Periodic Reporting for period 1 - RAMOBRIS (Risk Assessment and MOnitoring for BRIdges under Scour hazard)
Okres sprawozdawczy: 2022-02-07 do 2024-02-06
Bridge scour, the erosion of sediment around bridge foundations due to river flow, is a leading cause of bridge failures globally. Climate change is worsening this issue by increasing flood frequency and severity. Flood-related scour damage can lead to economic losses, casualties, community isolation, longer travel times, and high repair costs. Current risk evaluation methods, such as visual inspections and simplified formulas, often miss critical scour depths and ignore the long-term effects of scour. Additionally, monitoring data is not effectively incorporated into risk assessments and decision-making.
The RAMOBRIS project seeks to improve resilience against bridge scour hazards by developing an advanced and multidisciplinary approach for monitoring and risk evaluation. Specifically, the project aims to review monitoring techniques for scour and identify critical bridges for case studies, establish a low-cost real-time scour risk monitoring system, develop a probabilistic framework integrating monitoring and forecasting data using a Bayesian approach, and create numerical strategies to assess bridge vulnerability and future scour risks, considering hydraulic loads and climate change. The project contributes to advancing the field of bridge scour monitoring and risk assessment, with significant implications for infrastructure resilience, decision-making, and flood risk management. These efforts also establish a solid foundation for ongoing research and practical applications in bridge scour management, with the potential to enhance infrastructure resilience and flood risk management on a broader scale.
The RAMOBRIS project seeks to improve resilience against bridge scour hazards by developing an advanced and multidisciplinary approach for monitoring and risk evaluation. Specifically, the project aims to review monitoring techniques for scour and identify critical bridges for case studies, establish a low-cost real-time scour risk monitoring system, develop a probabilistic framework integrating monitoring and forecasting data using a Bayesian approach, and create numerical strategies to assess bridge vulnerability and future scour risks, considering hydraulic loads and climate change. The project contributes to advancing the field of bridge scour monitoring and risk assessment, with significant implications for infrastructure resilience, decision-making, and flood risk management. These efforts also establish a solid foundation for ongoing research and practical applications in bridge scour management, with the potential to enhance infrastructure resilience and flood risk management on a broader scale.
To address the identified research needs and achieve the set objectives, the following actions have been taken. The project initially focused on exploring and evaluating various scour monitoring technologies. A critical part of this effort involved comparing different sensors, assessing their effectiveness, and identifying new strategies for both direct and indirect monitoring of scour risks. Based on this review, two case studies were developed at key bridges in Scotland (Figure 1). These case studies provided real-world settings for testing the selected monitoring technologies. The sensors deployed at these sites are part of an ongoing effort to collect data, which has been incorporated into an open-source dataset. This dataset continues to be updated, allowing for long-term monitoring of scour risks at these sites.
Furthermore, one of the case studies was numerically modelled as part of a Secondment activity, which enabled a deeper understanding of scour processes and their impacts during extreme events such as floods. The data collected from these sensors was integrated with advanced scour forecasting models to provide real-time estimations of local scour (Figure 2). This integration allowed for the creation of a dynamic tool capable of continuously updating scour risk estimates based on monitored flow properties. The use of advanced forecasting models ensures that the predictions take into account the variability in hydraulic conditions and their effect on scour, thereby improving the accuracy and reliability of the predictions.
The next step in the research was the development of a Bayesian Network model (Figure 3), which was designed to incorporate data from the monitoring tools and integrate forecasting results. This framework enables a more robust risk assessment by accounting for uncertainty and incorporating real-time observations, which are often overlooked in traditional methods. Additionally, it enables sensitivity analysis on various parameters, guiding the identification of optimal monitoring strategies. This capability ensures the efficient use of available resources and technology by pinpointing the most influential factors in scour risk and focusing efforts where they are most needed. Moreover, the Bayesian Network was employed to generate expert-judgment-based fragility curves for various types of bridge foundations and considering different hydraulic loads. These curves are essential for understanding the vulnerability of bridges, helping bridge operators and managers understand the potential impacts of scour and better adjust their strategies for flood events. Finally, the Bayesian Network functions as a powerful Decision Support System, providing a flexible and adaptive approach to scour risk management. It empowers decision-makers to make informed predictions based on a combination of historical data, current observations, and predictive models, allowing for more effective planning for flood events. Ultimately, this methodology assists policymakers in shaping environmental risk policies and making well-informed decisions during flood emergencies.
