EVENFLOW Use Cases: Infrastructure Life Cycle Assessment
A growing number of applications rely on AI-based solutions to carry-out mission-critical tasks, many of which are of temporal nature, dealing with ever-evolving flows of information. Crucial for mitigating threats and taking advantage of opportunities in such domains, is the ability to forecast imminent situations and critical complex events ahead of time. The EVENFLOW project works on developing hybrid learning techniques for complex event forecasting, which combine deep learning with logic-based learning and reasoning into neuro-symbolic forecasting models.
EVENFLOW conducts research work on three challenging use cases related to i) oncological forecasting in personalised medicine, ii) safe and efficient behaviour of autonomous transportation robots in smart factories and iii) reliable life cycle assessment of critical infrastructure.
What if we could check, in real time, the health status of any water pipe in our underground network and be able to predict malfunctions and points that need to be maintained? In EVENFLOW we aim to utilize the sensor infrastructure in the public water pipe network and use our advanced complex event forecasting techniques to make accurate, reliable and explainable predictions.
Using this technology, the sensors in the pipes will be able to gather data regarding the healthy status of the pipes themselves (cracks, leakages, etc.) and regarding the fluid conditions (temperature, pressure, flow etc.) in real time. EVENFLOW’s AI algorithms will process them and provide timely predictions. The algorithms, through the use of neurosymbolic learning and other techniques developed in EVENFLOW, will not only provide high-quality predictions but will also be able to shed light on the reasons behind them. This will help to optimise the maintenance and management policies and activities of the public infrastructure operator, especially in trenchless interventions and reduction of maintenance costs.