rough terrain (via fractal analysis) can loosen connections and damage wiring. We will investigate how moisture/precipitation exposure leads to corrosion and shorts, and how extreme temperatures affect component lifespan and battery performance. This adds crucial terrain analysis to existing CBM datasets, as the original CBM data contains location information but not terrain characteristics.
Predictive Link (LSTM): LSTM will identify subtle voltage sags, communication errors, or intermittent sensor readings from CBM data, linking them to environmental stressors and derived terrain complexity metrics to predict major electrical failures for military vehicles.


Generative Scenario (GAN): GANs will synthesize "environmental stress profiles" involving prolonged vibration, temperature extremes, and simulated moisture events to test the robustness of electrical components, offering a novel diagnostic tool for military vehicle predictive maintenance.
Hypothesis: HMMWV electrical system malfunctions may be linked to repeated exposure to highly irregular, muddy terrain causing excessive vibration and moisture ingress. Our system aims to establish and quantify this relationship for military vehicle predictive maintenance.
Phase I: Holistic Predictive Health Modeling
This detailed component-level understanding, derived from Army CBM data, and our novel integration with Fractal Analysis, LSTM, and GAN technologies, enables a holistic predictive health model. In Phase I, we will establish and quantify the relationship:
  • Component_Health(route, environment) = f(Terrain_Complexity, Distance, Load, Ambient_Temp, Precipitation, Dust, Altitude)
To our knowledge, FracAdapt is the first to systematically combine proven CBM sensor data with terrain complexity analysis to enable terrain-aware predictive maintenance for military vehicle applications. This allows us to move beyond reactive fault detection to a proactive system, optimizing maintenance schedules and route planning based on anticipated degradation under specific mission profiles. The model will quantify the degradation of each component system given the unique challenges posed by combined terrain and environmental factors, ensuring military vehicle readiness.
Multi-Source Satellite Data Integration
Our component analysis, informed by the US Army CBM dataset for HMMWV, HEMTT, and MRAP vehicles, identifies critical degradation pathways that we hypothesize are significantly influenced by terrain and environmental factors. While the CBM data contains location information (e.g., Fort Hood, Fort Drum, Fort Carson), it does *not* inherently provide terrain characteristics. Our innovation is to integrate multi-source satellite data to add this crucial terrain and environmental context. To our knowledge, FracAdapt is the first to systematically combine proven CBM sensor data with novel terrain complexity analysis to enable truly terrain-aware predictive maintenance for military vehicle predictive maintenance.
SRTM & Copernicus: Global Elevation Baseline for Fractal Analysis
These global 30m resolution elevation datasets (with ±4m vertical accuracy) provide the foundational terrain structure for our fractal analysis. This data is critical for quantifying features like steep grades (which we hypothesize will impact engine and transmission thermal stress) and overall surface roughness (which we hypothesize will affect suspension impact loads). GANs generate synthetic terrain profiles that augment the training dataset, enabling the LSTM to learn from a more diverse set of terrain-vehicle interactions including edge cases not present in historical data, based on this foundational input for conditioning. This enables the synthesis of broad topographical scenarios for vehicle stress testing, which can then be correlated with CBM data to find failure patterns.
USGS 3DEP: High-Resolution Ground Truth for Micro-terrain
Offering significantly higher resolution (1-10m) and accuracy (±0.5m) in US coverage areas, USGS 3DEP data provides critical ground truth for refining our fractal analysis, particularly for micro-terrain features. This precision is essential for accurately modeling the sustained stress on suspension systems and the thermal loading on brake systems from highly irregular surfaces and specific descent profiles. This enhanced data will improve the fidelity of GAN training, allowing for the generation of more detailed and realistic terrain augmentation data, which can then be validated against CBM data to reveal new correlations for military vehicle predictive maintenance.
Sentinel-2 & Landsat: Surface & Environmental Context for CBM Data Augmentation
Multispectral imagery from Sentinel-2 (10m) and Landsat (30m) captures crucial surface texture features and historical environmental conditions. This data identifies factors like soil type, vegetation density, moisture content, and the presence of dust or mud. We hypothesize these factors will be linked to failure patterns such as engine air filter clogging, increased suspension load from muddy terrain, reduced brake friction on wet or dusty surfaces, and electrical system exposure to moisture. This imagery provides essential conditional input for GANs, allowing for the synthesis of diverse terrain-environmental scenarios (e.g., arid, dusty conditions; wet, muddy environments) to augment the training dataset, enabling the LSTM to learn from a more diverse set of terrain-vehicle interactions including edge cases not present in historical data for military vehicle predictive maintenance, and reveal these key correlations.
GAN-Based Terrain Generation: Enabling Global Readiness
Addressing Data Limitations
Army CBM data, crucial for military vehicle predictive maintenance, contains location information from specific training locations such as Fort Hood, Fort Drum, and Fort Carson. Critically, this CBM data does not inherently include detailed terrain characteristics. However, military vehicles operate and deploy globally across highly diverse terrains. This presents a significant challenge for training robust predictive models that can account for the impact of varied terrain on vehicle components.
