Advanced Sensors and Modelling for Next-generation Bridge Management

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The UK's transport networks, including bridges, are critical to its economy. The vital importance of bridges to the economy is most evident when a bridge is briefly out of service, e.g. (i) the Hammersmith flyover in 2011 and (ii) a bridge near junction 3 on the M1 in 2012: (i) was closed due to the sudden discovery of corroded steel tendons, and (ii) was closed following a fire under the bridge. These closures caused massive disruption and the resulting cost to individuals/businesses for disrupted journeys was very significant. Maintaining the large UK bridge stock permanently in service is challenging for the responsible organisations.The current state of the art in bridge management is periodic visual inspection of the bridge by suitably trained inspectors. The limitation of the current system is that it provides no quantitative information on the in service bridge behaviour, therefore decisions are based on visual information only which can be subjective depending on the experience of the inspector. The improvement proposed in this project is to add novel sensing and data modelling to existing visual inspections. The project exploits the fact that in current bridge management practice, bridges in a given geographic area tend to be inspected together over a period of days or weeks. This means that there is an opportunity to lay out customised, easy to mount sensors on all the bridges to be inspected on the first day of the inspection block, carry out the inspections as normal then lift the sensors on the last day. Doing it this way is cost-effective, adding relatively little cost to the existing inspection regime but will provide the opportunity to obtain structural behaviour data to supplement visual information. However to implement this new system challenges in two specific areas need to be addressed:(a) DATA COLLECTION; how to record data of adequate quality with low financial and operational cost. Conventional sensing systems are expensive, require on site power and therefore are not compatible with the current bridge inspection regime. In this project we put forward a novel system to address this.(b) DATA INTREPERATION & NORMILISATION; how to convert the recorded data into information useful for decision making. How a bridge deflects under load, or how it vibrates can be indicators of its condition. So changes in how the bridge moves can indicate change/deterioration in a bridge's condition. Unfortunately the magnitude of a given bridge's movements are also significantly influenced by environmental factors such as temperature. Therefore a method to separate the effect of potential damage or deterioration on the bridges movements, from those movement changes caused by environmental variation is required. This 'cleaning' of the data is often referred to as data normalisation. In this project, this separation will be carried out using a novel computer algorithm/model which will be developed as part of the project. The benefits of the proposed system is more efficient bridge management with reduced traffic disruption. For example it is likely that having a baseline of performance data for the both bridges (i) and (ii) above would have mitigated the difficulties. Since for (i) the problem may have come to light sooner and for (ii) the bridge could have been reopened earlier. This use of sensor data during regular bridge inspections represents a step change to current practice, as the quantitative information obtained will allow better direction of limited bridge maintenance budgets and facilitate greater resilience of bridges to shock events.