Digital Twins

This allows organizations to:

• Detect issues at an earlier stage
• Plan maintenance based on actual conditions
• Reduce operational risk
• Optimize investments
• Manage infrastructure over the long term
• Run simulations in real spatial conditions with accurate vessel dimensions and parameters such as wind, sea level, and other operational factors

• Monitoring infrastructure condition over time
• Making maintenance decisions
• Planning investments
• Reporting and documenting the condition
• Integrating with operational data and stimulations
• Linking with vessel movement data (AIS) and real operational conditions in ports

In other words, data is not used only for visualization, but for making concrete and timely decisions. If the data is not collected and verified according to appropriate standards, such decisions may be based on incorrect assumptions.

The quality of the record depends on:

• The method of data collection
• Control of recording conditions
• Model processing and verification
• Integration into a unified coordinate system

A digital twin is not just a mode, but a controlled and verified system for documenting underwater infrastructure. Deviations in any of these steps can result in loss of accuracy, textural anomalies, or geometric errors that are not always visually apparent.

• Data remains the property of the client
• It can be used over multiple years
• It enables comparisons across different maintenance cycles
• It is structured in a way that supports long-term use across various systems

In addition, data quality must be demonstrable through measurable parameters and control metrics to ensure long-term reliability.

Advanced AI models enable automatic detection of cracks and degradation based on large volumes of labelled data from real infrastructure projects. This further increases the reliability of the analysis and allows for earlier identification of risks.