The centralised dashboard offers a clear, real-time overview of all critical data, enabling swift and informed decision-making. As a turnkey solution, it seamlessly integrates sensors, data analysis, and monitoring, ensuring secure and efficient infrastructure management.
The platform provides a quick snapshot of the entire network’s condition, leveraging an intuitive interface that combines geographical data, key metrics, and colour-coded indicators for easy interpretation of bridge health. This clear and detailed view enables an effective planning of in-depth interventions.
Within the platform, each asset has a dedicated detailed page, featuring activity history and advanced structural analyses. These insights, integrated into a 3D model, enable the optimisation of inspection activities and maintenance interventions.
The real-time notification system alerts users when critical thresholds are exceeded, enabling rapid risk response and efficient maintenance planning. In the event of an anomaly, the platform delivers precise insights into the issue’s severity and location, powered by proprietary AI-driven algorithms.
Thanks to proprietary algorithms developed through years of field research, Displaid has created tailored indicators to monitor the most critical damage for each bridge type, enabling precise, localised anomaly detection. The application of artificial intelligence to analyse complex factors, such as traffic and environmental effects, reduces KPI variability, minimising false positives and ensuring early identification of anomalies.
Displaid has developed and validated a fully automated modal analysis algorithm that initializes quickly and scales seamlessly across multiple bridges. The modal parameters, adjusted for temperature effects, are displayed in real-time control charts on the platform, which provides a visualisation of trends in natural frequencies, damping, and 3D vibration modes.
Thanks to proprietary algorithms developed through years of field research, Displaid has created tailored indicators to monitor the most critical damage for each bridge type, enabling precise, localised anomaly detection. The application of artificial intelligence to analyse complex factors, such as traffic and environmental effects, reduces KPI variability, minimising false positives and ensuring early identification of anomalies.
Displaid has developed and validated a fully automated modal analysis algorithm that initializes quickly and scales seamlessly across multiple bridges. The modal parameters, adjusted for temperature effects, are displayed in real-time control charts on the platform, which provides a visualisation of trends in natural frequencies, damping, and 3D vibration modes.
The platform allows for the automatic generation of reports, customisable according to specific parameters of interest and different levels of detail. This approach ensures clear, targeted communication tailored to the recipient’s needs. Reports range from a general overview of the infrastructure’s status for efficient archiving to the download of comprehensive historical data for in-depth analysis, ideal for supporting inspections and maintenance operations
