AI Damage Prediction System for Rapid Structural Assessment
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Solution Overview
Problem
Current methods for assessing damage to structures after natural disasters or hazards are time-consuming and require manual inspections, especially when multiple structures are affected, leading to delays in processing insurance claims.
Innovation Solution
A system utilizing artificial intelligence and aerial vehicles equipped with cameras to rapidly assess structural damage by obtaining hazard data, identifying impacted parcels, generating flight paths, capturing images, and using machine learning models to annotate potential damage and estimate damage scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used to assess structural damage, then measurement precision can be maintained, but productivity decreases significantly when multiple structures are affected
Solution Approach 1:
The patent uses aerial vehicles to capture images and creates digital copies of structures from multiple angles. These image copies are then analyzed using machine learning models to identify damage, replacing the need for physical manual inspection while maintaining assessment accuracy through detailed visual documentation
Solution Approach 2:
The patent replaces the mechanical manual inspection process with an automated system combining aerial vehicles, camera systems, and machine learning algorithms. The machine learning model automatically analyzes captured images to detect and classify damage, substituting human inspectors with an automated computational system that operates faster and handles multiple structures simultaneously
2Productivity
If automated aerial vehicle inspection is implemented, then productivity increases for assessing multiple structures, but device complexity increases
Solution Approach 1:
The aerial vehicle system is designed as a multi-functional platform that combines navigation, image capture from multiple angles, and integration with machine learning analysis. This universal system can assess various structure types (buildings, bridges, infrastructure) using the same core technology, managing complexity through standardized processes
Solution Approach 2:
The patent introduces an image processing and machine learning analysis system as an intermediary between the aerial vehicle capture process and the final damage assessment. This intermediary layer automatically processes images, identifies damage patterns, and generates reports, reducing the operational complexity of coordinating multiple vehicles and inspectors
3Measurement precision
If comprehensive image capture from multiple angles is performed, then measurement precision improves for damage identification, but loss of time increases due to extended flight paths
Solution Approach 1:
The system performs preliminary actions by capturing images from multiple angles during a single coordinated flight sequence before analysis begins. The aerial vehicle is pre-programmed with flight paths that ensure comprehensive coverage, and images are captured and stored for later batch processing, eliminating the need for multiple return flights
Solution Approach 2:
The patent maintains continuity of useful action by capturing images continuously along predetermined flight paths without interruption. The aerial vehicle flies through programmed routes capturing images at multiple angles in a continuous sequence, maximizing data collection efficiency while minimizing total inspection time through uninterrupted operation
Data Source
AI summary
A damage prediction system that uses hazard data and/or aerial images to predict future damage and/or estimate existing damage to a structure is described herein. For example, the damage prediction system may use forecasted hazard data to predict future damage or use actual hazard data to estimate existing damage. The damage prediction system may obtain hazard data in which structures were or will be impacted by a hazard. The damage prediction system can then generate a flight plan that causes an aerial vehicle to fly over the impacted parcels and capture images. The damage prediction system can use artificial intelligence to process the images for the purpose of identifying potential damage. The damage prediction system can also use a hazard model, the hazard data, and structure characteristics to generate a damage score. The damage prediction system can then use the processed images and/or damage score to generate a virtual claim.