Section 2.5 NEW SECTION
Moving Beyond Pavements and Bridges to Other Assets
Most of the available funding, and management effort, allocated by transportation agencies for preserving infrastructure is spent on highways and bridges. While these assets constitute the predominant contribution to the overall value of their infrastructure portfolio, other assets in the road corridor can be significant as well (with consequential investment and liabilities). Agency personnel are responsible for maintaining various ancillary assets, such as signs and signals, guardrails, culverts, lighting, pavement markings, sidewalks, and retaining walls. As agencies increasingly adopt TAM principles, they find more effective ways to manage all their assets through data-driven decisions. However, with often limited information about these ancillary assets, transitioning to data-driven management has been challenging. Nonetheless, some agencies have made progress in using data to manage certain ancillary assets. Including ancillary assets in a TAMP helps highlight maintenance funding needs and provides a more comprehensive view of the total cost of delivering mobility at the service standards set by the agency.
There is growing interest in developing comprehensive inventories for ancillary assets and establishing procedures for regular performance evaluation. Due to limited resources, it is often impractical to collect inventory and condition data for all assets simultaneously. Therefore, a systematic method is needed to identify the appropriate management approach and complementary data collection processes to support decision-making for these asset classes.
Moving Beyond Pavements and Bridges to Other Assets
Most of the available funding, and management effort, allocated by transportation agencies for preserving infrastructure is spent on highways and bridges. While these assets constitute the predominant contribution to the overall value of their infrastructure portfolio, other assets in the road corridor can be significant as well (with consequential investment and liabilities). Agency personnel are responsible for maintaining various ancillary assets, such as signs and signals, guardrails, culverts, lighting, pavement markings, sidewalks, and retaining walls. As agencies increasingly adopt TAM principles, they find more effective ways to manage all their assets through data-driven decisions. However, with often limited information about these ancillary assets, transitioning to data-driven management has been challenging. Nonetheless, some agencies have made progress in using data to manage certain ancillary assets. Including ancillary assets in a TAMP helps highlight maintenance funding needs and provides a more comprehensive view of the total cost of delivering mobility at the service standards set by the agency.
There is growing interest in developing comprehensive inventories for ancillary assets and establishing procedures for regular performance evaluation. Due to limited resources, it is often impractical to collect inventory and condition data for all assets simultaneously. Therefore, a systematic method is needed to identify the appropriate management approach and complementary data collection processes to support decision-making for these asset classes.
2.5.1
Drivers for Including Ancillary Assets
This subsection highlights various drivers prompting agencies to collect additional ancillary asset data, such as performance-based investment decisions, technological advancements, and the integration of information across business units. It emphasizes that these drivers necessitate a strategic assessment considering risks, organizational changes, and existing practices when expanding data collection efforts for ancillary assets.
TAM Webinar #43 - Geotechnical Assets and TAM
A number of factors might prompt an agency to modify its requirements, priorities, or procedures, which necessitate the collection of additional ancillary asset data. For instance, an agency might choose to:
- Make performance-based investment decisions for assets beyond pavements and bridges.
- Invest in new data collection technology enabling a more cost-effective alternative to past methods.
- Integrate and share information across business units, leading to a review of future data needs across multiple asset classes.
- Better understand the full cost of mobility including construction, maintenance, preservation, rehabilitation, and replacement of ancillary assets.
- Adopt risk-based management strategies to improve resiliency and avoid damage due to recurring events such as flooding, slope failures, safety incidents, and liability risk.
- Implement a more efficient process to address funding requests for expanded data collection efforts from various business units within the agency.
These, or other types of changes within an agency, typically prompt an evaluation of data needs to support decision-making for ancillary assets. Most agencies acknowledge the benefits of having comprehensive and accurate inventory and condition data for their assets. However, maintaining this information requires available resources, along with existing business processes and software tools to support analysis and reporting.
As shown in Figure 2.10, this strategic assessment of ancillary asset data needs is typically influenced by the risks the agency aims to manage, organizational or technical changes enabling new processes, or discrepancies between current and desired management practices. In its strategic review and decisions on expanding data collection efforts, the agency should consider its policies, budgets, resources, performance, legislated mandates, and available technology.
