All transportation infrastructure assets have a life cycle, which includes several stages from initial construction to removal or replacement (see figure 4.1). Life cycle management is an investment approach that considers maintenance, renewal, replacement, or repair options through an asset’s service life with the intent to maximize the benefit provided by the asset at the minimum practicable cost. It employs data on asset condition, treatment options, costs, deterioration rates, replacement cycles, and other factors to evaluate trade-offs between possible investment strategies and treatment timings. Effective life cycle management requires knowledge of the agency’s strategic priorities and an understanding of the performance criteria driving investment decisions, so the right management strategy can be identified and implemented for each asset class. Aligning asset management measures with agency priorities ensures the investments made to extend asset service life provide the maximum impact to the agency’s long-term goals.

Figure 4.1 illustrates a variety of interventions that occur over an asset life cycle. The larger circles represented in the figure are service life altering, and represent a capital investment in infrastructure. Capital investments provide significant life extension, and may alter or enhance the operational nature of the asset, e.g. expand capacity, without fully replacing the asset. Maintenance (reactive, interval based and routine) activities are required throughout the life cycle to ensure the asset achieves its service life.Preservation treatments restore condition or performance to achieve service life, and may extend service life as well, but do not significantly alter the operational nature of the asset. Some agencies may capitalize investment in these preservation activities; however, regardless of the timing and character of the selected interventions, all of them are part of the life cycle management process. More (lower cost) maintenance interventions can offset the number and cost of the larger (and more costly) interventions. Balancing the right intervention, at the right time, can greatly reduce the overall investment needed for infrastructure to be reliably available for providing service.

Life cycle management can be used at both network level and at project level. At network-level, life cycle management considers the needs of an entire asset class, as well as the available funding, to determine the most appropriate life-cycle strategies. For example, analysis can establish the optimal proportions of overall investment that should be allocated to different types of interventions over the network, to minimize investment to achieve performance targets or an average condition level. At a project level, life cycle management is commonly used to develop asset-specific strategies. Project level life cycle plans provide input into the network level life cycle plans. Large bridges or other distinct network components are often planned and managed in this manner.

Life Cycle Cost Analysis (LCCA) is an engineering-economics approach that can be used to quantify the differential costs of alternative design approaches. Network level life cycle management, while a more wholistic process that manages every stage of an asset’s life, may employ LCCA or other forms of analysis to inform management decision-making. Figure 4.2 highlights some of the major differences between life cycle management and life cycle cost analysis. At the network level, LCCA can be used to understand how to best manage the network as it ages. At a project level, it is used to understand what are the most effective actions to be taken on the assets within the project scope at the time of project delivery. Both network level and project level analyses contain many aspects of engineering economic analysis, such as consideration of user benefits, user costs, and the time-value of money to identify alternatives that represent the lowest practicable life cycle cost over the analysis period to achieve the desired objectives.

Decision Making Context

Life cycle management is driven by the need for owners to provide consistent service to those that use the transportation system with the resources available. Infrastructure decision making can take place at several levels within an organization, and in each case, considers different but often interrelated factors. These are illustrated in table 4.1.

Table 4.1 - TAM Decision-Making Contexts
Key Questions and Connections to Other Chapters

Establishing Desired Levels of Service

Before a whole-life strategy can be developed and implemented, an agency must determine what they seek to achieve. In many transportation agencies, the desired level of service (or asset management organizational objectives, in ISO 55000 terminology) provides the linkage between what the goals of an agency are, and what investments and interventions should take priority when managing assets. High level goals should directly influence investment choices when resource allocation decisions are made. Service levels help establish when gaps need closing to achieve a goal, and merits investment. Chapter 2 discusses ways to create linkages between goals and investment decision making.

When managing the life cycle of existing assets, performance targets are commonly established as a way to manage service levels for the transportation network. How to determine the expected level of performance may vary depending on the type of asset being managed. Level of service targets that are part of performance framework typically are a mixture of both customer focused performance measures, and technical service measures that help those responsible for the asset assess what types of interventions might be required and when. Customer focused service measures are important to road users and other stakeholders that require mobility. Travel time reliability, safety, load capacity and clearances, and lane availability are all examples of service targets that are customer focused. Condition, strength, regulatory compliance and examples of technical service attributes are commonly of greater interest to asset stewards than asset users. Both types are service level targets that are important to evaluate the efficacy, effectiveness and efficiency of a transportation system.

