Leveraging AI to Accelerate Data Collection in Risk-Based Inspection Projects
by Stephen Elmer, 10/27/2025 Be the first to commentTags: API 580 API 581 Data Analysis Data Collection Data Management Data Migration Data Validation Risk Based Inspection Technology Value
Data collection is an RBI project bottleneck, often taking a very significant percentage of total effort due to manual interpretation of old, varied engineering documents. AOC developed AI-driven tooling to automatically extract, classify, and normalize this data, achieving a very high accuracy and significantly streamlining the process for faster, more reliable RBI implementation.
For decades, data collection has represented one of the most significant challenges in Risk-Based Inspection (RBI) implementations. Before analysis can begin, engineers must locate, interpret, and enter large volumes of information from historical design, fabrication, and inspection documents - a process that can account for as much as 60% of total project effort.
Many of these records exist only as scanned images, often decades old, and vary widely in format, structure, and readability. Extracting data from such a diverse collection of forms has traditionally required extensive manual work, with all the inefficiencies and potential for error that manual entry entails.
Recognizing this bottleneck, AOC's software development team explored how recent advances in artificial intelligence could streamline the process. Using Google Document AI and Vertex AI, we developed a proof-of-concept tool that automatically extracts, classifies, and normalizes data from scanned engineering documents.
The system is trained to recognize key data fields across multiple generations of standard forms, interpret handwritten or typed entries, and convert them into a structured, machine-readable format. A subsequent AI-based normalization process ensures that units, formats, and lookup values align with project and software requirements. Results with confidence levels below a configurable threshold are automatically flagged for engineer review.
Early results have been promising. The tool now achieves an average extraction accuracy of approximately 94%, significantly reducing manual effort and improving data consistency. Engineers report that the process not only saves time but also improves confidence in the quality of imported data.
Ongoing work is focused on adding anomaly detection and additional validation capabilities to further enhance reliability.
At AOC, we continue to look for ways to combine our engineering expertise with emerging technologies to deliver practical solutions that make our clients' projects more efficient, accurate, and cost-effective.
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