Improved Infrastructure Assessments of Water Main Breaks Using Data Mining and Machine Learning Algorithms

使用数据挖掘和机器学习算法改进水管破裂的基础设施评估

• 批准号: RGPIN-2018-04623
• 负责人: McBean, Edward
• 金额: $ 2.62万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=659145
• 关键词: Improved; Infrastructure; Assessments; Water; Main

Maintaining the integrity of buried water infrastructure is at the forefront of priority issues for municipalities across Canada. However, substantial portions of our buried water infrastructure are in need of repair/ rehabilitation, and possible replacement, all of which require significant expenditures. Increased vulnerability to imposed stresses as a result of climate change and urban densification, and avoiding compromised water quality, are examples of challenges complicating the prioritization of investments.*** In response, this research proposal is focused on developing guidance procedures to most effectively direct such expenditures. The merits of dramatically improved data mining and machine learning algorithms, and including the potential to improve the utilization of information now available from evolving smart data acquisition procedures becoming available, will be explored in this research.*** Research insights will include the merits of prioritization to include factors such as health risks, external factors of adjacency of high-cost impacts should pipe failure occur, climate change influencing water demand patterns, urban intensification and the concomitant increased demands that will arise, all of which contribute to the merits of a performance-based management approach for failure prediction assessments.*** Data mining models will be used to improve discovery of patterns, and machine language models will be employed to improve prediction efforts, all with the intent to improve pipe break prediction modeling and to provide better prioritization approaches for types of pipe rehabilitation. The utility of smart infrastructure technology will also be explored to determine the potential to decrease water distribution issues of pipe breaks and maintenance issues.*** This research will train HQP students in municipal engineering water infrastructure (condition evaluation, lifecycle costing, lifecycle prediction, deterioration modeling, machine learning, and data mining approaches). The proposed research will provide direct funding to assist in the training of three PhD and four masters students,one U/G, and a post-doc. The goal is to include the mentoring of grad students in research management and effective communication skills in this critical area of Canadian needs.

保持地下水基础设施的完整性是加拿大各城市优先考虑的问题。 然而，我们的地下水基础设施的很大一部分需要维修/恢复，并可能更换，所有这些都需要大量的支出。由于气候变化和城市密集化，更容易受到压力的影响，以及避免水质受损，这些都是使投资优先次序复杂化的挑战的例子。 对此，本研究建议的重点是制定指导程序，以最有效地指导此类支出。 本研究将探讨大幅改进的数据挖掘和机器学习算法的优点，包括提高对目前可从不断发展的智能数据采集程序中获得的信息的利用率的潜力。 研究见解将包括优先考虑的优点，包括健康风险等因素，如果管道故障发生，高成本影响的外部因素，影响水需求模式的气候变化，城市集约化以及随之而来的需求增加，所有这些都有助于故障预测评估的绩效管理方法的优点。 数据挖掘模型将用于改进模式的发现，机器语言模型将用于改进预测工作，所有这些都旨在改进管道破裂预测建模，并为管道修复类型提供更好的优先级排序方法。 还将探索智能基础设施技术的效用，以确定减少管道破裂和维护问题的水分配问题的潜力。 这项研究将培养HQP学生在市政工程水基础设施（条件评估，生命周期成本，生命周期预测，退化建模，机器学习和数据挖掘方法）。 拟议的研究将提供直接资助，以协助培训三名博士和四名硕士生，一名U/G和一名博士后。 其目标是包括格拉德生的指导研究管理和有效的沟通技巧，在加拿大的需求这一关键领域。