Figure 2.10 Strategic Review of Risks, Changes, and Gaps
2.5.2
Selecting Assets for Inclusion in Asset Management Programs
This subsection discusses the challenges agencies face in expanding asset management programs due to limited resources and budget constraints. It introduces a handbook that outlines a seven-step process for prioritizing asset classes, providing examples of typical highway ancillary asset classes and illustrating how agencies like Nevada and Ohio establish priority tiers for efficient data collection and management aligned with their management priorities.
Organizations face constraints on resources and budgets. Therefore, those seeking to broaden their asset management programs by incorporating additional information require a method for assessing the benefits and costs of system development, data collection, and data management. The Handbook for Integrating Ancillary Assets into TAM Programs (FHWA 2019) presents structured approaches, outputs, and essential guidelines concerning the steps needed to formulate a prioritized strategy for integrating asset classes. Prioritization is crucial to ensure the effective and efficient use of limited resources. The Handbook outlines a seven-step procedural framework fostering collaboration among business units with asset data requirements or responsibilities in data collection and management. Determining which asset classes to include in an organization's asset management program hinges on a thorough grasp of the organization's IT policies, architecture, and strategies.
Figure 2.11 Prioritization Overview
Typical asset classes considered though this process can be extensive. The assets listed in Table 2.2 are an illustration of potential classes that may be considered by agencies, and was compiled in the Handbook (FHWA 2019), adapted from NHI 2017.
Table 2.2 - Typical Highway Ancillary Asset Classes
| FUNCTIONAL AREA | ASSET CLASS | ||||||
|---|---|---|---|---|---|---|---|
| Structures (not Bridges or otherwise in the National Bridge Inventory) |
| ||||||
| Traffic Control and Management—Active Devices |
| ||||||
| Traffic Control and Management—Passive Control Devices |
| ||||||
| Drainage Systems and Environmental Mitigation Features |
| ||||||
| Other Safety Features |
| ||||||
| Roadside Features |
| ||||||
| Other Facilities and Other Items |
|
*See NCHRP Research Report 903: Geotechnical Asset Management for Transportation Agencies (FHWA 2019) for more details on best practice for managing geotechnical assets.
Many agencies institute priority tiers to group asset classes, enabling the development of distinct plans for gathering, storing, and managing necessary data in accordance with management objectives. Determining which assets to include in a specific implementation project depends on a wide range of interconnected factors, rendering the development of a precise ranked order for all assets challenging. Streamlining this process can be achieved through the adoption of a tiered system which groups assets of comparable priority together. As exemplified in Table 2.3, both Nevada and Ohio compiled ranked lists that can be translated into prioritized tiers. Each agency possesses distinct priorities and can utilize a similar methodical approach to assess their portfolio within the framework of their operational and strategic planning capabilities, as well as their management objectives.
Table 2.3 - Examples of prioritized tiers developed by Nevada and Ohio DOTs
Adapted from 2019 FHWA Handbook on Ancillary Asset Management
| TIER LEVEL | NEVADA DOT | OHIO DOT |
|---|---|---|
| I |
|
|
| II |
|
|
| III |
|
|
2.5.3
Data Collection
This subsection discusses the importance of data collection in asset management programs, emphasizing the need for coordination between different agency groups. It introduces a performance-based management strategy using the RCM approach, detailing condition-based, interval-based, and reactive maintenance components relevant to life cycle planning of ancillary highway assets. The document also provides a table summarizing maintenance approaches for various asset classes, supporting agencies in efficiently collecting high-quality data for informed decision-making.
After evaluating the priority of asset classes, asset stewards need to understand the intended functions, potential failure possibilities, available maintenance options, and the consequences of failure for each asset class. This information is typically dispersed across various areas or business units within an agency. Gathering this information necessitates coordination among different groups within the agency. The ideal approach involves following a framework to establish a performance-based management strategy for ancillary assets, identifying the optimal data elements for collection, and selecting the most suitable data collection techniques for each asset class.