For pavements and bridges, and other assets managed using a condition-based approach, asset condition is commonly used to establish expected technical levels of performance, but also is relevant to customers. For example, condition is employed as a proxy in this way for pavements because it is objectively measurable, deterioration has some predictability. It is a valuable service attribute because often, user experience is also directly connected to condition as well. Potholes, rutting and roughness all reduce quality of service from a pavement. Performance measures, such as those discussed in Chapter 6, are used to establish the desired long-term performance and to set short-term targets that can be used to track progress towards the long-term objectives. For other highway assets, including those managed using interval- or time-based maintenance approaches, performance may be linked to the expected service life, the ability of the asset to fulfill its intended function, and/or other operational factors. For these other highway assets, performance targets are often established as part of a Maintenance Quality Assurance (MQA) program in terms of desired maintenance levels of service (MLOS) and integrated with operational service targets that may also be customer focused.

Risk can also be used as a measure of performance. As described in chapter 2, risk considers both the potential impact and consequence of failure. This can be particularly useful when the potential consequences of failure impact other assets or facilities. An example of how Colorado uses risk to manage rockfalls is included in section 4.3 of this chapter. Additional details on how to track risk-based performance measures is included in Chapter 6.

Establishing a desired level of performance is typically a collaborative process that considers existing conditions, available funding, expected demands on the system, policy goals and guidance, and stakeholder priorities. The desired level of performance is typically established once baseline data is available, so performance trends can be evaluated. The desired level of performance may be adjusted over time to reflect changes in agency performance, changes in asset condition, capacity, safety, resiliency and other factors.

Three types of service expectations are often used in combination to manage asset performance:

• Performance target – the level of performance beyond which additional performance gains are not desired or worth the additional cost. When performance is measured based on condition, the desired performance may describe the desired state of good repair. There may be an expected specific time frame to achieve this desires performance target.
• Current Performance – an intermediate level of performance achieved by the organization and is usually reported relative to the desired target. Target setting is described in more detail in Chapter 5.
• Minimum acceptable performance – the lowest level of performance allowed for the asset or asset class to still function as designed.

Performance expectations may be set for the road network, a road corridor, for individual assets or for a group of assets. Commonly, performance expectations are set using a combination of asset class or subclass or sub network, such as:

• Key network corridors.
• Bridges on the National Highway System.
• Interstate pavements.
• Culverts larger than 10 feet in diameter.
• Traffic signals serving more than 10,000 vehicles per day.

The nature of performance expectations can be either strategic or tactical or operational. Strategic expectations support freight movement; for example, the long-term goal of providing unrestricted flow of legal loads is supported by a performance expectation of no load-posted or restricted bridges on interstate highways. This expectation cannot be accomplished without the tactical delivery of work to address factors contributing to the physical condition of bridges. Thus, an agency may include tactical expectations to perform maintenance and repair on structural members on a routine basis or as conditions warrant. These enhancements can be also integrated with renewal and other rehabilitation interventions to help improve both tactical performance metrics, as well as achieve higher level goals and objectives. Operational improvements such as more responsive snow clearance, and better signage are all integrated treatment options to achieve the strategic objective.

Life cycle management analysis, and the decisions it supports, require service levels, performance targets and other objectives to be able to determine the optimal choices for agencies to select during resource allocation. Over an asset life cycle, a range of interventions are possible, from reactive, routine and preventative maintenance, to large investment associated with renewal, replacement, or removal. Having targets helps select the right interventions and investment option while balancing risk, service and cost.

Connecting performance measures to higher level strategic goals also supports an agency’s ability to communicate how technical measures relate to system performance as experienced by highway users and other external stakeholders, thus tying asset management outcomes to system performance. Asset management measures are often very technical. Performance indicators like bridge ratings, pavement distress measurements, and risk ratings are not commonly understood by those outside transportation agencies. However, agencies can use these technical measures to support the performance indicators that are more commonly understood and prioritized by system users and external stakeholders. Communicating system performance and the status of the road network is discussed in Chapter 2, and is illustrated in several examples below. Customer service level targets are often established for this purpose, and give users an ability to understand the quality of service they should expect on the transportation system.

Using Computerized Management Systems to Optimize Life Cycle Management

For condition-based analysis, computerized management systems are valuable tools for evaluating life cycle strategies. Computerized systems support the larger life cycle management process by providing relevant, reliable information and analysis results to decision makers at the right time.

