Advanced Sensors and Modelling for Next-generation Bridge Management

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.

This project will apply the latest low cost sensing technology and data modelling approaches to bridge management. This will transform existing bridge inspection approach into quantifiable structural performance monitoring, and allow bridge managers to make optimal decisions for cost effective bridge maintenance.

In an economic sense, initially the primary beneficiaries will be UK transport infrastructure organisation such as Highways England (HE) and Network Rail (NR) who are responsible for managing bridge infrastructure. For this impact to be realised the key is to communicate the findings of the project to relevant stakeholders. This will be achieved by engaging with these key stake holders at events such as the Bridge Owners Forum (BOF) and the UK bridge industry conference 'Bridges' which runs annually.

On the commercial side there is the potential that existing companies providing structural monitoring services will be in a position to use the research outcomes from this project to develop new products and procedures e.g., a commercialised version of the sensors and data modelling being trialled in this project. Again the key to facilitating this impact is communicating the findings of the project to relevant commercial companies. To ensure that pertinent information is communicated effectively to commercial organisations a number of steps will be taken. (i) Publication of an article on the project in a professional magazine for bridge professionals e.g., 'The Structural Engineer' or 'New Civil Engineer'. (ii) Participating in 'Bridges' conference will be another avenue to engage with companies who could potentially commercialise the findings of this research project.

Another impact of the project will be on the skills of those directly involved with the project. In particular the Post Doctoral Research Assistant (PDRA), PhD student and the Principal Investigator (PI). The skills of the PDRA will be enhanced by providing them training in a range of activities e.g. training on specific data modelling techniques, technical writing, effective presentations, time and project management. Depending on the activity the training will be delivered by appropriate experts primarily the PI or other project collaborators. The school funded PhD student who the PI will be supervising during the project will also benefit from the 'on the job' training/advice they receive when assisting the PDRA with the site work on the project. The skills of the PI will be enhanced and the procedures described below are in place to grow skills in key areas critical for future career development namely (i) technical skill: following the PDRA's training on data modelling in University of Sheffield (UoS) these skills will be passed on to the PI during the remainder of the project (ii) Managing Research Project: The PI will be responsible for managing this project and his skills will be enhanced via guidance from the project advisory committee which comprises senior staff members from the School of Natural and Build Environment (SNBE).

Within QUB a wide group of students will also benefit. This will be achieved by introducing some aspects of the project into the undergraduate curriculum, primarily via supervision of undergraduate students undertaking their final year research projects on topics of relevance to this project, e.g., using the data collected to test new data modelling techniques. Further student engagement will be achieved by integrating the concepts and experimental results obtained from this work in the undergraduate curriculum, particularly in the stage 4 Bridge Engineering module which the PI coordinates. This will provide the students with the opportunity to become familiar with current trends in bridge SHM research, enhancing their appreciation of the subject and ultimately, their employability.