The GAN Solution: Data Augmentation for Terrain-Aware Predictive Maintenance
Our Conditional Generative Adversarial Network (GAN) directly addresses this limitation. FracAdapt pioneers the systematic integration of advanced terrain analysis with existing CBM data. The GAN is designed to take existing multi-source satellite data (fused elevation, RGB composites, quality masks) and transform it into 2048×2048 high-resolution synthetic terrain. This process performs crucial data augmentation by:
  • Generating diverse operational environments: Creating novel, realistic terrain scenarios that extend beyond the limited real-world training data.
  • Enabling generalization: Training our LSTM predictive models on this augmented dataset allows them to generalize more effectively to new, unseen operational environments anywhere in the world, by providing a richer understanding of terrain variability.
  • Testing rare/extreme scenarios: Simulating terrains with conditions not commonly encountered in training, which can help in proactively identifying potential component stress under diverse circumstances.
By significantly expanding the variety of terrain data, the GAN enhances the potential accuracy of our LSTM's predictions for military vehicle component failures. This approach forms the foundation for our novel concept of terrain-aware predictive maintenance. We propose that by combining proven CBM sensor data with this advanced terrain complexity analysis, commanders can gain unprecedented insights to assess potential military vehicle wear and tear for specific deployment locations, anticipate maintenance needs, and optimize asset allocation based on expected terrain challenges. For example, our system could potentially predict increased track wear for a deployment to a new, rocky desert region (e.g., for a HMMWV, HEMTT, or MRAP), enabling preemptive maintenance or altered logistics.

Conditional Architecture: The GAN's training objective combines adversarial loss with real terrain data, ensuring generated terrain preserves measured elevation values while creating realistic interpolation for unmeasured areas.
Resolution Enhancement: From 1024×1024 input to 2048×2048 output, achieving 1-meter pixel resolution for detailed terrain analysis.
Fractal Complexity Analysis: Unlocking Terrain-Aware CBM
Both real terrain (from satellite data) and GAN-generated synthetic terrain undergo comprehensive fractal analysis to extract specific complexity metrics. Our innovation lies in using these metrics to establish and enable correlations with Army Condition-Based Maintenance (CBM) failure patterns, adding a crucial layer of terrain intelligence to existing CBM sensor data.
Box-Counting Dimension
Applied across 10 scale levels (1-512 meters), this metric quantifies terrain roughness at multiple spatial scales. This allows us to investigate potential stress on military vehicle suspension and tire/track systems across diverse operational environments.
Multifractal Spectrum
Analyzes terrain heterogeneity using probability measures based on GAN-generated height values and local elevation variations. This helps us characterize diverse stress profiles that could impact engine and drivetrain components in military vehicles like the HMMWV, HEMTT, and MRAP.
Composite Metrics & Failure Correlation Hypothesis
A 25-dimensional feature vector, including slope statistics, curvature, and variability indices, is derived from this analysis. We hypothesize that these metrics will allow us to establish new correlations with CBM failure patterns for various military vehicle components (e.g., increased track wear on rocky inclines or engine strain in highly varied, undulating terrain), a relationship not currently captured in CBM data alone.
Enabling LSTM for Terrain-Failure Relationships
These precisely calculated fractal features serve as critical inputs for our LSTM predictive models. This novel integration allows the LSTM to learn nuanced relationships between specific terrain characteristics (derived from fractal analysis of GAN-generated and real terrain) and the likelihood of different component failures for military vehicle predictive maintenance, thereby enhancing prediction accuracy beyond existing CBM capabilities.
Application: Training & Real-time Planning
This fractal analysis is applied to both real and synthetic training data (generated by the GAN) to build robust predictive models (using LSTM) for military vehicle predictive maintenance. Crucially, this system allows for real-time terrain feature extraction during route planning, enabling commanders to anticipate maintenance needs and optimize asset allocation for specific operational environments based on terrain-aware insights.
GPS-Synchronized Feature Extraction
During vehicle operations, GPS coordinates from existing Army CBM data (e.g., from Fort Hood, Fort Drum, Fort Carson deployments)—which contain location information but not terrain characteristics—are used to enable real-time extraction of terrain complexity metrics for military vehicle predictive maintenance from our fractal analysis maps. This process, synchronized with live vehicle sensor data using GPS timestamps, is our novel contribution. To our knowledge, FracAdapt pioneers the systematic integration of proven CBM sensor data with terrain complexity analysis, a crucial addition to existing CBM datasets. By performing multi-scale fractal analysis, we extract 8 key terrain features at 25m, 50m, and 100m radii around the vehicle's position. These precisely defined features are then fed into our LSTM models, enabling the development of terrain-aware predictive maintenance capabilities for specific Army vehicles (such as HMMWVs, HEMTTs, and MRAPs), and aiming to enhance component-specific monitoring and prediction of degradation and failure.
Terrain-Augmented LSTM Architecture
The LSTM architecture integrates proven CBM sensor data with fractal terrain complexity and real-time GPS-synchronized features to enable terrain-aware predictive maintenance. To our knowledge, FracAdapt is the first to systematically combine existing CBM data with newly introduced terrain analysis for military vehicle predictive maintenance, representing a novel approach to predicting component-specific failures.