Figure 2.12 Reliability Centered Maintenance
The Handbook (FHWA-HIF-19-006) offers a potential framework of interconnected processes that can be tailored to an agency's specific requirements. The process for developing a performance-based management strategy employs the Reliability-Centered Maintenance (RCM) approach, which is elaborated upon in Chapter 4. RCM utilizes a series of risk-based questions to assist agencies in identifying the most effective and efficient management strategies. The three primary components of an RCM program pertinent to the life-cycle planning of ancillary highway assets are condition-based maintenance, interval-based maintenance, and reactive maintenance. This is illustrated in Figure 2.12, as adapted in the Handbook from NASA (2008. Reliability-Centered Maintenance Guide for Facilities and Collateral Equipment. National Aeronautics and Space Administration, Washington, DC). A decision tree can be used to establish an appropriate management and maintenance approach for each ancillary asset class to inform data requirements. This is also illustrated in Figure 4.5 of Chapter 4.
Figure 2.13 RCM Decision Tree
- Condition-based maintenance includes predictive maintenance and real-time monitoring. Inspections note current capital and maintenance interventions as well as current state to be considered by maintenance teams and inputted into predictive models.
- Interval-based maintenance is conducted independently of the asset's condition and involves performing inspections or replacements at predetermined intervals.
- Reactive maintenance assumes that failure is low risk to operations and where there are no practical monitoring approaches and/or regular deterioration or failure patterns. Repairs are made after the failure. Table 2.4 presents a summary of the applicability of condition, interval, or reactive maintenance for each of the asset classes.
Table 2.4 Typical Maintenance Approaches by Asset Class
| Asset Class Elements | Condition Based | Interval Based | Reactive Based |
|---|---|---|---|
| All Structures (excluding bridges) | Preferred | Not Recommended | Feasible |
| Traffic Control and Management - Active Devices | Feasible | Preferred | Feasible |
| Traffic Control and Management - Passive Devices | Feasible | Feasible | Preferred |
| Drainage systems and environmental mitigation features | Feasible (except preferred for small culverts) | Preferred (except feasible for small culverts) | Feasible |
| Other Safety Features | Feasible | Feasible | Preferred |
| Roadside features | Feasible | Feasible* (not recommended for roadside hazards) | Preferred** |
| Other facilities items | |||
| Rest areas, weigh stations and buildings | Preferred | Feasible | Feasible |
| Parking Lots, Roadside litter and fleet | Feasible | Preferred | Feasible |
| Graffiti | Feasible | Feasible | Preferred |
* - Preferred for landscaping, access ramps, and bike paths
** - Feasible for landscaping, access ramps, and bike baths
2.5.4
Data Required for Decision-Making
This subsection emphasizes the significance of data collection for effective decision-making in asset management programs, particularly using a Reliability Centered Maintenance (RCM) approach. It outlines essential data requirements for different maintenance types, such as condition-based, interval-based, and reactive-based maintenance, and highlights the importance of complete and reliable data. Additionally, the document discusses desirable data that can enhance decision-making by providing clarity, supporting different agency departments, generating accurate work orders, managing asset risks, and tracking the asset's full life cycle.
Data-driven decisions depend on asset data to guide effective investment choices. Effective data collection practices allow an agency to execute strategic RCM maintenance and streamline work order distribution. With accurate data, metrics can be derived, and performance measures compared to assess the effectiveness of a maintenance strategy and identify areas for improvement. Ancillary asset data can be categorized as either essential or desirable, depending on the management approach. Regardless of the data type, it is crucial that the data be complete and reliable. Generally, the RCM process required the following essential data for effective management.
Table 2.5 – Essential Data by Maintenance Approaches for Ancillary Assets
| Maintenance Type | Asset Type | Asset Location | Asset Unique ID | Condition Data |
|---|---|---|---|---|
| Interval-based maintenance | X | X | X | - |
| Condition-based maintenance | X | X | X | X |
| Reactive-based maintenance | X | X | X | - |
There are various strategies and technologies that support data acquisition that can be found in Chapter 7 and in the Handbook for Ancillary assets (FHWA-HIF-19-006). Other desirable data can augment decision-making by:
- Providing additional clarity and accuracy to the essential data collected.
- Supporting different departments within an agency.
- Assisting in generating accurate work orders.
- Helping manage asset risks.
- Tracking the asset’s full life cycle to make informed decisions.