Condition-based management is common for pavement and bridge assets. Often pavement and bridge decision making is supported by a computerized system that is used to support optimized life cycle management. The results from this analysis provide insights into optimal life cycle strategies for all network assets or for a specific group of assets. These models can be configured to include the effects, maintenance, preservation, rehabilitation, and reconstruction actions. Depending on the type of condition-based modeling approach, uncertainty can also be included.

Various life cycle scenarios can be generated by modifying one or more variables in the analysis. By running multiple network-level scenarios and comparing the results, pavement and bridge management systems can identify viable life cycle strategies and help an agency select the strategy that best achieves the stated objectives.

More information on the use of pavement and bridge management systems is available in the FHWA document, Using a Life Cycle Planning Process to Support Asset Management: A Handbook on Putting the Federal Guidance into Practice. Life cycle planning is a required component of risk-based TAMPs developed by state DOTs (23 CFR 515), that uses computerized asset management systems to establish long-term life cycle strategies for pavements, bridges and other highway assets. NCHRP Report 866, Return on Investment in Transportation Asset Management Systems and Practices, provides an assessment of how state DOTs have implemented asset management systems, including practice examples. The end of this section includes a how-to guide for using a pavement management system for life cycle planning, a requirement for risk-based TAMPs developed by state DOT’s for pavements and bridges on the National Highway System (23 CFR 515).

These computerized systems are designed to develop network-level scenarios for analyzing the impacts of different program variables over long periods of time. Typical pavement management scenarios will cover 10 to 40 years, while bridge management scenarios may need to cover 100 years or more to ensure inclusion of multiple life cycles within the scenario.

Various life cycle scenarios can be generated by modifying one or more variables in the analysis. By running multiple network-level scenarios and comparing results, pavement and bridge management systems can identify viable life cycle strategies and help an agency select a strategy that best achieves the stated objectives.

Predicting Asset Performance

A life cycle strategy is enhanced by the availability of models and analysis tools that facilitate the evaluation of different combinations of treatment type and timing across the asset class. For this analysis a model that predicts future asset deterioration and response to treatments is required.

For condition-based approaches to managing assets, historical performance is typically used as a baseline for developing models to predict future performance. The predicted conditions are used to determine the type of treatments that may be needed over an asset’s service life, so the ability to accurately predict asset conditions in the future, with and without treatment, is an essential component of asset management. Models are developed by comparing performance, typically measured as asset condition, over time with actions or treatments performed on specific assets. This means that performance is associated to the last action or treatment that impacted performance in a positive way. However, assets may also receive treatments that delay the onset or advancement of distress. As a result, most models assume assets receive some level of preventive or routine maintenance between more significant treatments. If agency practices change to delay or cease maintenance activities, assets may not perform as models predict.

Several methods can be used to estimate future asset performance, the two most
common of which, deterministic and probabilistic, are described below. Additional information has been published by NCHRP (Report 713, 2012 ): Estimating Life Expectancies of Highway Assets. This report also contains guidance on selecting the most appropriate modeling approach for various highway asset classes.

Deterministic Modeling

Deterministic modeling is a common and relatively simple approach for using historic data to predict future asset performance. Deterministic models apply regression analysis to one or more independent variables, typically condition over time, and develop a “best-fit” equation to determine the rate at which asset conditions change. The independent variables are used to predict a single dependent variable, most commonly represented as the predicted condition at some point in time in asset management applications. Developing deterministic models is relatively easy but relies on quality data collected consistently over several years to produce dependable results. Deterministic models are more easily implemented as they are more readily paired with linear program solving. They also provide consistent outputs. The downside of deterministic models is the limited insight that they provide into the cost uncertainty surrounding a strategy.

Probabilistic Modeling

Unlike deterministic models, which provide a single repeatable outcome, probabilistic models provide a distribution of possible strategies that provides insight into the cost uncertainty of plans. Probabilistic models can also more readily accept uncertainty in other variables, as represented by the shading in Figure 4.7. Given that condition changes are probabilistic, no two strategies that the model will provide are the same. This means that multiple iterations of the model with the same inputs can provide different results. Accordingly, probabilistic models are useful for setting funding limit expectations, while deterministic models help to provide insights into which projects are best to apply to specific assets.