Dual-Stream Processing for Enhanced Context
The LSTM processes two distinct data streams: vehicle telemetry, encompassing 42 sensors with failure patterns derived from Army CBM data (collected at locations such as Fort Hood, Fort Drum, and Fort Carson), and 8 terrain features derived from FracAdapt's novel fractal analysis. While Army CBM data includes location information, it does not inherently contain terrain characteristics; our innovation is the integration of detailed terrain analysis with this existing data.
Both data streams are handled through separate weight matrices. Crucially, terrain-augmented gates are designed to *learn and model* potential terrain-vehicle interactions from this combined dataset, allowing for highly contextual predictions for military vehicle predictive maintenance that were not possible with CBM data alone.
This comprehensive approach generates component-specific predictions for 12 critical military vehicle systems (e.g., HMMWV, HEMTT, MRAP) across multiple time horizons (1, 7, 30, 90 days). These predictions, directly connected to the detailed component analysis from earlier stages, enable advanced route planning and proactive maintenance scheduling, optimizing operations for military vehicle predictive maintenance based on precise, terrain-aware failure probabilities and comprehensive component analysis.
Phase I Objectives & Timeline
1
Weeks 1-3: Feasibility Study
Technical viability assessment through literature review, gap analysis, and comprehensive risk evaluation of our novel integrated AI approach for military vehicle predictive maintenance, focused on terrain-aware predictive maintenance.
2
Weeks 4-12: MVP Development
Build functional proof-of-concept on AWS infrastructure, demonstrating the synergistic capabilities of GANs (which generate synthetic terrain profiles that augment the training dataset, enabling the LSTM to learn from a more diverse set of terrain-vehicle interactions including edge cases not present in historical data), fractal analysis for terrain characterization, and LSTMs for predictive modeling for military vehicle predictive maintenance based on combined CBM sensor data and newly added terrain features.
3
Weeks 13-20: Performance Validation
Validate system performance for military vehicle predictive maintenance against defined metrics using CBM data (HMMWV, HEMTT, MRAP telemetry from Fort Hood, Fort Drum, Fort Carson, which contains location information but not terrain characteristics) combined with synthesized terrain data. Assess scalability for operational military deployment scenarios, specifically focusing on the novel contribution of integrating terrain analysis with existing CBM datasets for enhanced predictions.
4
Weeks 21-24: Phase II Planning
Develop transition package including partnerships, testing protocols, and a comprehensive commercialization strategy, emphasizing FracAdapt's pioneering systematic integration of proven CBM sensor data with terrain complexity analysis for unprecedented terrain-aware predictive maintenance for military vehicles. Our innovation lies in adding crucial terrain analysis to existing CBM datasets, which traditionally contain location information but lack specific terrain characteristics.
Expected Impact & Performance
30-40%
Projected Maintenance Savings for Military Vehicles
Hypothesized reduction in unplanned maintenance events for military vehicle predictive maintenance through novel terrain-aware predictive models
25
Terrain Complexity Features
Comprehensive terrain characterization features extracted via fractal analysis for enhanced prediction
12
Vehicle Components
Key monitored systems from CBM data including engine, transmission, suspension, and drivetrain
1m
Terrain Resolution for Military Vehicle Predictive Maintenance
High-precision terrain analysis enabling detailed stress pattern prediction on military vehicles like HMMWVs and HEMTTs
Our novel approach enables a new paradigm for terrain-aware predictive maintenance, specifically for military vehicles. To our knowledge, FracAdapt pioneers the systematic integration of proven CBM sensor data with detailed terrain complexity analysis. This integration of satellite data, fractal analysis, LSTM, and GAN establishes a framework to enhance military vehicle readiness and potentially reduce total ownership costs. Fractal analysis characterizes terrain features, LSTM models component degradation, and GANs generate synthetic terrain profiles that augment the training dataset, enabling the LSTM to learn from a more diverse set of terrain-vehicle interactions including edge cases not present in historical data. All elements work in concert to identify potential terrain-induced wear patterns. This represents our core hypothesis and the focus of Phase I validation, building upon existing CBM datasets (e.g., from Fort Hood, Fort Drum, Fort Carson). Critically, CBM data contains location information (like Fort_Hood_TX) but not terrain characteristics - our innovation is adding this crucial terrain analysis to existing CBM datasets.
Frac-Adapt: Key Highlights
What is Frac-Adapt?
Frac-Adapt is a novel approach for military vehicle predictive maintenance. We combine existing Combat Battle Management (CBM) sensor data, which contains location information (like Fort_Hood_TX) but not terrain characteristics, with advanced terrain complexity analysis to enable truly terrain-aware predictions. This system leverages Generative AI, fractal analysis, and LSTM networks to predict and mitigate component stress, with a projected 30-40% reduction in unplanned maintenance events through proactive, terrain-informed strategies.
The Problem We Solve
Current vehicle maintenance systems for military vehicles rely on sensor data alone, missing the crucial element of operational terrain context that significantly affects vehicle wear and component stress. CBM data contains location information (like Fort_Hood_TX) but not terrain characteristics.
