The Impact of Digital Twins on Infrastructure Maintenance

The future for infrastructure related industries such as engineering, and construction looks bright according to sources such as “Forum of the Future” and “Global Infrastructure Hub”. Although they are mostly referring to the world population growth and the related urbanization as “engines” for investment in new infrastructure, one aspect should not be neglected and that is the ageing infrastructure in the industrialized nations in Europe or Northern America. The maintenance of existing infrastructure such as highways, railroad networks or bridges will become more important to keep the infrastructure alive and “competitive”.

The challenges connected with infrastructure maintenance include the vast number of infrastructure items and the way they are inspected and maintained today: manually.

Digital Twins offer a lot of potential to make maintenance more efficient. Reliable assessments of the infrastructure will need well organized lifecycle information from design to operation for many years. A Digital Twin e.g. of bridge could carry such information. Damage records could be linked to the objects of the bridge. Environmental conditions such as temperature, humidity and loading history could be used for analysis and predictive maintenance purposes. Based on algorithms, the bridge`s structure and condition will be analyzed. The results will be visualized in real-time in the digital twin. This performance twin will help to prioritize and improve the performance and reliability of the construction. Prerequisites are the integration of sensors into the bridge`s structure and the scanning of the bridge via e.g. photogrammetry to generate reality respectively 3D models of the bridge. The model will be updated by regular, recurring scans and by data resulting from inspections, the monitoring of the sensors and maintenance activities. A first example is the creation of a Digital Twin for a bridge on London`s Metropolitan Underground line. Other areas of application include road networks. Here the Digital twin will be used to spot and self-report damages to the road surface. Furthermore, it will be used to predict and proactively manage road operation and maintenance also below the surface.

Digital Twins will allow for more targeted and accurate maintenance of infrastructure objects. Furthermore, they will also pave the way for an array of simulations e.g. how the infrastructure will “behave” in case of a collision of two trains. Therefore, damage becomes predictable. Despite all the benefits brought up by digital twins, one could raise the objection that this will incur a lot of effort given the thousands of roads and bridges in an infrastructure network. This is true, and first ideas have been created to ease the effort including the concept of creating a standard or base digital twin that incorporates standard “features” found in any particular type of infrastructure. This leaves only minor adaption work to the individual features of the object. This approach is currently discussed for bridges but also electric power transformation substations. Target is to fully exploit the benefits of a Digital Twin without incurring too much effort.