Common approaches to developing probabilistic models are the Markov, Semi-Markov and Weibull models. Markov modeling works well for assets with condition ratings based on regular inspections. There are several ways of establishing a Markov model, but the simplest is to calculate the proportion of assets that change from one condition state to the next in any given year. These proportions are then used to develop what is known as the transition matrix. At the start of the model run, an asset “knows” its condition state. Once this is known there is then a probability it will change from its current condition state to the next in any given year. While these types of Markov approaches have been widely used, they do not necessarily model deterioration effectively, as the rate of change of condition increases with time. To address this, Semi-Markov models are used. Like Markov, Semi-Markov models have a condition transition matrix, but this is also augmented with a time selection matrix. In these models the probability of a condition jump is calculated, then the length of time an asset will remain in that condition state is also selected. Using more advanced mathematical techniques, the Semi-Markov approach can be expressed similarly to the Markov approach, but for Semi-Markov, the transition matrix changes with time. This reflects the increasing likelihood the asset will transition (deteriorate faster as its ages). Such models are typically used on long-lived assets.

A Weibull model offers another approach for modeling asset deterioration. A Weibull distribution predicts the likelihood of asset failure or deterioration as a function of age. Weibull models are particularly useful for addressing assets rated on a pass/fail basis during inspection. The Weibull model provides an additional factor meant to address the increasing or decreasing likelihood of an asset moving from an acceptable to an unacceptable state between inspection cycles. Reliability is the inverse of the probability of failure (i.e. 1 -p(f)). Reliability, like Weibull can thus be used to assess the likelihood an asset will provide the required service. The relationship between time and reliability is assessed by analyzing asset behavior to understand potential modes of failure. This analysis is a core aspect of reliability-centered maintenance, and is more typically used on short lived assets.

Accounting for Uncertainty in Asset Performance

Performance modeling uses historic data to estimate future performance; however, not all future events are predictable nor is past performance necessarily a predictor of future performance. This section considers the how uncertainty can be introduced into the analysis.

The unpredictability of future events introduces uncertainty into prediction models. Additionally, the amount of uncertainty tends to increase with time so their affects are compounded. As outlined in the previous section, probabilistic modeling is one approach that can be used for accounting for uncertainty, but what level of uncertainty is acceptable?

To minimize uncertainty, an agency must first understand the source of the uncertainty. A common type of uncertainty related to asset management is the behavior of the assets themselves. Due to the advancement of technology and knowledge and differences in materials and construction practices, there can be significant differences in performance between otherwise similar assets. The change in behavior can be positive, such as the introduction of epoxy-coated reinforced steel in bridge decks to delay the onset of corrosion from road salt intrusion or the introduction of Superpave and performance graded asphalt binders to reduce pavement cracking and rutting. Other changes in behavior are less easy to predict, such as the impact of salt intrusion on prestressed, post-tensioned concrete box-beam bridges. Other sources of uncertainty include:

• Weather events, e.g. flooding, drought, or freeze-thaw
• Earthquakes
• Climate change
• Traffic accidents
• Data inaccuracies
• Inaccurate models
• Poor assumptions

Uncertainty caused by variability in the data can often be addressed through the development of quality assurance plans that describe the actions an agency has established to ensure data quality, whether the data is collected in-house or by a contractor. Common quality assurance techniques include documented policies and procedures to establish data quality tolerance limits, independent reviews of collected data, and training of data collection crews. Data management strategies are discussed in more detail in Chapter 7.

To evaluate the accuracy of models and assumptions, agencies can include multiple scenarios in their life cycle planning analysis to test the impact of different decisions. This type of sensitivity analysis can be helpful in identifying areas in need of further research or developing contingency plans if the initial assumptions turn out to be inaccurate.

To understand whether time and effort should be invested in minimizing uncertainty, a risk-based approach can be used. Assuming the consequence arising from a defined issue or event remains the same, the cost in terms of data collection of reducing uncertainty can be investigated. As an example, the condition state of an asset, as determined using a visual approach, may not provide the required level of insight, which results in poor or unknowable treatment decisions. To minimize the uncertainty, extra testing can be carried out. The level of testing would be defined by the risk-cost reduction ratio. Similarly, with climate change, how much would have to be invested in studies to understand the effects on asset longevity? Thus, through risk management, an agency determines which risks are tolerable and which must be actively managed through investigations, studies other research. The risks are identified, prioritized, and tracked using a risk register (see Chapter 2). For those risks that should be managed, plans are developed to outline actions that will be taken to mitigate threats or take advantage of opportunities, as discussed in Chapter 6.

Introduction

The majority of asset management costs, for large transportation agencies, are contract costs for preservation, renewal, and construction of assets. However, maintenance investments have a significant impact on asset conditions and should be accounted for in a TAMP. Capturing maintenance costs for inclusion in a TAMP often poses unique challenges, because maintenance work is often delivered by in-house crews or through different contract mechanisms than major capital projects. These costs may also be tracked through different IT systems than capital costs.