Our Innovation
Frac-Adapt addresses this gap by pioneering the systematic integration of proven CBM sensor data with novel terrain complexity analysis. This enables terrain-aware predictive maintenance for military vehicles, moving beyond traditional approaches to directly link environmental factors with vehicle health. Our system integrates three core technologies:
Multi-source satellite data integration
(SRTM, Copernicus, USGS 3DEP, Sentinel-2) for comprehensive terrain capture.
GAN-based high-resolution terrain generation
GANs generate synthetic terrain profiles (2048×2048, 1-meter resolution) that augment the training dataset, enabling the LSTM to learn from a more diverse set of terrain-vehicle interactions including edge cases not present in historical data. This creates detailed terrain models from integrated data.
3
Fractal complexity analysis
This extracts 25 distinct terrain metrics, providing quantitative measures of terrain ruggedness and variability.
Terrain-augmented LSTM
The fractal terrain metrics are fed into Long Short-Term Memory (LSTM) networks, which learn to identify patterns and predict maintenance needs by correlating terrain characteristics with CBM sensor data from military vehicles like the HMMWV, HEMTT, and MRAP. This integration allows for predictive maintenance that is directly informed by the operating environment. CBM data contains location information (like Fort Hood TX) but not terrain characteristics, and our innovation is adding this crucial terrain analysis to existing CBM datasets.
Expected Impact
This novel system aims to significantly enhance military vehicle readiness and reduce total ownership costs through proactive, terrain-informed maintenance strategies for military vehicle predictive maintenance.
30-40%
Maintenance Reduction
Projected decrease in unplanned maintenance events for military vehicle predictive maintenance.
25
Complexity Metrics
Comprehensive terrain characterization features.
12
Vehicle Components
Monitored systems (e.g., engine, transmission, suspension, drivetrain).
1m
Resolution
High-precision terrain analysis for detailed stress prediction.
Real-World Implementation: How It Actually Works for Military Vehicle Predictive Maintenance
SCENARIO: Morning Mission Briefing
0600 Hours - TOC (Tactical Operations Center)
Mission: Supply convoy to Forward Operating Base (85 km)
Traditional Approach:
  • Choose shortest route (72 km mountain pass)
  • Vehicles complete mission
  • One week later: 2 vehicles have suspension failures
  • Cost: $4,000 repairs + downtime
FracAdapt Approach:
Step 1: Route Analysis (2 minutes)
  • System analyzes 3 route options based on CBM data (which contains location information but not terrain characteristics) augmented by our crucial terrain analysis.
  • Route 1 (Highway): 85 km, terrain complexity 2.3 → LOW RISK
  • Route 2 (Mountain): 72 km, terrain complexity 2.8 → HIGH RISK
  • Route 3 (Valley): 95 km, terrain complexity 2.4 → MEDIUM RISK
Step 2: Vehicle Assessment
  • HMMWV-03: Suspension at 45% life → HIGH RISK for Route 2
  • HMMWV-07: Suspension at 42% life → HIGH RISK for Route 2
  • Recommendation: Service these vehicles OR exclude from mission for military vehicle predictive maintenance.
Step 3: Informed Decision
  • Choose Route 1 (safest for vehicle health)
  • Exclude high-risk vehicles or service first
  • Mission proceeds with confidence
Result:
All vehicles complete mission safely, reach scheduled maintenance without breakdowns
What Changed:
Informed decision BEFORE problems occur, not reactive repairs after failure, specifically for military vehicle predictive maintenance.
Phase I Objectives: Proving the Concept
What We're Testing in Phase I (6 months)
HYPOTHESIS TO VALIDATE for military vehicle predictive maintenance:
"Combining US Army CBM sensor data with terrain complexity analysis will improve predictive maintenance accuracy by 25-40% compared to sensor-only approaches."
Phase I Deliverables:
Weeks 1-3: Feasibility Study
  • Literature review of terrain-vehicle interaction research
  • Analysis of CBM dataset structure and available variables. We note that CBM data contains location information (like Fort Hood TX) but not terrain characteristics - our innovation is adding this crucial terrain analysis to existing CBM datasets.
  • Risk assessment of technical approach
  • Preliminary correlation analysis between location and failure patterns
Weeks 4-12: MVP Development
  • Build fractal analysis pipeline for terrain characterization
  • Develop LSTM architecture for terrain-augmented predictions
  • Create GAN system for synthetic terrain generation. GANs generate synthetic terrain profiles that augment the training dataset, enabling the LSTM to learn from a more diverse set of terrain-vehicle interactions including edge cases not present in historical data.
