SensIT is an innovative research concept that aims at bringing together several enabling technologies to create a new way to monitor, evaluate and inspect the civil infrastructure. SensIT combines different cutting-edge technologies to deliver a decision-support tool for the owners and managers of today's infrastructure.
A network of individual sensors, strategically distributed in a structural element, are used to monitor a set of physical and mechanical parameters governing the health state of the structure. Sensor data is then transferred to an IoT gateway which streams the data to the cloud for storage and subsequent analysis.
Machine Learning algorithms facilitate the reduction of the generated data volume in favour of increased data value. Moreover, with the right type and amount of data, these algorithms can be trained to make predictions regarding the evolution of degradation processes and the decay of structural performance.
Common numerical analysis techniques, such as the Finite Element Method, can be significantly enhanced by combining advanced material models with real-time sensor data. This hybrid approach can lead to the obtention of a more detailed and accurate description of the structure’s health condition.
Critical information must be conveyed in a clear, efficient and intuitive way to the inspectors and infrastructure managers. SensIT features a cross-platform web-based BIM viewer application which is built upon free open-source software to deliver a user-friendly interface that provides an effective and hassle-free user experience.
A wireless sensor network is used to monitor several physical and envinronmental parameters of the structure.
Artificial Intelligence algorithms specifically developed and trained for structural health monitoring purposes can be used to detect the occurrence of a damage event along with its location and magnitude based on sensor data.
Mechanical and physico-chemical numerical analyses retrofitted with real-time sensor data can be used to deliver accurate predictions of the service condition of the structure.
An interactive cross-platform tool relying on web-based BIM can enable infrastructure owners and operators to easily assess the health condition of a structure, anywhere, anytime, thanks to an intuitive user-interface.
Augmented Reality can help reducing the time and operational cost of inspections while enhancing their effectiveness.
A group of reinforced concrete beams outfitted with distributed optic fiber sensors (DOFS) and subjected to 4-point loading is being continuously monitored at the laboratory of Structural Engineering at Chalmers University of Technology. The main objective of this application is to develop and train machine-learning algorithms that can detect and quantify concrete cracks based on the strain distribution patterns measured at the reinforcement.
Hönöverket is a research wind power turbine on the island Hönö, outside Gothenburg. The turbine will be renovated and is being moved to a new location. As part of the renovation and moving of the turbine, a new reinforced concrete foundation will be cast. An extensive network of strain sensors combining vibrating wire strain gauges and polymeric optic fibre will be installed in the foundation to evaluate the real stress (strain) state of the structure which could lead to improved calculation tools for optimized structural design.
A rockfall protection shed structure located in the Garraf area, outside Barcelona city (Spain) is currently undergoing a thorough reburbishment due advanced corrosion damages in the structure. As part of the upgrade planned for the structural elements, various sensors are going to be installed to monitor the effectivity of the paliative measures applied (cathodic protection, sacrificial anodes) while acting as an alert system for future potential corrosion damage.