Early warning decision support system for the management of underwater scour risk for road and railway bridges

PARTNERS- Transport Scotland (TS), Network Rail (NR), SEPA, Arup.
CHALLENGE- Flood induced scour is by far the leading cause of bridge failures, resulting in loss of lives, traffic disruption and significant economic losses. TS is responsible for 1,567 bridges, 8% of these currently classified as needing scour protection measures and costing £2m per annum in routine inspections. Similarly, NR Scotland Route includes 1,750 structures susceptible to scour requiring an annual inspection spend of £0.4m. Inspections are expensive and time consuming, and often the information collected is qualitative and subjective.
OBJECTIVE- To develop a prototype of a Decision Support System (DSS) for scour risk management for NR's and TS's bridges, which enhances users' safety, while minimizing traffic disruptions and transport agencies' operational cost. The system will estimate, and continuously update, the present and future scour failure risk using (i) real-time information from monitoring of scour depth (ii) rainfall forecast and (iii) river flow characteristics.
INNOVATION- Sensor and communication technologies offer the possibility to assess in real time the scour depth at critical bridge locations; yet monitoring an entire infrastructure network is not economically sustainable. A way to overcome this limitation is to install monitoring systems on a limited number of critical locations and use a probabilistic approach to extend this information to the entire asset. In the occurrence of a flood, monitoring observations are used to give the real-time best estimate of bridge failure probability. The sensing technology proposed is a prototype of smart probe with integrated electromagnetic sensors designed to detect changes in the dielectric permittivity of the surrounding bridge foundation, designed and calibrated to detect scour and sediment deposition in various soil types.
KEY ACTIVITIES
- Collect data from past scour inspection on NR's and TS's inspection records; define formal vulnerability model based on the fragility curve method.
- Define flood hazard models based on SEPA rainfall and river flow datasets, and accounting for CCRA climate change prediction.
- Model correlation in hazard and vulnerability among different bridges, using Bayesian networks.
- Collect data from TS pilot scour monitoring system.
- Implement algorithms for updating fragility curves and state variables of the Bayesian network based on real-time information from monitoring system.
- Develop a prototype DSS that continuously updates bridge failure risk using real-time information from pilot monitoring system and rainfall forecast.
- Produce an exploitation plan.
DELIVERABLES- The BRIDGE VULNERABILITY MODEL, the FLOOD HAZARD MODEL and the SCOUR HAZARD MODEL will provide NR and TS with quantitative up-to-date information on the probability of scour failure of each bridge. Before a flood event, this will allow prioritising inspections according to risk and, when necessary, to introduce precautionary bridge closures or traffic management. This framework can be equally used to simulate the impact of potential extreme weather events on travel agencies' network, and to plan in advance appropriate emergency procedures and countermeasures. The TS PILOT SCOUR MONITORING SYSTEM will provide validation to a new technology for direct assessment of underwater scour, which can complement traditional NR's and TS's river-bank visual assessments. The DECISION SUPPORT SYSTEM will provide information for long term prediction of future scour risk to drive strategic maintenance, repair and rehabilitation actions. The same DSS can be used as an early warning system that automatically takes action when the risk estimated exceeds a given threshold. The EXPLOITATION PLAN will set a roadmap to transfer the new technology and knowledge to other infrastructure systems and geographical areas.
DURATION- 12 months
COST- £94,763.02

The objective of the project is to develop a prototype of a Decision Support System (DSS) for scour risk management for road and railways bridges, which enhances the safety of users, while minimizing traffic disruptions and operational costs. The system will estimate, and continuously update, the present and future failure risk due to scour using: (i) real-time information from monitoring of scour depth, (ii) rainfall forecasts and (iii) river flow characteristics.

The anticipated benefits of the system are:

1. Direct assessment of the risk of scour bridge failure. The system will provide NR and TS with quantitative up-to-date information on the probability of scour failure of each bridge. Before a flood event, this will give an opportunity to prioritise inspections according to risk and, when necessary, to introduce precautionary bridge closures or traffic management. Similarly, immediately after an event, this will help to identify the critical bridges requiring river-bank or underwater inspections.

2. Support to strategic MR&R. Information for strategic long-term prediction of future scour risk to drive strategic maintenance, repair and rehabilitation (MR&R) actions. Particularly, the information from the DSS will complement the Scour Risk Rating assessed through the Level 2 process as in Procedure EX2502 or BD97/12, providing a formal quantitative risk evaluation, rather than a mere classification. This will facilitate risk ranking and strategic action prioritisation on the bridge stock.

3. Scenario planning for extreme events. The proposed risk analysis framework can be equally used to simulate the impact of potential extreme weather events on transport agencies' road networks, and to plan in advance appropriate emergency procedures and countermeasures.

4. Early warning. As risk assessment is carried out in real-time, based on the measurements from on-line monitoring systems, the same DSS can be used as an early warning system that automatically takes action when the estimated risk exceeds a given threshold. For example, the system could, in principle, control a traffic light which automatically restricts access to the bridge when the prediction of scour level is estimated to be critical for the safety of the users.

5. General method. Although the proposed method is developed for scour risk, its scope is much broader, and can be equally applied to any engineering problem where different data sources are integrated to optimize the operation of infrastructure.