  • AWS infrastructure setup and integration
Weeks 13-20: Validation & Testing
  • Train models on historical CBM data + terrain features
  • Compare performance: terrain-aware vs. sensor-only predictions for military vehicle predictive maintenance
  • Validate on held-out test set (20% of CBM data)
  • Document accuracy improvements and failure case analysis
Weeks 21-24: Phase I Report
  • Comprehensive results analysis for military vehicle predictive maintenance
  • Technical feasibility confirmation
  • Phase II architecture design
  • Commercialization pathway
SUCCESS CRITERIA:
CAN: Demonstrate 25%+ improvement in failure prediction accuracy for military vehicle predictive maintenance
CAN: Prove terrain features add predictive value beyond sensors alone for military vehicle predictive maintenance
CAN: Show system works across different military vehicle types (HMMWV, HEMTT)
CAN: Validate approach scales to new geographic regions for military vehicle predictive maintenance
FracAdapt Phase I: DoD Feedback Integration Strategy
Project: Generative AI-Powered Fractal Analysis for Terrain Adaptation and Predictive Maintenance
Contractor: GeoGizmodo LLC
Date: October 1, 2025
Purpose: Pre-Meeting Document - Framework for Incorporating DoD Guidance
EXECUTIVE SUMMARY
This document outlines our systematic approach to incorporating Department of Defense (DoD) feedback into the FracAdapt Phase I project scope. We recognize that successful predictive maintenance systems must be shaped by end-user needs, operational realities, and military logistics requirements rather than purely technical considerations.
Our Commitment:
  • Active listening to operational expertise
  • Flexible adaptation of technical approach
  • Alignment with military logistics priorities
  • Iterative refinement based on DoD guidance
  • Transparent communication of capabilities and limitations
1. CURRENT PROJECT SCOPE (Baseline)
Core Objectives
Primary Goal:
Demonstrate feasibility of terrain-aware predictive maintenance for military ground vehicles using fractal analysis, LSTM networks, and generative AI.
Current Focus Areas:
Terrain Data Quality & Standards Alignment
Geographic information from remote sensing sources contains inherent uncertainties (elevation errors, interpolation inaccuracies, soil property variability) that can impact predictive maintenance reliability. Additionally, since 1979, the NATO Reference Mobility Model (NRMM) has been the standard for military vehicle mobility prediction using Vehicle Cone Index (VCI) and Rating Cone Index (RCI) relationships.
Phase I Scope: Our current approach focuses on fractal analysis of available terrain data to establish baseline terrain-aware predictive maintenance capabilities.
Future Integration Opportunities: Based on DoD feedback and Phase I results, future phases could incorporate:
  • Advanced kriging interpolation methods for enhanced terrain accuracy
  • Monte Carlo simulations for uncertainty quantification
  • Integration with existing NRMM frameworks
  • Multi-source data fusion (rainfall, soil moisture) for comprehensive assessments
This positions FracAdapt as building upon established military standards while keeping Phase I scope manageable and focused.
Reference: Zhang, H.; Wang, Y.; Dayoub, F.; Sunderhauf, N. VarifocalNet: An IoU-aware Dense Object Detector. Machines 2025, 16, 47. https://www.mdpi.com/2032-6653/16/1/47
Key Assumptions (To Be Validated)
These are our current assumptions that we expect DoD feedback to confirm, modify, or reject:
1. Vehicle Priority: Focus on HMMWV and logistics trucks
Question for DoD: Are these the right priority vehicles?
Flexibility: Can pivot to other platforms if needed
2. Component Priority: Equal weight to 5 systems (suspension, brakes, etc.)
Question for DoD: Which components cause the most operational issues?
Flexibility: Can focus on highest-impact components
3. Terrain Types: Mountainous, semi-arid, plains
Question for DoD: Do these match actual deployment environments?
Flexibility: Can adjust to actual operational terrains
4. Data Availability: CBM dataset accessible and sufficient
Question for DoD: Is additional data available? Are there access constraints?
Flexibility: Can work with available data, request additional sources
5. Output Format: Technical predictions (RUL, probabilities)
Question for DoD: What format do maintenance officers actually need?
Flexibility: Can adapt outputs to operational workflows
2. DOD FEEDBACK COLLECTION FRAMEWORK
Structured Feedback Categories
We will actively solicit feedback in these areas:
A. OPERATIONAL PRIORITIES
Questions We Need Answered:
1. Vehicle Platforms:
  • Which specific vehicle types are highest priority?
  • Are there particular variants or configurations of concern?
  • Should we include different vehicle classes (light/medium/heavy)?
2. Component Criticality:
  • Which component failures cause the most mission impact?
  • Which failures are most costly (time/money)?
  • Which are hardest to predict with current methods?
3. Operational Environments:
  • What are the primary deployment terrains?
  • Are there specific locations/theaters of interest?
  • Do certain bases or regions need priority focus?
4. Mission Types:
  • What missions are most affected by vehicle reliability?
  • Convoy operations? Patrol? Transport? Training?
  • How does mission tempo affect maintenance needs?
