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Rapid development with LLM and AI-driven automated analysis solution for infrastructure inspection and structural deterioration diagnosis

Rapid Construction of Inspection Models Using LLM
Automated Analysis of Damage and Deterioration Based on On-Site Photo Data
Centralized Management and Visualization of Analysis Results via Dashboard

For Various Industries Industry-Specific Use Cases

01. Construction & Civil Engineering (Structure Inspection & Deterioration Diagnosis)

Use Case: AI automatically detects and classifies cracks and delaminations in tunnels and bridges.; Quick processing of extensive infrastructure images captured by drones and cameras.; Link past inspection data with AI analysis results to predict the progression of deterioration.

Effects: Reduces missed detections and improves inspection quality compared to visual inspections.; Significantly shortens inspection time on site and improves work efficiency.; Enables planned repair work and optimizes maintenance costs.

02.Transportation Infrastructure (Railway Facility Maintenance & Safety Improvement)

Use Case: Automatically identifies minor damage to railway structures (viaducts, tracks).; Immediately shares inspection results via a dashboard to identify urgent areas.; Regular data collection allows for time-series monitoring of facility health.

Effects: Reduces inspection burden on inspectors and enables efficient inspections outside of normal operating hours.; Early detection of abnormalities reduces the risk of major accidents.; Supports data-driven decision-making necessary to maintain safety standards.

03.Manufacturing (Equipment Maintenance & Quality Control)

Use Case: Detection of cracks, rust, and other defects in operating production line equipment and piping from images.; Used in product appearance inspections to learn and determine patterns of minor defects and scratches.; Applies AI for remote inspection of equipment in hazardous or elevated locations, reducing labor costs.

Effects: Prevents unexpected equipment failures (downtime) in advance.; Achieves inspections with consistent quality standards without variability among inspectors.; Ensures safety of inspectors and establishes an inspection system not reliant on expert knowledge.

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