When capturing costs for incorporation into a TAMP, it is also important to capture what is purchased, or accomplished, with those investments. Maintenance accomplishments are typically captured in terms of maintenance activities. However, the units of accomplishment for these activities may not easily align with asset management measures. Further the units of accomplishment may vary between work performed by in-house crews and work performed by contract.

In-House Maintenance Costs

Maintenance crews commonly report their efforts and accomplishments on a daily basis through a work reporting system. Most State DOTs currently use some form of maintenance management systems (MMS) to report work performed, along with the resulting accomplishments and costs.

In-house maintenance costs commonly include the cost of labor, equipment, materials, and overhead expenses related to work performed by maintenance field crews. These costs are captured through work order or daily report records, in which costs are associated with one or more actions, commonly called “tasks,” performed by the crew. Each task has an associated task code, which serves as a unique identifier, and a specific unit of measure for reporting accomplishments. For example, the task of “ditch cleaning” may have a unit of accomplishment of “feet,” or the task of “mowing” may have a unit of accomplishment of “acres.”

Contract Costs

Maintenance contract costs, like typical construction contract costs, are captured in terms of contract pay items. These pay items commonly do not align with the maintenance. Moreover, the unit of accomplishment may depend on the type of contract used to procure the work. For example, a contract to clean ditches may have a pay item measured in feet of ditch cleaned, cubic yards of material removed, or hours of work performed.

Maintenance contracts may be funded from an agency's Capital program or Maintenance budget. In many cases maintenance contracts funded through different programs are managed with different systems and have different means of tracking both costs and accomplishments. All of these differences can make tracking maintenance costs more challenging than the costs for other work types.

Aligning Costs from Different Sources

To incorporate maintenance costs into a TAMP, the costs from multiple sources need to be aligned in regard to both their financial components and their units of accomplishment.

The primary source of misalignment between in-house and contract maintenance costs is that in-house costs are captured through individual components of labor, equipment, materials, and overhead, whereas contract costs are captured based on pay items that combine each of these components. The labor costs captured through maintenance management systems (MMS) may or may not include an estimate of overhead costs. To facilitate comparison of in-house labor costs to contract costs an overhead multiplier should be applied. Best practices for ensuring in-house costs are accurate includes interfacing the maintenance management systems with the agency’s financial or enterprise resource planning (ERP) system.

Once the costs are aligned financially, they must be aligned to common units of accomplishment to allow them to be incorporated into asset management analyses. The Maryland DOT State Highway Administration (MDOTSHA) and Texas DOT (TxDOT) offer two examples of how this alignment can be accomplished. These examples are taken from NCHRP Report 1076, Guide for Incorporating Maintenance Costs into a Transportation Asset Management Plan. TxDOT’s approach requires inspectors on maintenance contracts to enter daily accomplishment information into both the Capital Program management system, SiteManager, and the MMS. MDOTSHA has developed a set of Project Cost Activity (PCA) codes to track in-house maintenance expenses. These codes are incorporated into the records of all maintenance contracts to facilitate alignment of costs and accomplishments.

Life Cycle Planning

One benefit of categorizing maintenance activities by their impact on the asset life cycle is that it facilitates incorporation of costs from those activities into Life Cycle Planning (LCP) analysis.

• Preventive Maintenance, Repair, and Unit Replacement costs are all critical inputs to LCP analysis. These costs are sometimes considered under the Preservation work type by State DOTs. Assets are typically eligible for each type of maintenance activity at a specific point in their lifecycles. Once a certain level of deterioration has been reached, a given type of activity ceases to be a cost-effective treatment. To incorporate these maintenance activities into LCP for a given asset class, agencies should consider the following:
• 1. The size of the asset inventory.
  2. The number (or percentage) of assets eligible for the activity type.
  3. The length of time a typical asset is eligible for the activity type.
  4. The available treatments and their unit costs.
  5. How the application of each treatment impacts asset conditions.
  6. The quantity of each treatment to be applied in each year of the analysis.
• Operations, routine maintenance, and Organizational Strengthening costs do not have a direct impact on future conditions, and are generally not included in LCP analysis.

Risk Management

Maintenance crews and contracts are often critical components of risk mitigation strategies. The way these resources are deployed to address risks depends on the type of risk, which can be broadly incorporated into two categories: events and trends.