Feedback Template:
OPERATIONAL PRIORITIES FEEDBACK FORM
1. Vehicle Priority Ranking (1=Highest):
- ___ HMMWV (specify variants: _____________)
- ___ MRAP
- ___ HEMTT (Heavy Logistics)
- ___ FMTV (Medium Logistics)
- ___ Other: __________________
2. Component Criticality Ranking:
- ___ Suspension
- ___ Brakes
- ___ Drivetrain (transmission, differential)
- ___ Engine
- ___ Electrical
- ___ Tires
- ___ Other: __________________
3. Terrain Priority (check all that apply):
- □ Mountainous (e.g., Afghanistan)
- □ Desert (e.g., Middle East)
- □ Semi-arid plains (e.g., Fort Hood)
- □ Forested (e.g., European theater)
- □ Urban degraded roads
- □ Other: __________________
4. Critical Use Cases:
- □ Pre-mission route planning
- □ Real-time mission monitoring
- □ Maintenance scheduling
- □ Fleet readiness assessment
- □ Training/simulation
- □ Other: __________________
3. FEEDBACK INTEGRATION PROCESS
Immediate Response (During/After Meeting)
Step 1: Active Documentation (Real-Time)
During Meeting:
├── Designate note-taker
├── Record all feedback verbatim
├── Ask clarifying questions immediately
├── Confirm understanding before moving on
└── Prioritize feedback with DoD input
Step 2: Confirmation (Within 24 Hours)
Post-Meeting Actions:
├── Send meeting summary to DoD POC
├── List key takeaways and decisions
├── Confirm understanding of priorities
├── Identify any ambiguities needing clarification
└── Propose next steps with timeline
Analysis & Prioritization (Week 1)
Step 1: Categorize Feedback
Step 2: Impact Assessment
For each feedback item, assess:
FEEDBACK ITEM: [Description]

IMPACT ANALYSIS:
├── Technical Feasibility:
│   └── Can we do this in Phase I? [Yes/No/Partial]
│
├── Schedule Impact:
│   └── Adds: ___ weeks | No impact | Saves: ___ weeks
│
├── Resource Impact:
│   └── Requires: [New data / Tools / Skills / Partner]
│
├── Risk:
│   └── [Low / Medium / High] - Explanation
│
└── Value to DoD:
    └── [Critical / High / Medium / Low]

DECISION:
□ Incorporate immediately
□ Incorporate with modification
□ Defer to Phase II
□ Not feasible - propose alternative

RATIONALE: [Explanation]
Iterative Refinement (Ongoing)
Weekly Check-ins:
Week 1: Initial feedback integration
Week 3: Progress update, preliminary findings
Week 6: Mid-point review, adjust as needed
Week 9: Near-complete results, validate approach
Week 12: Final results, Phase II planning
Continuous Communication:
  • Monthly written reports
  • Bi-weekly email updates
  • Ad-hoc consultation as needed
  • Open-door policy for questions
4. SPECIFIC ADAPTATION SCENARIOS
Scenario 1: Different Vehicle Priority
Current: Focus on HMMWV and logistics trucks
"MRAP vehicles are our highest priority due to ongoing operations"
Our Response:
Immediate Actions:
  • Confirm MRAP variants of interest (M-ATV, MaxxPro, etc.)
  • Identify available MRAP data sources
  • Assess MRAP-specific failure modes
  • Adjust component priorities (MRAP has different critical systems)
Scope Adjustments:
BEFORE:
├── Primary: HMMWV (80% effort)
├── Secondary: Logistics trucks (20% effort)
└── Terrain: General purpose

AFTER:
├── Primary: MRAP specified variants (70% effort)
├── Secondary: HMMWV (30% effort, for comparison)
└── Terrain: Relevant to MRAP operations (e.g., IED-damaged roads)
Impact:
  • Timeline: No change (same methodology applies)
  • Data: Need MRAP-specific CBM data
  • Validation: Adjust test scenarios to MRAP missions
  • Output: MRAP-specific component focus
Scenario 2: Limited Data Access
Current: Assume full 200-vehicle CBM dataset
"Full dataset classified/unavailable. You can have 20 vehicles unclassified data"
Our Response:
Immediate Actions:
  • Assess what's possible with 20 vehicles
  • Request most diverse sample (different terrains, ages, conditions)
  • Identify supplementary data sources
  • Adjust statistical confidence accordingly
Scope Adjustments:
BEFORE:
├── Data: 200 vehicles, robust statistics
├── Validation: 30 test vehicles
└── Confidence: 95% confidence intervals

AFTER:
├── Data: 20 vehicles, focus on patterns
├── Validation: 5 test vehicles, qualitative assessment
└── Confidence: Demonstrate feasibility, not statistical significance

ADAPT:
├── Use transfer learning (models from Kuiper et al.)
├── Increase synthetic data generation (GAN)
├── Focus on case studies vs. population statistics
└── Position as proof-of-concept for Phase II expansion
Scenario 3: Different Use Case Priority
Current: Route planning and maintenance scheduling
"Route planning is nice, but we really need fleet readiness assessment for commanders"
Our Response:
Scope Adjustments:
BEFORE (Route Planning Focus):
├── Input: Proposed routes
├── Process: Terrain analysis + vehicle state
├── Output: Route recommendations
└── User: Mission planners

AFTER (Fleet Readiness Focus):
├── Input: Current fleet status
├── Process: Aggregate vehicle health + mission requirements
├── Output: Readiness dashboard + risk assessment
└── User: Commanders

KEEP:
└── Same underlying technology (LSTM predictions)
└── Just different aggregation and presentation
Scenario 4: Security & Compliance Requirements
Current: AWS cloud infrastructure with standard security practices
"System must meet FedRAMP/FISMA requirements and operate in AWS GovCloud"
Our Response:
Immediate Actions:
  • Confirm specific compliance requirements (FedRAMP Moderate/High, FISMA, etc.)