The above list is not all-inclusive but provides clear examples of reasons why additional data collected in the field could be beneficial to an agency. Each of these items is described in more detail in the handbook. Other desirable data attributes have been referenced elsewhere (HMEP 2013), and may also be considered, including:
- Maintenance intervals.
- Frequency of failure.
- Allocated risk factors.
- Maintenance requirements.
- Engineering specific data
2.5.5
Managing Ancillary Asset Data
This subsection underscores the importance of integrating ancillary asset data into an agency's overall data management system for informed decision-making on management, maintenance, and capital investment. It outlines guiding principles for effective data management practices, including interdepartmental coordination, an authoritative hub with integrated databases and web services, a common data dictionary, and business improvements in querying, analyzing, displaying, and reporting data.
Integrating ancillary asset data into an agency’s overall data management system ensures that decision-making associated with management, maintenance and capital investment is based on the best available information across the organization. An agency will, at the same time, improve transparency and public trust. The following guiding principles distinguish good data management practices from less comprehensive approaches to data management:
- Strategic Plan—Interdepartmental coordination.
- Authoritative Hub—Integrated database and web services.
- Common Data Dictionary—Agency agreement on assets and attributes.
- Business Improvements—Query, analyze, display, and report data.
Data management concepts are discussed in more detail in Chapter 7.
Yukon Department of Highways and Public Works
The Yukon Department of Transportation and Public Works (TPW) is committed to taking a consistent, strategic approach to asset planning and management; to deliver services matching their customers’ expectations, while maximizing value for money. Vegetation management is a key part of TPW’s roadside safety program. It improves highway safety and helps preserve their infrastructure by:
- Improving visibility and vehicle sight lines.
- Reducing wildlife collisions.
- Establishing a clear zone.
- Facilitating roadside drainage.
- Preserving roadside surfaces.
- Controlling invasive weeds.
- Enhancing the overall driving experience.
The TPW roadside vegetation management program was established in the early 2000’s to address the challenges of maintaining right-of-way growth throughout Yukon. Over the past few years, the program was reassessed, leading to several improvements in the inspections and decision-making processes. This included establishing a life cycle model in the agency's dTIMS management system, to project the future condition of roadside vegetation, generate possible treatment strategies (e.g., mowing, brushing) for each section of road, and identify an optimal solution by assessing the life cycle costs and benefits from each treatment strategy for each road section. This included steps to define and compile the model inputs, including roadway inventory, vegetation condition ratings, deterioration curves, treatment options, treatment decision logic and financial parameters. The dTIMS roadside vegetation life cycle model was used to develop an optimized long-term investment plan that assessed the impacts of alternative budget scenarios and/or constraints. It also provided an example of how the software could be used by TPW staff to later model decision making for other asset types.
Yukon Department of Highways and Public Works
The Yukon Department of Transportation and Public Works (TPW) is committed to taking a consistent, strategic approach to asset planning and management; to deliver services matching their customers’ expectations, while maximizing value for money. Vegetation management is a key part of TPW’s roadside safety program. It improves highway safety and helps preserve their infrastructure by:
- Improving visibility and vehicle sight lines.
- Reducing wildlife collisions.
- Establishing a clear zone.
- Facilitating roadside drainage.
- Preserving roadside surfaces.
- Controlling invasive weeds.
- Enhancing the overall driving experience.
The TPW roadside vegetation management program was established in the early 2000’s to address the challenges of maintaining right-of-way growth throughout Yukon. Over the past few years, the program was reassessed, leading to several improvements in the inspections and decision-making processes. This included establishing a life cycle model in the agency's dTIMS management system, to project the future condition of roadside vegetation, generate possible treatment strategies (e.g., mowing, brushing) for each section of road, and identify an optimal solution by assessing the life cycle costs and benefits from each treatment strategy for each road section. This included steps to define and compile the model inputs, including roadway inventory, vegetation condition ratings, deterioration curves, treatment options, treatment decision logic and financial parameters. The dTIMS roadside vegetation life cycle model was used to develop an optimized long-term investment plan that assessed the impacts of alternative budget scenarios and/or constraints. It also provided an example of how the software could be used by TPW staff to later model decision making for other asset types.