IMPROVING DECISION MAKING FOR INFRASTRUCTURE MANAGEMENT

管理基础设施的挑战与在不断变化的气候、相关的不确定性和日益增长的预算限制下维持其性能水平的成本不断增加有关。例如，MTQ在其网络上管理着5500多个主要结构，另外还有4300个来自市政网络的结构。这些结构的年度维护预算超过6.4亿美元，预计随着基础设施的老化，预算还会增加。拟议的研究计划是对先前发现基金研究主题的演变，重点是通过减少与基础设施组件（如桥梁）的现状和剩余寿命评估相关的不确定性，以及通过网络分析改进维护、维修和更换组件的调度决策分析，提高基础设施资产管理的有效性和效率。

Challenges in managing our infrastructure are associated with the growing costs in maintaining its level of performance in a changing climate, the associated uncertainties and growing budget constraints. For example, the MTQ manages over 5500 major structures on its network with an additional 4300 structures from the municipal network. The annual budget for the maintenance of these structures is over 640M$ and is predicted to increase with ageing of the infrastructure. The proposed research program is an evolution of the subjects studied in the previous Discovery Grant and is focused on improving the effectiveness and efficiency of infrastructure asset management by reducing uncertainties related to the evaluation of the current condition and residual life of infrastructure components (e.g. bridges) and improving decision analysis for the scheduling of maintenance, repair and replacement of components through network analysis. The industry is increasingly relying on structural health monitoring, non-destructive techniques, and numerical models to reduce uncertainties on the current and future condition of structural components and to supplement information obtained from visual inspections. The ability to detect structure deterioration at earlier stages can facilitate implementation of appropriate remedial measures and prevent this damage from becoming critical. The research will focus on evaluating the potential of the impulse response (IR) test and vibration analysis for the condition assessment of concrete plates and to develop guidelines as a function of the type, location and severity of defects for field investigations and quality control. The ability to accurately predict the residual life of a component given its current condition is the next important element in preventing damage to reach critical levels and avoid the spiralling high repair costs that we are observing. Current Bridge Management Systems rely mainly on historical records of condition states derived from visuals inspections to predict the residual life of components using Markov Chains models. This approach does not favor preventive maintenance since visual signs of deterioration occur only once it has reached critical levels and cannot be used for planning future interventions in the context of climate change. An approach based on physical and numerical models of deterioration is proposed for predicting condition states for different climate change scenarios. Finally, recent experience with major repairs in Montreal highlight the lack of planning and coordination of major rehabilitation projects and their negative impact on traffic and the economy in general. The last element of the research proposal is to address the issue of planning interventions in space and time over a network by considering the uncertainties on the current state of bridges, their residual life, climate change scenarios, financial constraints and impacts on users.