The study results have been presented at various international conferences, and several papers are currently in preparation or under submission for publication in journals. Regular meetings with stakeholders, including the bridge operator company, ensured that the risk assessment outcomes were effectively communicated, leading to improved flood risk management strategies. Participation in outreach activities, such as the Explorathon event, and engagement with local communities further contributed to raising awareness and fostering a better understanding of scour risks and flood management
Furthermore, one of the case studies was numerically modelled as part of a Secondment activity, which enabled a deeper understanding of scour processes and their impacts during extreme events such as floods. The data collected from these sensors was integrated with advanced scour forecasting models to provide real-time estimations of local scour (Figure 2). This integration allowed for the creation of a dynamic tool capable of continuously updating scour risk estimates based on monitored flow properties. The use of advanced forecasting models ensures that the predictions take into account the variability in hydraulic conditions and their effect on scour, thereby improving the accuracy and reliability of the predictions.
The next step in the research was the development of a Bayesian Network model (Figure 3), which was designed to incorporate data from the monitoring tools and integrate forecasting results. This framework enables a more robust risk assessment by accounting for uncertainty and incorporating real-time observations, which are often overlooked in traditional methods. Additionally, it enables sensitivity analysis on various parameters, guiding the identification of optimal monitoring strategies. This capability ensures the efficient use of available resources and technology by pinpointing the most influential factors in scour risk and focusing efforts where they are most needed. Moreover, the Bayesian Network was employed to generate expert-judgment-based fragility curves for various types of bridge foundations and considering different hydraulic loads. These curves are essential for understanding the vulnerability of bridges, helping bridge operators and managers understand the potential impacts of scour and better adjust their strategies for flood events. Finally, the Bayesian Network functions as a powerful Decision Support System, providing a flexible and adaptive approach to scour risk management. It empowers decision-makers to make informed predictions based on a combination of historical data, current observations, and predictive models, allowing for more effective planning for flood events. Ultimately, this methodology assists policymakers in shaping environmental risk policies and making well-informed decisions during flood emergencies.
The study results have been presented at various international conferences, and several papers are currently in preparation or under submission for publication in journals. Regular meetings with stakeholders, including the bridge operator company, ensured that the risk assessment outcomes were effectively communicated, leading to improved flood risk management strategies. Participation in outreach activities, such as the Explorathon event, and engagement with local communities further contributed to raising awareness and fostering a better understanding of scour risks and flood management
The project significantly advances bridge scour research by enhancing monitoring methods, improving risk assessment frameworks, and fostering innovation. It integrates cost-effective remote sensing technologies with advanced temporal scour modelling, improving scour risk monitoring. Data from two pilot cases provides valuable real-world insights for validating models. The project also developed practical tools, such as a tool for real-time scour estimations and a probabilistic framework for the risk assessment that accounts for uncertainty and field observations. The project outcomes, in addition to overcoming the limitations of current practices, also set the stage for future research in related areas like hydraulic loads and debris accumulation.
The project has also significant social and economic implications. It provides advanced and probabilistic tools for local authorities, civil protection organizations, and policymakers to enhance flood risk management, implement sustainable monitoring, make informed decisions during flood emergencies and reduce flood-related mortality. Additionally, the project lays the groundwork for new low-cost sensors combining automated image velocimetry and temporal scour models, which could lead to sensor prototypes and commercialization. By improving risk mitigation and emergency response planning through real-time data, the project supports better resource allocation, benefiting industries, transport operators, and bridge management agencies.
RAMOBRIS aligns with Sustainable Development Goal 13 and the Adaptation to Climate Change Mission of Horizon Europe, aiming to reach the anticipated objectives by 2030 by enhancing infrastructure resilience and supporting climate change adaptation efforts. The project’s educational and communication activities further promote awareness of scour risks, ensuring its findings benefit a broad audience.
The project has also significant social and economic implications. It provides advanced and probabilistic tools for local authorities, civil protection organizations, and policymakers to enhance flood risk management, implement sustainable monitoring, make informed decisions during flood emergencies and reduce flood-related mortality. Additionally, the project lays the groundwork for new low-cost sensors combining automated image velocimetry and temporal scour models, which could lead to sensor prototypes and commercialization. By improving risk mitigation and emergency response planning through real-time data, the project supports better resource allocation, benefiting industries, transport operators, and bridge management agencies.
RAMOBRIS aligns with Sustainable Development Goal 13 and the Adaptation to Climate Change Mission of Horizon Europe, aiming to reach the anticipated objectives by 2030 by enhancing infrastructure resilience and supporting climate change adaptation efforts. The project’s educational and communication activities further promote awareness of scour risks, ensuring its findings benefit a broad audience.