Intelligent bridge monitoring through neuronally networked sensors

Time frame: 01.08.2013 – 31.07.2016

As the most important Central European transit country, Germany faces the challenge of maintaining and continuously expanding the efficiency of its transport infrastructure. In addition to the construction of new routes or the expansion of existing ones, the maintenance of the infrastructure plays a key role. Neuralgic structures are above all bridges, since their maintenance or replacement entails enormous investment costs and thus has a major impact on the national economy. In principle, considerable expenditure is required to guarantee and maintain the availability, traffic safety and service life of bridge structures. Against this background, it is essential to identify damage or potential damage as early as possible. The aim of the “iBridge” project, funded by the BMWi as part of the KMUinnovativ programme, is to develop a system for monitoring a bridge structure in real time using a neural sensor data processing system and fibre-optic sensors (FBG and Rayleigh system).

In contrast to today’s technology, the focus of the evaluation is no longer on individual sensors and their measured variables. Instead, all sensor data is merged and processed in an appropriately programmed neural network. Such “intelligent” bridge monitoring makes it possible to implement a self-configuring monitoring system that informs the operator in detail about the actual state of the bridge structure. The provided data inform in real time about the stability and thus allow to initiate measures depending on usage/wear and thus efficiently and at the “right time”. This makes a considerable savings potential usable, as bridges can be maintained more effectively. Annoying traffic closures, the economic impact of which should not be underestimated, can be shortened or avoided altogether. Follow-up costs are reduced due to less costly renovations and decisions on replacement new buildings can be made more efficiently on the basis of well-founded data through reliable analyses and forecasts.

As a project partner, the Institute of Sensor and Actuator Technology (ISAT) is responsible for the selection of fiber optic sensors, the validation of the sensors, the generation of test data sets for the development of the neural network, and the integration of the sensors into the network.

Project partner:

  • Company Pötzl Ingenieure GmbH
  • Company Ci-Tec GmbH
  • Karlsruhe Institute of Technology – Institute of Applied Computer Science