Data Mining Tools to Improve Asset Management for Buried Water and Sewer Pipes

由于时间的推移和预算的挑战，加拿大各地的水管平均每天断裂约100根，老化的下水道正在以越来越快的速度失去其输送完整性。 虽然维修、修复和更换基础设施的组成部分是可行的，但确定最佳行动和优先考虑供水和下水道管道的投资战略极具挑战性。 这个问题的重要性是显而易见的，因为仅多伦多市就积压了超过10亿美元的供水和下水道管道需求。 由于供水和下水道管道的故障率受到众多外部和内部因素的影响，因此在确定投资优先次序方面存在挑战。数据挖掘工具将用于改善资产管理，通过加强条件评估，恶化建模，生命周期成本计算和预测工具。 该研究将评估这些数据挖掘工具的能力，以表征水和下水道管道的恶化率，并表征过去20年来用于修复基础设施的补救技术的性能。 此外，该研究将通过评估污水泄漏/出口的程度来提高我们对污水渗出率的理解，并有助于确保我们有工具来评估人类和周围环境的暴露风险。 这项研究将培养市政工程（条件评估，生命周期成本，生命周期预测，恶化建模等）的HQP学生。拟议的研究将提供直接资助，以协助培训四名博士，四名硕士生和三名合作学生。 麦克比恩博士的目标包括在这一关键领域的研究、管理和有效沟通技巧方面仔细指导研究生。

Due to the passing of time and budgetary challenges, water pipes are breaking at an averate rate of about 100 per day across Canada, and aging sewers are losing their conveyance integrity at increasing rates. While repair, rehabilitation and replacement of components of the infrastructure is feasible, identifying the best actions and prioritizing investment strategies for water and sewer pipes is extremely challenging. The importance of the issue is evident because the City of Toronto alone has a backlog of needs in excess of more than a billion dollars for water and sewer pipes. The challenges in prioritizating investments arise since the rates of failure of water and sewer pipes are influenced by numerous external and internal factors. Data mining tools will be used to improve asset management, through enhanced condition evaluation, deterioration modeling, lifecycle costing and prediction tools. The research will assess the capabilities of these data mining tools to characterize rates of deterioration of water and sewer pipes, and characterize the performance of remediation technologies which have been used to repair the infrastructure within the last 20 years. As well, the research will improve our understanding of sewage exfiltration rates by assessing magnitudes of sewage leakage/egress and help to ensure we have tools to assess exposure risks to humans and the surrounding environment. This research will train HQP students in municipal engineering (condition evaluation, lifecycle costing, lifecycle prediction, deterioration modeling, etc.). The proposed research will provide direct funding to assist in the training of four PhD, four Master's students and three co-op students. Dr. McBean's goals include the careful mentoring of grad students in research, management and effective communication skills in this critical area.