Keywords
Description
Casper is a Challenge-Based Innovation for Artificial Intelligence (CBI4AI) project which aims to develop a novel control-based monitoring system approach to arrest the propagation of failures in building structures. This is achieved by optimizing the placement, communication, data interpretation, analytics, and maintenance of building safety measurement devices in corners and/or outside walls.
Achieving resilient societies requires designing buildings and infrastructures able to withstand and recover from extreme abnormal events. Such events often cause local-initial damage to critical elements of building structures. The current building monitoring systems for preventing collapse aim to identify signs of damage or wear through manual inspections and non-destructive testing (NDT) techniques, such as ultrasound, radiography, and magnetic particle inspection. Although these strategies have been shown to be valid in certain situations (e.g. small initial failure in inaccessible areas of the building), there are other situations (e.g. initial failure of several important components, human error, failure in not accessible areas) in which the fundamental components of the structure (e.g. foundation, plinth, superstructure) can help to propagate a local failure and lead to building collapse. It is therefore both urgent and necessary to define new control approaches to remedy these limitations to mitigate the risk of disaster and improve the security of our buildings.
The Casper project aims to develop a new monitoring system approach that combines data gathering and storage from accessible and inaccessible areas of buildings with ultrasonic sensors and Ultra Ram technology, and real-time data processing with AI technology. This approach is radically different from current strategies and involves connecting real-time data from all areas of the building through the LoRaWAN network to Ultra Ram technology, which will provide information on the current state of building integrity, real-time insights, proactive monitoring, and seamless communication.
Therefore, the Casper device is able to predict small initial failures when failure propagation is avoidable. This approach to the building collapse problem also finds its foundations in the lack of specialized personnel for its use, which in many countries is difficult to find, due to a lack of education or low population density. Even more, it does not require personnel at all. The device will operate by itself warning people living in the building when any risk of fall is reached.
