Data-driven decision support for in-service and post-hazard infrastructure management

针对在役和灾后基础设施管理的数据驱动决策支持

• 批准号: RGPIN-2016-06405
• 负责人: Goulet, JamesAlexandre
• 金额: $ 1.75万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636135
• 关键词: Data; driven; decision; support; service

Around the world, public infrastructure maintenance has been underfunded for years. Canada is no exception, the aging of existing infrastructure has lead to an alarming number of bridges that are in critical condition. In addition, cities located on the west coast of Canada and along the Saint-Lawrence Basin face the threat of earthquakes which could have catastrophic impacts on our already vulnerable infrastructure. Public infrastructures are essential components of economic development. Yet, society cannot afford at short and medium term, to mitigate the risks of ageing infrastructure and hazard vulnerability by strengthening or replacing all deficient structures; we face the challenge of stretching even further the life of the deficient infrastructure. A path to mitigating these challenges is data-driven technologies for supporting management decisions regarding the condition and safety of structures during their service life and following extreme natural hazards such as earthquakes. Sensing technologies are now inexpensive and are commercially available for measuring structural behaviours such as displacement, strain, tilt, acceleration, and environmental effects. Comparatively, our capacity to interpret data is still in its infancy. Factors such as the complexity of the interactions between a structure and its operating environment, the errors caused by the operation in harsh conditions, and the sheer volume of data acquired are all sources of false alarms. Our capacity to autonomously filter out false alarms currently limits our capacity at detecting anomalies.

在世界各地，公共基础设施的维护多年来一直资金不足。加拿大也不例外，现有基础设施的老化导致了数量惊人的桥梁处于危急状态。此外，位于加拿大西海岸和沿着圣劳伦斯盆地的城市面临地震的威胁，这可能对我们已经脆弱的基础设施造成灾难性影响。公共基础设施是经济发展的重要组成部分。然而，社会无法在短期和中期内通过加强或更换所有有缺陷的结构来减轻基础设施老化和易受灾害影响的风险;我们面临的挑战是进一步延长有缺陷的基础设施的寿命。缓解这些挑战的一个途径是数据驱动技术，用于支持有关结构在使用寿命期间和地震等极端自然灾害发生后的状况和安全的管理决策。传感技术现在价格低廉，可用于测量结构行为，如位移、应变、倾斜、加速度和环境影响。相比之下，我们解释数据的能力仍处于起步阶段。诸如结构与其操作环境之间的相互作用的复杂性、在恶劣条件下操作所引起的错误以及所获取的数据的绝对量等因素都是错误警报的来源。目前，我们自动过滤错误警报的能力限制了我们检测异常的能力。