Proprietary algorithms

Displaid’s data analysis algorithms are based on years of academic research in structural engineering and infrastructure monitoring, developed in collaboration with Politecnico di Milano. The methodologies draw on scientific studies, international publications, and field experimentation on real infrastructure. This ensures each algorithm is grounded in solid engineering principles, adapted and validated for operational structural monitoring of bridges and viaducts.

Yes. Displaid algorithms have been applied and validated on numerous real bridges and infrastructure networks, both in research and operational projects. Large volumes of data collected over time have confirmed algorithm performance under real and variable conditions, enabling Displaid to offer mature, reliable solutions for continuous infrastructure management.

Displaid uses AI to support engineering, not to replace physical models or technical expertise. AI analyses large datasets, detects significant variations in structural behaviour, and enables scalable monitoring across extensive networks. Displaid algorithms identify recurring patterns, reduce the influence of external factors like traffic and temperature, and improve analysis stability over time. All models are integrated within a transparent engineering framework, with physically interpretable indicators linked to structural response mechanisms.

The AI approach is not a black box: every result is verifiable and subject to dual algorithmic and engineering control, ensuring traceability, physical consistency, and reliable information for asset managers.

Displaid algorithms systematically cover the main damage mechanisms relevant to each bridge type, going beyond simple global behaviour observations. Unlike conventional approaches, which often struggle to interpret non-linear effects induced by traffic, temperature, or variable environmental conditions, Displaid combines engineering models and AI to separate these effects from signals attributable to actual degradation. This allows not only anomaly detection but also the identification, localisation, and interpretation of damage phenomena, linking them to specific structural elements and physical mechanisms. The result is more reliable, time-comparable information that directly supports targeted inspections and maintenance planning.

Sensor data are processed through algorithms that convert them into synthetic indicators directly linked to specific structural damage mechanisms, such as foundation scouring, corrosion, or bearing deterioration. This information is presented clearly and understandably, supporting operational decisions. The goal is to transform raw data into actionable insights to plan inspections, prioritise interventions, and manage the infrastructure lifecycle more efficiently.