DNV and Havtil’s collaboration on AI-driven safety improvements targets dropped object incidents in offshore industry

DNV and Havtil’s collaboration on AI-driven safety improvements targets dropped object incidents in offshore industry

(IN BRIEF) DNV and the Norwegian Ocean Industry Authority (Havtil) have launched a collaborative project to enhance offshore safety using artificial intelligence (AI). Their report, focusing on reducing risks related to dropped objects, demonstrates how AI can extract valuable insights from historical data and improve safety outcomes. The AI tool, paired with ontologies, helps safety teams analyze and interpret complex data, offering immediate insights into safety incidents. This innovative approach could revolutionize the way safety is managed offshore by providing data-driven solutions for incident prevention.

(PRESS RELEASE) BÆRUM, 7-May-2025 — /EuropaWire/ — DNV has partnered with the Norwegian Ocean Industry Authority (Havtil) to explore the potential of artificial intelligence (AI) in improving safety in offshore operations, particularly in addressing the issue of dropped objects. The report from this collaboration, titled “Use of Artificial Intelligence to Reduce Danger and Risk of Accidents Due to Falling Objects,” demonstrates how AI can be used to enhance the learning process from historical data, improving safety practices in the industry.

The Norwegian government’s supervisory agency, Havtil, is responsible for overseeing safety, working environment, emergency preparedness, and security on the Norwegian continental shelf. Over the years, Havtil has collected a wealth of data through inspections, investigations, and damage analyses, but the data has been difficult to access and utilize for systematic learning. DNV’s report highlights the need for new technology to make this valuable data more accessible and actionable for improving safety outcomes.

Morten Langøy, Principal Engineer at Havtil, shared, “Our collaboration with DNV on developing an ontology for falling objects is a promising step forward for using AI on industry safety data. We plan to continue expanding this project alongside industry experts to create a more comprehensive model for incident causation.”

Pilot Project Details:

The pilot project utilizes a prototype AI tool paired with ontologies to enhance the AI’s understanding of data relationships. Ontologies, or knowledge graphs, help organize complex data and represent knowledge in a structured, systematic way. By using AI, safety teams can now query the system to analyze data, asking questions about patterns and correlations in safety incidents. This technology eliminates the need for manual document review and provides immediate insights into complex safety data, offering a level of analysis that would be difficult for humans to achieve on their own.

David R. Watson, Innovation Lead at DNV – Digital Solutions, stated, “This report explores a promising approach to leveraging new technology to mitigate risks in the industry, particularly through the use of ontologies combined with AI.”

The AI tool, distinct from large language models like ChatGPT, is designed to work with domain-specific ontologies created by industry experts. This ensures that data is understood and interpreted in the correct context, increasing the accuracy and reliability of the results. The system also offers full transparency, allowing safety professionals to review the queries made and the data sources used, ensuring trust in the AI-generated insights.

AI’s Potential in Offshore Safety:

The AI system can answer practical questions posed by safety professionals, such as, “What are the most common causes of falling objects during lifting operations on platform X?” or “Are there correlations between the time of day and the severity of falling object incidents?” By analyzing historical data, the AI identifies important patterns, such as how specific weather conditions or crew shift patterns might contribute to incidents.

Furthermore, the system’s transparency safeguards and maintains data security by showing users the queries and keeping a detailed log of all actions taken by the AI, allowing for full verification of any analysis performed.

The Future of Offshore Safety:

DNV’s report indicates the potential of using industry-wide data, collected anonymously, to revolutionize safety practices. If successful, the system could transform safety management, offering deeper insights into the underlying causes of safety incidents across the offshore sector.

DNV’s long experience in ontology engineering, developed over two decades, positions the company to lead the way in integrating AI and digitalization into maritime and offshore safety. Their work continues to push boundaries, developing digital reference structures and supporting industry-wide collaborations to enhance safety standards and drive efficiency.

About the pilot project

DNV is building an architecture that uses a prototype AI tool paired with ontologies to increase the AI’s understanding of the data’s relationships and eliminating hallucinations, when the model generates false, misleading or nonsensical information that appears plausible but isn’t based on real data. An ontology, also known as a knowledge graph, is a structured framework that organizes information into categories and defines the relationships between them. It helps in understanding and managing complex data by providing a clear and systematic way to represent knowledge.

Safety teams would, instead of manually finding and reviewing hundreds of safety reports and other documents, ask the AI to analyse the information, gaining immediate insights that would be difficult or impossible for humans to see without the help of the technology. A strength of the tool over common large language models such as ChatGTP or Copilot is its connection with an ontology developed by domain experts. This ensures that data is understood in the correct context. The tool also offers full transparency of the data query used and the data sources, increasing the users’ trust in the output for sound business decisions.

“The report explores a promising approach to leveraging new technology to mitigate risks in the industry, particularly through the use of ontologies combined with AI,” says David R. Watson, Innovation Lead at DNV – Digital Solutions. 

The potential is for AI and large language models to be trained on massive amounts of collected data regarding safety incidents. Some of the most valuable information is locked in detailed investigation reports that are hard for the AI to access. Extracting the key facts is a significant challenge, as is teaching the AI to understand the complexity and nuance of human reports.

What can safety professionals ask the AI?

The AI can answer practical questions that safety professionals might ask through an interface that understands natural language. They could ask questions such as “What are the most common causes of falling objects during lifting operations on platform X?” or “Is there a correlation between the time of day and the severity of falling object incidents?” or “What were the common contributing factors in dropped object incidents in the last year?”

The system can then use historical data and make sense of the results. It summarizes the findings and highlights the most important patterns, presenting them in a way that’s easy for humans to understand. For example, it may discover that a certain type of dropped object incident on platform X is more common when certain weather conditions and crew shift patterns coincide.

Safeguards include transparency and data security. For example, the system shows the user the query so the expert can double-check it. It also keeps a process log of everything the AI does, step by step. Any answer can be checked, showing which queries have been run and which data were used.

The report highlights the potential of a new era of safety management. If the system could use data gathered from across the industry in an anonymized database. This could revolutionize safety practices by analysing patterns industry-wide.

About ontology engineering at DNV

DNV’s long experience in maritime and oil and gas classification includes working with structured vocabularies and rules. As the amounts of data have grown in size and complexity, digital reference structures are needed. DNV’s work in ontology engineering spans more than two decades. Digitalization and artificial intelligence are increasing the power of ontologies, giving them the means to transform industries. In addition to developing ontologies for customers, the ontology engineering team at DNV runs joint industry projects (JIPs) and organizes ontology engineering workshops, bringing industries and domain experts together.

Media Contact:

David Watson
Innovation lead
Phone: +4746841768
David.Ross.Watson@dnv.com

SOURCE: DNV