  • Assess AWS GovCloud migration requirements
  • Identify data classification levels and handling requirements
  • Review encryption, access control, and audit trail needs
Scope Adjustments:
BEFORE:
├── Infrastructure: AWS Commercial Cloud
├── Security: Standard AWS security practices
├── Data: Assume unclassified handling
└── Access: Basic authentication

AFTER:
├── Infrastructure: AWS GovCloud (US-East/West)
├── Security: FedRAMP compliance framework
├── Data: Classified data handling procedures
└── Access: Multi-factor authentication, role-based access
Questions for DoD:
  • What is the target data classification level?
  • Are there specific compliance frameworks required?
  • What are the preferred authentication/authorization methods?
  • Are there restrictions on data storage locations or retention?
Scenario 5: System Integration & Deliverables
Current: Standalone proof-of-concept system
"System needs to integrate with existing maintenance management systems and provide specific deliverables"
Our Response:
API & Integration Questions for DoD:
  • What existing systems need integration (GCSS-Army, ULLS, maintenance databases)?
  • What data formats are preferred (JSON, XML, military standards)?
  • Are there existing API standards or protocols to follow?
  • What authentication methods for system-to-system communication?
Frontend & User Interface Questions:
  • Who are the primary users (maintenance officers, commanders, technicians)?
  • What devices will be used (desktop, tablets, mobile in field)?
  • Are there existing UI/UX standards or style guides to follow?
  • What level of technical detail should be displayed vs. simplified dashboards?
Phase I Deliverables (Baseline):
TECHNICAL:
├── RESTful API for terrain-aware predictions
├── Web-based dashboard for visualization
├── Documentation and user guides
└── AWS deployment scripts

RESEARCH:
├── Technical feasibility report
├── Performance benchmarks
├── Validation results
└── Phase II recommendations
Potential Adaptations Based on Feedback:
  • Custom API endpoints for specific military systems
  • Mobile-responsive interface for field use
  • Integration with existing DoD identity management
  • Compliance with DoD software development standards
5. COMMUNICATION PLAN & SUCCESS CRITERIA
Continuous Feedback Loop Structure
┌─────────────────────────────────────────────────────────┐
│                 CONTINUOUS FEEDBACK LOOP                │
├─────────────────────────────────────────────────────────┤
│                                                         │
│  Week 0: Initial Meeting (this meeting)                │
│  ├── Present baseline approach                         │
│  ├── Collect initial feedback                          │
│  ├── Establish communication channels                  │
│  └── Define feedback mechanisms                        │
│         ↓                                               │
│  Week 1: Feedback Integration                          │
│  ├── Analyze feedback                                  │
│  ├── Update project scope                              │
│  ├── Share revised plan with DoD                       │
│  └── Get confirmation/further feedback                 │
│         ↓                                               │
│  Weeks 2-3: Early Development                          │
│  ├── Progress update                                   │
│  ├── Share preliminary findings                        │
│  ├── Request additional clarification if needed        │
│  └── Adjust based on early learnings                   │
│         ↓                                               │
│  Week 6: Mid-Point Review                              │
│  ├── Demonstrate partial results                       │
│  ├── Validate approach with DoD                        │
│  ├── Get feedback on direction                         │
│  └── Make course corrections if needed                 │
│         ↓                                               │
│  Week 9: Near-Complete Preview                         │
│  ├── Show near-final results                           │
│  ├── Get feedback on outputs                           │
│  ├── Refine deliverables                               │
│  └── Plan Phase II transition                          │
│         ↓                                               │
│  Week 12: Final Delivery + Retrospective               │
│  ├── Deliver final results                             │
│  ├── Discuss what worked / what didn't                 │
│  ├── Plan Phase II based on lessons learned            │
│  └── Establish ongoing partnership                     │
│                                                         │
└─────────────────────────────────────────────────────────┘
Communication Channels
Primary Contact:
  • DoD POC: Julio Toala (M CIV USAF AFMC AFCEC/CBFG)
  • Frequency: Bi-weekly scheduled calls
  • Method: Teams/Zoom + email
Documentation:
  • Shared folder: [SharePoint/Google Drive/Confluence]
  • Version control: All documents versioned and dated
  • Access: DoD team has read access to all work products
Escalation Path:
  • Routine questions: Email to POC
  • Urgent issues: Phone call + email
  • Strategic decisions: Request meeting with stakeholders
Flexibility Boundaries
What We CAN Adjust:
  • CAN: Vehicle types and priorities
  • CAN: Component focus areas
  • CAN: Terrain types and regions
  • CAN: Use case emphasis
  • CAN: Output formats and dashboards
  • CAN: Deployment architecture
  • CAN: Data sources (within reason)
  • CAN: Validation approach
  • CAN: Success metrics
What We CANNOT Change:
  • CANNOT: 6-month timeline (Phase I fixed)
  • CANNOT: Core technology approach (LSTM + Fractal + GAN)
  • CANNOT: Budget constraints
  • CANNOT: Fundamental physics (terrain does affect vehicles)
  • CANNOT: Data science requirements (need sufficient data)
What We Can Discuss:
  • Flexibility: Level of accuracy achievable
  • Flexibility: Scope vs. depth tradeoffs
  • Flexibility: Phase I vs. Phase II features
  • Flexibility: Resource allocation across tasks
6. OUR COMMITMENTS & NEXT STEPS
To DoD:
Listen First
Understand operational needs before proposing solutions
Be Flexible
Adapt our approach based on DoD guidance
Communicate Transparently
Share both successes and challenges openly
Deliver Value
Focus on operationally relevant outcomes
Think Long-Term
Build foundation for Phase II transition
Be Responsive
Reply to questions/concerns within 24 hours
Stay Aligned
Check assumptions early and often
Document Everything
Maintain clear records of decisions and rationale
What We Ask From DoD:
Clear Priorities
Help us understand what matters most
Honest Feedback
Tell us early if we're off track
Data Access
Facilitate access to necessary information
Timely Input
Respond to questions/decisions when needed
Operational Context
Share real-world constraints and workflows
Partnership Mindset
Work with us to solve problems together
Next Steps (Post-Meeting Action Plan)
Immediate (Within 1 Week):
  • Day 1: Send meeting summary and feedback capture to DoD
  • Day 2-3: Analyze feedback and prioritize changes
  • Day 4-5: Draft revised project scope document
  • Day 6-7: Review internally, prepare for DoD confirmation call
Week 1 Deliverable: Updated Project Scope Document v2.0 incorporating all feedback
Near-Term (Weeks 2-4):
  • Week 2: Begin adapted development based on DoD priorities
  • Week 3: First progress report showing alignment with feedback
  • Week 4: Check-in call to validate direction
Phase I (Ongoing):
  • Maintain bi-weekly communication
  • Share work products as they develop
  • Request feedback at decision points
  • Adjust course as needed based on learnings
  • Keep DoD informed of risks/issues
Questions We Hope to Answer Today:
  • What are your highest-priority vehicles and components?
  • What data can we access and what are the constraints?
  • What use cases matter most to end users?
  • How should we measure success?
  • What infrastructure requirements exist?
  • Who are the key stakeholders we should engage?
CONCLUSION
This document represents our framework for incorporating DoD feedback into the FracAdapt Phase I project. We view this meeting as the beginning of an ongoing dialogue, not a one-time event.
Our Philosophy:
"The best predictive maintenance system is the one that solves real problems for real users. We bring technical capabilities, but you bring operational wisdom. Together, we can create something truly valuable."
We look forward to this discussion and to refining FracAdapt to best serve your needs.
Prepared By: GeoGizmodo LLC FracAdapt Team
Date: October 1, 2025
Contact: aneesh@geogizmodoapp.com
Status: Pre-Meeting Discussion Document
Key Personnel
Aneesh Hariharan - Founder & CEO
MS Applied Math, UW Seattle and MS Math & Statistics, Auburn University
One granted patent and three patent-pending applications
Former AWS Technical Instructor specializing in ML and GenAI
Currently teaches at Amazon Cloud Institute (ACI) - Developer Intermediate-2 and AI for Developers courses
Founded GeoGizmodo in October 2024 with a mission to use modeling, AI, and ML to build innovative solutions with lasting impact
Dr. Aditya Khanna - Associate Professor, Brown University
PhD Quantitative Ecology and MS Statistics, University of Washington
Computational social scientist specializing in modeling biobehavioral health systems
Expert in network analysis, agent-based modeling, and predictive analytics for health equity
Research focuses on HIV prevention, substance use behaviors, incarceration, and COVID-19
Affiliations: Center for Alcohol and Addiction Studies, Population Studies and Training Center
Dr. Kathryn Lindsey - Associate Professor, Boston College
PhD in Mathematics, Cornell University (2014)
Associate Professor in the Department of Mathematics at Boston College
Expert in mathematical foundations of deep learning theory and dynamical systems
Research focuses on geometric foundations of deep learning, ReLU neural networks, topological entropy, and Thurston sets
Published extensively on functional dimension of neural networks, hidden symmetries of ReLU networks, and topological complexity measures
Current NSF grant recipient (#2133822) supporting machine learning research
Office: 567 Maloney Hall, Boston College
Alex Zare - Contractor
Title: Amazon Cloud Institute Graduate & Cloud Solutions Expert
Background: Transitioning from Project Leadership to Cloud Innovation. After 7+ years successfully delivering complex, multimillion-dollar projects in construction and healthcare, now channeling passion for problem-solving into cloud technology and full stack development.
Education: AWS Cloud Institute program (graduating September 2025)
Expertise: AWS architecture and infrastructure as code (IaC), Complex project management from conception to delivery, DevOps, cloud automation, and secure solution design, Serverless computing, CI/CD pipelines, and scalable cloud architectures
Experience: Strong foundation in stakeholder management and system optimization, combined with cutting-edge cloud solutions and real-world application through labs, capstone projects, and personal cloud applications.