Asset Management

Asset management planning is a sustained systematic process of designing, operating and maintaining assets/facilities effectively. It streamlines decision-making through the life cycle of an asset to provide the best value to system users and an optimum budget performance. Asset management is tied to a “best value” and “service delivery” model which encompasses the following paradigms:

  • Service delivery: addressing the social, environmental, and economic needs of stakeholders;
  • Life cycle analysis: assessing the operating and maintenance requirements, and the implications of eventual replacement or retirement of assets;
  • Integrated approach: coordinating service delivery, across all assets and all governmental agencies by looking beyond stewardship of individual assets and examining the collective performance of the total asset base during decision making; and
  • Accountability for asset investments: requiring and providing greater transparency and quality in accounting and reporting practices.

People

The members of this research group are:

  • Prof. Tamer E. El-Diraby, Principal Investigator 

  • Prof. Hesham Osman, Collaborator 
  • Madeh Piryonesi, PhD. student

  • Amirreza Mahpour, PhD. student

Collaborators 

The infrastructure asset management research program at UofT has been conducted with extensive outreach and collaboration from governments and industry, including the following entities:

  • Heavy Construction Association of Toronto (HCAT)

  • Toronto and Area Raod Builders Association (TARBA)

  • Greater Toronto Sewer and Watermain Contractor Association (GTSWCA)

  • Residential and Civil Construction Alliance of Ontario (RCCAO)

  • Ontario Good Roads Association (OGRA)

Research Agenda 

Asset management is the science and practice of managing infrastructure systems to achieve the highest levels of services for our communities, with the most optimal life cycle cost. This requires substantial effort in monitoring performance along with the development/use of advanced models to predict deterioration levels and assess the quality of services. The key to success in realizing these goals is to integrate asset management into capital budgeting and planning processes. Public officials at all levels of government need evidence-based tools to study policy options—including performance measuring and funding schemes. However, asset management is not limited to the engineering and financial aspects. New socio-political paradigms–including environmental stewardship, energy conservation, community engagement, and economic development– add to the complexity of decision making. The use of informatics systems offers promising opportunities to collate, and re-use our collective knowledge to optimize decision making. Beyond the use of information technology tools, informatics exploits semantic systems and social network analysis along with advanced algorithms (such as Big Data models) to extract and package new knowledge to support more reliable decision-making process.

The research program for infrastructure asset management at UofT spans the following areas.

  • Support capacity building: one key need to support advancing asset management practices in Ontario is to provide assistance to municipalities for capacity building. This includes programs for staff training; benchmarks for competency analysis and efficiency evaluation of asset management programs; and collection, distribution as well as implementation support of best practices in change management (how to re-engineer decision-making practices to actualize asset management plans).
  • Collaboration: establish incentives and programs to encourage the pooling of resources. For example, for smaller municipalities, encourage collaboration in acquiring and sharing highly qualified staff with complementary expertise.
  • Link life cycle analysis to asset management: asset management is an essential component of any life cycle costing. It is important that project scoping, delivery systems and budgets embed asset management expertise and investment considerations.
  • Climate change and resilience: increasing the resilience of assets and enhancing levels of services are at the core of any climate change action. All stakeholders should synchronize asset management practices with any climate action plans.
  • Advancing levels of service analysis: work on expanding the depth and scope of the definition of levels of services, especially in the softer issues such as customer satisfaction. This is part of a larger need to engage communities in asset management planning.
  • Data and data science: there is limited shared protocols for data modeling, collection specifications, and quality assessment. Advancing asset management practices in the evolving knowledge economy cannot be achieved without significant change and investments in the use of data science. Leadership and the development of pragmatic tools in this regard can help motivate stakeholders, pool resources and coordinate actions.
  • Funding clarity and stability: as asset management practices advance, the needed investments are becoming clearer. Clarity about funding sources, formulae and commitments is an essential part of any effective and sustained asset management policy.

Asset Management Informatics & Data Analytics  

One of the key challenges to the success of the Ontario AM strategy is the management of data. There is inconsistency in data specifications and, more importantly, limited quality assessment guidelines. In many cases, in the analysis of AM systems, especially for condition assessment, the reliability of analysis is highly impacted by the accuracy of the data provided. An equally important, but hard to appreciate, aspect is data modeling and interoperability. The government should encourage (and maybe later enforce) the adoption of a consistent, not necessarily unified, data model of AM parameters. Such data model will include consistent (or common) definitions and measuring practices for AM data, allowing for easier and formal comparison of asset conditions across municipalities. Such model is essential for the interoperability of AM data with data from other domains, which can allow easier linking of AM data to that of GIS, budgeting and finance software. Interoperability with energy and life-cycle costing data models is needed if we want to embed AM in any integrated project decision making systems.

It is important to note that this does not mean that one model will be applicable to all municipalities. Larger municipalities have different situations from those of smaller ones. MOI approach for setting the guidelines for LOS has successfully showcased how to build such definitions bottom-up with careful consideration to variances in needs and conditions. However, as the practice of data collection and management in Ontario advances, it should be expected that the definition of AM parameter and measures (including LOS) will have to be further developed to be more sophisticated and formal.

Research work in this thrust focuses on using advanced tools and Big data analysis to uncover patterns of deterioration in infrastructure; develop algorithms to estimate life cycle costing and predict/understand reasons for its escalation; study means to link levels of services to quality of work processes; understanding community needs and identifying matching criteria to guide capital budgeting and finance decisions; etc. This includes the following topics:

    1. Development of new tools and Big Data analytics to uncover patterns of deterioration in infrastructure
    2. Optimization of life cycle analysis algorithms to predict reasons for failure
    3. Studying the link between effectiveness/efficiency of service to quality of work processes
    4. Understanding of community needs
    5. Identification of matching criteria to guide capital budgeting and finance decisions

Climate Change and Regenerative sustainability

The next opportunity for AM in Ontario is to link it to climate change strategies and programs. There are significant overlaps between the two domains. For example, variations in precipitation volumes and patterns, as well as changes in storm severity, can have a major impact on assets. In one possibility, increasing temperatures may have a positive impact on some roads given the expected decrease in winter salting. In the same vein, linking asset management to life-cycle analysis strategies is a major opportunity and need. Life-cycle energy management is directly related to rehabilitation decisions and the condition of assets. In fact, AM should be an integral part of any life-cycle costing exercise—because the majority of energy and costs for any facility are associated with the operation stage. 

Smart Cities 

The use of informatics systems offers new and promising opportunities to collate and customize relevant knowledge to support evidence-based decision making. The aim is not limited to managing assets optimally, but supporting the realization of smart city. By weaving civil infrastructure into the fabric of both its society and this data-enabled initiative, we will transform roads, bridges, and tunnels (among others) into a vibrant part of the knowledge economy. This data-driven approach leverages Canada’s position as a leader in computational data sciences while empowering its infrastructure sector to apply those skills in an efficient manner.

 

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