Risk Events

Maintenance resources represent a transportation agency’s first responders to major and minor disruptive events. Costs for response to risk events can be captured using normal work reporting procedures. Agencies often apply a code to signify if the work being reported is in response to a specific event. In some cases, the agency may be deploying maintenance resources in response to an event that has not yet been identified. In these cases, staff may need to review past records so they can be associated correctly.

Trends and Long-Term Change

Maintenance can be deployed to address long-term trends that impact asset conditions. Other trends may impact an agency’s ability to deploy maintenance. Examples of trends that impact maintenance include the following (Allen, et.al. 2023).

• Changes to regulations, requirements or standards may require a rapid response to replace non-conforming assets, or may impact the cost of future maintenance.
• Changing customer expectations may lead to changes in maintenance performance standards and drive future budget changes.
• Funding fluctuations may provide a boost to or decline in maintenance capabilities. In turn this could lead to changes in asset performance, or vulnerabilities.
• Aging infrastructure can lead to network wide changes in maintenance needs. This can occur cyclically, following large building cycles, such as the interstate construction era from 1960 to 1980.
• Staff turnover due to retirements can create a need for better knowledge management.
• Rapid system expansion can lead to an increased need for future maintenance. Since the need is not immediate, there can be a lag between the need arising and an awareness of that need among executives and legislators who establish maintenance budgets.
• Long-term or gradual environmental changes can impact asset performance by exposing infrastructure to conditions it was not designed to withstand. Maintenance may be required to retrofit or replace poorly performing assets.

Financial Planning

The TAMP Financial Plan should consider all sources of funding used to support maintenance. The Financial Plans should indicate how that available funding is expected to be used over the TAMP time period. This will typically require extrapolation or forecasting beyond established maintenance budgets. The maintenance activity categories can be helpful in determining how to account for this funding within the plan.

• Preventive Maintenance, Repairs, and Unit Replacements are often funded and delivered by multiple programs. As such some of these costs may be captured under the Preservation work type, and some captured under Maintenance. Agencies should take care in understanding how this work is funded and delivered to ensure costs are fully captured and not double counted in the TAMP.
• Funding for Operations and Routine Maintenance may come from its own budget line or be part of overall maintenance funding. Additionally, some operations expenses may be funded out of capital programming through programs such as the Congestion Mitigation and Air Quality (CMAQ) program. These activities are generally performed in a similar fashion regardless of asset condition. Therefore, these are commonly included as fixed-cost items that reduce the amount of funding available for activities that improve asset condition. Typically, this is included as an average annual cost.
• Organizational Strengthening activities can be budgeted separately from the other categories to clearly communicate their importance and show what activities are planned. These activities can be tracked using the time, labor, and material features in a maintenance management system.

A process for developing TAMP financial plans has been established through several documents. Most recently, NCHRP Report 1076, Guide for Incorporating Maintenance Costs into a TAMP updated this process to better incorporate maintenance costs (Allen et. al. 2023).

1. Determine the scope of the TAM program.
2. Identify maintenance fund sources.
3. Establish maintenance fund uses.
4. Structure maintenance sources and uses list.
5. Validated the list.
6. Document Constraints.
7. Document assumptions about fixed costs.

Investment Strategies

As described in Chapter 5 of this guide, investment strategies establish a long-term plan for how the agency will apply its resources to achieve its asset management objectives. Agencies typically develop investment strategies through a scenario-based planning process. In this process the agency evaluates costs and benefits of different investment scenarios to choose the most appropriate. Since the majority of TAM funding comes through capital programming that has traditionally been the focus of investment strategy development, incorporating maintenance costs and benefits into this process may require incorporation of additional performance measures, such as those established through Maintenance Quality Assurance (MQA). MQA performance measures are often more directly related to investments in maintenance activities, whether through in-house crews or contracts. NCHRP Report 1076, Guide for Incorporating Maintenance Costs into a TAMP suggests the following steps for developing investment strategies that fully incorporate maintenance investments and benefits (Allen et. al. 2023).

1. Define the role of maintenance in each scenario.
2. Identify existing commitments to future investments in maintenance.
3. Incorporate MMS and MQA data to improve predictions of future conditions.
4. Perform initial budget allocations, including maintenance.
5. Identify candidate projects and field crew capacity.
6. Develop scenarios for analysis.
7. Review predicted future conditions and predicted maintenance needs.
8. Finalize funding levels by use.
9. Document maintenance strategies for addressing performance gaps.
10. Document assumptions and strategies.