This paper explores the idea of using a camera and image analysis to extract traffic data in congested situations. The camera system measures vehicle positions and hence car/truck mix and inter-vehicle gaps, all key data for long-span bridge loading. It also measures vehicle lengths and approximates weights from the lengths, an approach shown to give good accuracy in statistical studies of this type. A high-resolution camera, installed on FRB in 2017, captured image data at 1 second intervals over a five-month period. Standard image processing approaches are applied to extract the lengths of the vehicles from the images. Calibration factors are used to convert the lengths in the images from pixels to metres and to correct for perspective. Freeflowing traffic captured by the camera is compared with the same traffic recorded by the on-site WIM system and confirms the accuracy of the calibration process. A year of data from the WIM system is used to establish correlations between vehicle length and weight and hence to provide estimates of weights for vehicles identified in the images. The approach is used to gather bridge traffic load information including car/truck mix and inter-vehicle gaps. Bridge load effect data are fitted to Extreme Value statistical distributions to calculate characteristic maximum values. Results are shown to be sensitive to the frequency of traffic jams on the bridge.

Generally, bridge principal inspection data fed into Bridge Management Systems is used to prioritise bridge maintenance activities and to prepare associated budgets. However, worldwide, high bridge maintenance costs and inadequate allocation of funds are causing significant backlogs in maintenance activities. Technological solutions that lower manpower requirements, reduce misdiagnosis and lower the cost of bridge maintenance regimes are therefore required to ensure that new and ageing bridges are adequately maintained. A desk review of the state of the art in bridge inspection and bridge management systems used in the United Kingdom, United States of America, Japan, Denmark, Ireland and Southern Africa was undertaken. The bridge inspection regimes reviewed continue to follow the 1976 OECD recommendations. Visual inspections remain the predominant method for carrying out bridge inspections. However, visual inspections suffer from problems of inspector subjectivity, visual acuity and observer fatigue, and technological solutions aimed at improving the reliability of visual bridge inspections have not become routine. This paper recommends that technological solutions that automate certain aspects of the visual principal inspection process, such as using optical and thermal cameras mounted on unmanned aerial vehicles (UAVs), can be used to improve the reliability of visual inspection data and support bridge inspectors in the arduous task of detecting and recording bridge defects.

In recent years there has been a rapid deterioration in the condition of bridges in the UK. In one year 2017-2018 the cost of bridge maintenance backlog increased by 34% to 6.7bn pound. Climate change and increasing traffic have undoubtedly contributed to this rapid growth. However, consultation with bridge owners has confirmed outdated management and maintenance methods are also attributing to the deterioration of our road networks. This research looks specifically at the development of a new bridge management system (BMS) using the Northern Ireland (NI) road network as a research platform. The proposed BMS will adopt a multi-objective decision making at the object and network level, incorporating bridge performance goals influenced by technical, environmental, economic and social factors. This paper will outline the initial investigations carried out to develop the data strategy for the development of the BMS and present some preliminary analysis of the data in the current BMS.

Bridge weigh-in-motion (WIM) uses existing bridges to find the weights of vehicles that pass overhead. Contactless bridge weigh-in-motion (cBWIM) uses bridges to weigh vehicles without the need for any sensors to be attached to the bridge. A camera is mounted on the back of a telescope, which magnifies the image to the extent that submillimeter bridge deflections can be measured accurately. A second camera is used to monitor traffic and to determine axle spacings. The two cameras are synchronized using light-emitting diodes (LEDs) activated by an interval timer. The exact position of the test vehicle relative to the bridge influence line is determined by optimization at a postprocessing stage. The new WIM concept was tested on a bridge in the United Kingdom. In a modest test sample of eight statically weighed vehicles, cBWIM was shown to be a feasible alternative to other forms of WIM. Accuracy of gross weight is already reasonably good; accuracy of groups and individual axles will require greater magnification or additional cameras.