1. Define and build/acquire generic components 2. Assemble components/build decision
support tools
System Development Process
Existing “Single-Asset” Systems
System Data System Data System Data System Data
Component-Based Asset Management System
Example Generic Components
Example Generic Components Example Decision Support ToolsExample Decision Support Tools
Modeling GIS B/C Analysis Report Generators Cost, Benefit, Impact Estimates Project Prioritization Project Packaging Program Tradeoffs
Asset Management Data Available to all Components and Tools
NCHRP Project 20-24(11)
Task 2 – Asset Management Framework
As in Model #5, the intent is to replace existing single-asset management systems. How- ever, the approach in Figure 4.14 focuses on creating building blocks, which can then be used as-is, or combined to construct more complex tools. The advantage of the compo- nent approach is that it can provide the flexibility to develop tools that meet the specific needs of different organizational units, and to adapt tools over time as business processes change. However, it is more expensive to develop a generic capability than one that needs to work in only one particular context. Incorporation of off-the-shelf components can counter-balance this added cost.
For example, a complete review of all asset management functions within the agency might indicate a set of requisite functions and analyses that are common to several asset classes and modes: e.g., updates of data on condition and performance, scenario testing under constrained budgets, economic analysis of optimal asset management strategies, and so forth. (These are examples of selected functions only – they do not represent an exhaustive asset management process.) In Model #5, these functions and analyses would be developed within systems modules, which would then be used to analyze different types of infrastructure assets: e.g., pavements, bridges, transitways, signs, ITS devices, rail, aviation facilities, and so forth. Stated another way, current asset management sys- tems such as PMS, BMS, PTMS, and other systems that manage individual types of infra- structure and features would no longer exist. All asset management functions would be handled by the generalized components. This approach clearly requires planning and a good understanding of the agency’s asset management process.
This option, like Model #5, is geared towards replacing most if not all of existing system functionality. Therefore, it would use either the centralized database or the distributed database approach.
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n 4.5 Use of Management Systems for Scenario Testing
Transportation agencies may already possess several capabilities that can be used at little additional cost to assist both asset management and GASB 34 reporting. This section pro- vides an example of such a capability, scenario testing, to discuss how existing manage- ment system capabilities can be applied to investigate the cost and implications of different asset management strategies. Systems that potentially could include a scenario testing capability are PMS, BMS, those MMS with levels of service defined, public transit asset management systems, and possibly capital programming systems and other systems for specific infrastructure features. The characteristic of interest in these systems is their capability to analyze needed expenditures as a function of target condition levels, network condition constraints, or in the case of maintenance management, levels of service. This scenario testing capability builds upon the set of engineering relationships and mathe- matical decision rules that are designed into the management system.
NCHRP Project 20-24(11) Task 2 – Asset Management Framework
Figure 4.15 illustrates an example comprising a set of three scenarios that have been ana- lyzed for an example network of 500 bridges using the Pontis® 4.0 bridge management
system.6 Each scenario tests a particular budget level to preserve the bridge network
through a 10-year analysis period. Figure 4.15 plots the condition of the bridge network versus time in years. The network-average bridge condition is gauged by the percent of bridges with Health Index (HI, a measure of bridge structural condition, as described in Ref. (6)) greater than 75 on a scale from zero (poor) to 100 (excellent). Other measures of condition, such as sufficiency rating, can also be used. The budget levels correspond to the following projected annual expenditures:
• A relatively high annual expenditure, which results in improvement of bridge network condition through the 10-year period. This case is illustrated by the top curve in Figure 4.15.
• A moderate annual expenditure, which is sufficient to maintain the status quo in net- work bridge condition through the analysis period. This case is illustrated by the mid- dle curve in Figure 4.15.
• No annual expenditure, representing a “do-nothing” policy, which results in a decline of network bridge condition through the analysis period. This case is illustrated by the bottom curve in Figure 4.15.
The three scenarios each result in a markedly different result at the end of the 10-year analysis period, and together define an envelope delimiting a range of options in funding bridge preservation. It is possible to plot the condition level at the end of 10 years, as indicated in Figure 4.16, versus the corresponding annual budget or expenditure level. The result is the relationship between condition level and needed expenditure as shown in Figure 4.17.
Figure 4.17 captures the tradeoff between constant expenditure level and resulting long- term condition. This relationship can be used directly as a guide identifying the expen- diture level to meet a specified target condition level. It can also be used to explore long- term trends in network or subsystem condition for different possible funding scenarios, and to discuss these with policy-makers in a proactive way. While this example focuses on bridges, other types of management systems also employ a scenario-testing or similar capability. Collectively, these management systems can address different networks and subsystems of a transportation system using the modified approach. Moreover, these analyses have significant benefit for asset management generally. For example, the curve in Figure 4.17 can be used for a several purposes, including program budget recommen- dations, impact analyses of changes in funding levels, and tradeoff analyses with other programs.
6 Pontis 4.0 is an AASHTOWare product. The network of bridges is assumed to exhibit a uniform
distribution of ages and conditions, and exhibit roughly a linear deterioration in network-level condition for reasons discussed in Appendix B.
NCHRP Project 20-24(11)
Task 2 – Asset Management Framework