Math for mass infrastructure: effective asset life-cycle management

Adding up the numbers in effective asset life-cycle management

Mathematical optimization has long been used in forestry to determine appropriate harvesting and planting, relative to the life spans of trees. The New Brunswick Department of Transportation is using similar methods to plan the long-term treatment of roads and bridges.

Anyone working in the transportation or civil infrastructure industries is most likely quite familiar with the management practice commonly known as “fix the worst first.” It’s a practice necessitated by aging infrastructure, insufficient funding and complex, competing priorities. With life-cycles that can stretch hundreds of years, these factors make informed decision-making and effective resource allocation daunting challenges.

Consider the complexities of monitoring deterioration: of roads, bridges, signs, sewers, etc. Then factor in their increasing use due to expanding populations and heavier volumes of traffic. Consider the boom-bust cycles that result in periods of significant expansion followed by periods of maintenance. Bear in mind adjacency issues: the proximity of pavements, culverts, sewers, curbs, sidewalks, pipes and signs to one another.

To put all of this into perspective, New Brunswick’s Department of Transportation (NBT), for example, maintains approximately 3,000 bridges across the province: some old, some new, all built to last a long time, and consequently with long treatment cycles. Which ones do you treat, how, and how often? Add to these considerations 18,000km of highways, 18 ferries, and 240,000 driveways and basins. The mathematical calculations become truly mind-boggling.

In 2002, NBT began looking for an alternative to this ongoing operational dilemma, one that would take into account the complexities of the province’s transportation network, and that would systematically deliver optimal treatment plans for all assets at the least cost. In conducting their research, officials looked at other transportation agencies, studying the successes and challenges of an array of asset management initiatives.

What they found was that while these engineering-centric transportation organizations were very good at making detailed operational judgments, they had yet to produce a model enabling optimal decision support at the strategic level.

A shift in culture

NBT recognized that a revolutionary asset management system would not come about without considerable change management and, in turn, a business framework within which to support that cultural shift. Drawing from best practices both in and outside the transportation industry, the team approached the system’s development with the following principles in mind:

  • The 80-20 rule: Eighty per cent of decisions can be made with 20 per cent of the data.
  • Performance-based measures: Monitor the long-term performance of assets using strategic, quantifiable measures and targets.
  • Least life-cycle cost: Evaluate treatment alternatives, enabling the network of assets to function at the lowest cost over a predetermined period.
  • Asset-centric data gathering: Establish sources of information related to design material, inspection, treatment and cost.
  • Continuous improvement: Incorporate mechanisms to accommodate updates to deterioration models and asset inventory.
  • Evolutionary implementation: Implement and evolve the system using prototype technology and pilot districts.

Acknowledging that these principles would require significantly higher levels of management efficiency, the project team devised a multi-step system for achieving it. Called the Infrastructure Management Maturity Model (IM3), the system measures organizational capability against industry best practices.

The objective is to institutionalize a culture of continuous improvement and ultimately lead to legislated least overall life-cycle costing. IM3 defines the procedures required to quantify performance, then uses this information to improve management decisions.

Looking beyond transportation to planning and practices employed in other industries, the project team took special note of the methods used by forestry experts, long recognized as leaders in sustainability management.

Of particular interest was the spatial-planning software used to build long-term models for managing forest lands. These models take into account complex factors such as habitat, biodiversity and watershed management.

Studying this software, the team gained insight into the intricacies of model building, asset dynamics, establishing objectives, and factoring in constraints. They learned what has long been known in the forestry industry: With multiple factors to consider in allocating resources, the only way to achieve true optimization is through mathematics.

They began to explore the differences between two main modes of planning: simulation and optimization. They quickly realized that simulation methods – using information systems, knowledge and experience to automate the planning process – were simply automating worst-first prioritization of asset treatment.

While simulation models serve a valuable purpose in operational planning, they are not sufficient for strategic-level planning that must take into account multiple treatment options and tradeoffs across various types of assets.

Optimization, by contrast, can enable holistic viewing of the entire planning horizon and consider what might be best for the asset network’s greater long-term sustainability.

Applying techniques initially developed to model forest lands, NBT implemented a software-based system that considers transportation factors such as deterioration curves and budget constraints, and is able to optimally manage the life-cycles of civil infrastructure assets.

Because it is designed to account for long-term sustainability, rather than a single point in time, it is especially adept at dealing with age-class distribution, a significant hurdle in the strategic planning of civil infrastructure. Age-class distribution is a period-in-time snapshot of various assets at different ages.

Periods of significant asset expansion or fiscal restraint are challenging to manage as they represent significant fluctuations in future preservation and maintenance funding. Their effective management, however, can make the difference between an increased infrastructure deficit and having no deficit at all.

Optimized planning addresses this age-distribution challenge by generating possible alternatives and automatically conducting trade-offs among asset groups.

Overall, best-case scenarios ideally distribute funding in a manner that eases the severity of the peaks and valleys. Planners are therefore able to determine stable funding measures.

For NBT, such mapping promises a transformation of operations, one that will enable department officials to put in place feasible one-year and four-year action plans, which in turn will contribute to a sustainable, strategic 40-year plan that New Brunswick can build on.

Kim Mathisen is assistant director (GIS), information management and technology, with New Brunswick Transportation; Mark Gallagher is senior business consultant, civil infrastructure; and Michelle Dunphy is consultant and business analyst at Xwave Inc.

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