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.

维持埋藏水基础设施的完整性是加拿大市政当局的优先问题的最前沿。 但是，我们埋藏的水基础设施的大部分需要维修/康复，并可能更换，所有这些都需要大量支出。由于气候变化和城市致密性以及避免水质损害，施加压力的脆弱性越来越大，这是挑战使投资优先考虑变得复杂的挑战。 在这项研究中将探索大大改进的数据挖掘和机器学习算法的优点，包括改善现在可用的智能数据获取程序可用的信息的利用的潜力。随之而来的增加的需求将会出现，所有这些需求都有助于基于绩效的管理方法进行故障预测评估的优点。***数据挖掘模型将用于改善模式的发现，并将采用机器语言模型来改善预测工作，所有这些模型都旨在改善管道损失预测模型，并提供更好的优先置于管道式置换式置于管道式的方法。 还将探索智能基础设施技术的实用性，以确定减少管道断裂和维护问题水分配问题的潜力。 拟议的研究将提供直接的资金，以协助培训三名博士学位和四名硕士学生，一名U/G和一名DOC。 目的是在加拿大需求的关键领域中包括研究研究生的指导和有效的沟通技巧。