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

公路铁路桥梁水下冲刷风险管理预警决策支持系统

• 批准号: NE/R009090/1
• 负责人: Daniele Zonta
• 金额: $ 12.3万
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FR009090%2F1
• 关键词: Early; warning; decision; support; system

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 monthsCOST- £94,763.02

合作伙伴-苏格兰交通局(TS)、网络铁路(NR)、SEPA、Arup.CHALLENGE-洪水引发的冲刷是目前为止桥梁故障的主要原因，导致生命损失、交通中断和重大经济损失。TS负责1,567座桥梁，其中8%目前被归类为需要采取冲刷保护措施，在例行检查中每年花费200万GB。同样，NR苏格兰路线包括1750个易受冲刷的建筑物，需要每年40万GB的检查费用。目的-开发一个用于NR和TS桥梁冲刷风险管理的决策支持系统(DSS)的原型，以提高用户的安全性，同时将交通中断和运输机构的运营成本降至最低。该系统将使用(I)来自冲刷深度监测的实时信息(Ii)降雨预报和(Iii)河流水流特性来估计并持续更新当前和未来的冲刷失败风险。克服这一限制的一种方法是在有限数量的关键位置安装监控系统，并使用概率方法将此信息扩展到整个资产。在洪水发生时，利用监测观测值对桥梁失效概率进行实时最佳估计。提出的传感技术是一个带有集成电磁传感器的智能探头的原型，旨在检测周围桥梁基础的介电常数的变化，设计和校准以检测各种土壤类型的冲刷和泥沙沉积。KEY活动-收集关于NR和TS的检查记录的过去冲刷检查的数据；基于脆弱性曲线法定义正式的易损性模型。-基于SEPA降雨和河流流量数据集定义洪水风险模型，并考虑CCRA气候变化预测。-使用贝叶斯网络建立不同桥梁之间的危险和易损性模型关联。-从TS Pilot冲刷监测系统收集数据。-根据来自监测系统的实时信息实施更新贝叶斯网络易损性曲线和状态变量的算法。-开发原型DSS，使用来自Pilot监测系统和降雨预报的实时信息持续更新桥梁失效风险。-制定开发计划。DELIVERABLES-桥梁脆弱性模型，洪水风险模型和冲刷风险模型将为NR和TS提供有关每座桥梁冲刷失效概率的最新定量信息。在发生水浸事件之前，这将允许根据风险确定检查的优先顺序，并在必要时实施预防性关闭桥梁或交通管理。该框架同样可以用于模拟潜在极端天气事件对旅行社网络的影响，并提前计划适当的应急程序和对策。TS试验冲刷监测系统将为直接评估水下冲刷的新技术提供验证，该技术可以补充传统的NR和TS的河岸目测评估。决策支持系统将为未来冲刷风险的长期预测提供信息，以推动战略性维护、修复和恢复行动。同一决策支持系统可用作预警系统，在估计的风险超过给定阈值时自动采取行动。开发计划将制定路线图，将新技术和知识转移到其他基础设施系统和地理区域。为期12个月COST-GB 94,763.02

项目成果

A Decision Support System for scour management of road and railway bridges based on Bayesian Networks

基于贝叶斯网络的公路铁路桥梁冲刷管理决策支持系统

• 发表时间: 2019
• 作者: Maroni A

Using Bayesian networks for the assessment of underwater scour for road and railway bridges

• DOI: 10.1177/1475921720956579
• 发表时间: 2018-05
• 期刊: Structural Health Monitoring
• 作者: Andrea Maroni;E. Tubaldi;D. Val;H. McDonald;D. Zonta

A Bayesian network approach to assess underwater scour around bridge foundations

用于评估桥梁基础周围水下冲刷的贝叶斯网络方法

• 发表时间: 2018
• 作者: Maroni A

Managing bridge scour risk using Structural Health Monitoring

使用结构健康监测管理桥梁冲刷风险

• 发表时间: 2019
• 作者: Maroni A

SHM-based Decision Support System for bridge scour management

基于SHM的桥梁冲刷管理决策支持系统

• 发表时间: 2019
• 作者: Maroni A