Read the latest issue of the magazine dedicated to corrosion prediction, prevention, protection and mitigation!
Corrosion always leaves a trace, and over more than a century, humankind has learned to decipher it: a rust stain, a flaking coating, a loss of thickness, a crack. This ability to interpret the past gave rise to modern corrosion protection engineering. Today, however, we are entering a new phase: understanding what has happened is no longer enough; we must be able to predict what will happen.
The future of corrosion is increasingly shaped by the collection, interpretation, and management of data, with a shift from reactive to predictive approaches.
But what data? Field data: valuable information that gives us a real-world picture of corrosion, outside the controlled conditions of the laboratory.
Every inspection carried out on a pipeline, every survey conducted on an offshore platform, and every thickness measurement recorded during a maintenance shutdown are much more than a simple snapshot of a plant’s current status. These are fragments of knowledge that, if collected and organised correctly, describe the actual behaviour of materials under their real operating conditions.
For years, this data has been confined to paper reports, spreadsheets, and archives that were difficult to consult. Today, however, it is becoming the heart of a new generation of digital tools capable not only of storing information but also of transforming it into predictions.
Artificial intelligence algorithms are, in fact, learning to recognise correlations that even the most experienced specialists may overlook. By cross-referencing thousands of inspections, climate data, operational parameters, and maintenance schedules, they can identify recurring patterns, estimate the likelihood of deterioration, and recommend when and where to take action.
This evolution finds its most tangible expression in digital twins. A digital twin is not simply a virtual replica of a structure: it is a digital organism that grows alongside the physical asset, is continuously updated with field-collected data, and enables the simulation of the system’s future behaviour. It is like a permanent laboratory where different scenarios can be tested without interrupting production or waiting for damage to occur.
The positive implications of this data-driven future are clear. Inspections can be planned based on actual risk rather than on pre-set deadlines. Financial resources are channelled where they are genuinely needed. Errors of judgement are reduced because decisions are based on a much greater volume of information than in the past. And maintenance ceases to be a reaction to emergencies and becomes a strategic planning tool.
Of course, the quality of the data collected in the field, its standardisation, and the ability to share it across the supply chain constitute the human element of this approach: designers, inspectors, installers, plant operators, and technology manufacturers all play a vital role in this data-driven culture.
For many years, we have regarded corrosion as an inevitable phenomenon that can only be contained. Now, we are beginning to treat it as a process that needs to be understood, controlled, and anticipated. We will be glad to discuss this with exhibitors and visitors at numerous trade fairs across various industrial sectors this autumn: SMM and Wind Energy in Hamburg, Innotrans in Berlin, Eurocorr in Dublin, ENES2026 in Warsaw, and AIM in Brescia.
The future of corrosion management lies not only in materials or coatings, but also in the ability to transform millions of data points collected in the field into better decisions. This is perhaps the most advanced form of protection technology can offer us.
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