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41 C h a p t e r 5 With a strategic framework defined to guide resource allocation decisions, the research team developed an Excel model to demonstrate the logic framework and the optimization logic. The team populated a sample case application based on plausible data that was adapted from the project research and previous experience. This demonstration of the solution model was initially displayed in a walk-through and test with the NCHRP Project 14-21 panel and was later refined as an initial logic framework. The logic framework and demonstration model were then applied and tested in the context of two real state DOT preservation resource allocation environments, providing two actual case studies. After lessons learned from the case studies were factored into the allocation logic and dem- onstration model, a workshop was held with the panel, key managers from the two DOTs that supported the case studies, and other SMEs. The workshop objective was to review the allocation logic framework and provide comments and suggestions on improvements that would enhance the broader usability and adoption potential across the transportation agency community. 5.1 Case Studies The case studies enabled the research team to test the Resource Allocation Logic Framework in situations using different terms and taxonomies for preservation activities and with different approaches to the process, logic, and data used for resource allocation. Two state DOTs were viable and willing candidates (Washington State DOT and Nevada DOT) for the real-world resource allocation case studies. Each employed established allocation practices following different approaches. Washington State DOT uses a centralized preservation resource allocation approach, whereas Nevada DOT uses more of a hybrid district-specific allocation approach. The research team also discovered wide variations in the taxonomies, scope of pres- ervation work addressed, methods for estimating resource needs, and processes for allocation adjustment decisionmaking. These varied considerably from the more generic processes and taxonomies encountered in previous research by the research team and by NCHRP. A general conclusion by the research team was that the allocation logic framework would need to allow wide latitude for variations in terminology and definitions of assets and preservation activities as well as for variations in the data that can reasonably be available to support quantification of preservation needs for NBP12 assets and activities. Case Studies and Workshop Findings 12Refers to assets and activities other than bridge- or pavement-related (Non-Bridge/Pavement assets/activities).
42 resource allocation Logic Framework for highway asset preservation The research team conducted a number of detailed telephone discussions with representatives from these two agencies and gathered information about the following: ⢠Overall processes used to allocate preservation and maintenance funds across different asset groupings ⢠Scope of the preservation and maintenance program and the asset groupings covered ⢠General taxonomy of assets and activities used in a preservation context ⢠Approach for assessing the performance or condition of assets ⢠Units of measure used to define inventory for different asset groups ⢠Rating standards and approaches to assess performance or condition ⢠Inventory data for bridge, pavement, and other asset groups addressed by the preservation programs ⢠Information on how the expenditures and accomplishments (by asset groups) are tracked and recorded ⢠Capabilities to determine unit costs from historical data Both Washington State DOT and Nevada DOT shared extensive information during the dis- cussions and also provided relevant documentation and data. Key findings from the case studies include the following: ⢠In general, assets are considered to be under the preservation program by both Washington State and Nevada DOT. However, taxonomy of assets was found to be different between the states. ⢠Both of the agencies have excellent condition and inventory data for pavements and bridges. ⢠Data for other NBP assets is very scarce (e.g., signs, guardrails, barriers, rest areas) â While historical expenditure data on NBP assets was generally available, little specific data was available on condition and inventory for NBP asset groups. â Both the agencies are working toward building a database to improve monitoring of condi- tion and inventory of NBP asset groups. ⢠It is difficult to accurately track expenses on NBP assets, because the costs are embedded as a part of major projects. ⢠Washington State uses formula-based allocation for NBP assets, and the allocation percentage is based on a percentage of pavement preservation projects. The percentage used for allocating funds for NBP assets is monitored and calibrated using analysis expenditure trends for NBP- related preservation work connected to pavement projects. ⢠Unit costs and deterioration rates can be reasonably determined for bridges and pavements. The research team greatly benefited from the data gathered, and the framework was modified as needed based on the findings from the two case studies. The research team has the impression that both agencies saw merit in a straightforward solution to balancing allocations based on need, espe- cially for testing and assessment of results and performance expectations after allocation adjust- ments have been necessary to address funding shortfalls. The concept can be implemented for bridge- and pavement-related preservation activity, but lack of data for most NBP-related activities is a hindrance at present. However, it is reasonable that a sufficient estimating basis can be devel- oped over time to support preservation needs assessment for activities related to NBP assets. The research team recognized that the allocation solution should be flexible enough to accommodate an initial lack of NBP data but should provide for the NBP data as it (and if it can be) developed. 5.2 Workshop Findings Upon completion of the case studies, the research team conducted a 1-day workshop in Washington, DC, to present the case studies, to indicate modifications to the solution model, and to gather comments and reactions from 15 practitioners, including the NCHRP Project 14-21 panel.
Case Studies and Workshop Findings 43 The research team and workshop participants defined and shared resource allocation issues and challenges, discussed the findings from case studies, reviewed the revised Resource Allocation Logic Framework and assessed the practicality of the solution. The research team conducted a live demonstration of the Excel model and conducted interactive model testing, where the participants were provided an opportunity to observe âtestâ scenarios. The live demonstration session was then followed with open discussion to receive inputs on the usability of the logic, data, and other issues. The workshop participants identified the refinements needed to the framework and demon- stration model. These have been subsequently implemented by the research team and are reflected in this report and logic framework. The key changes and improvements include the following: 1. Simplified Terminology and Acronyms. The original model used terms, such as Performance/ Condition/Life (PCL) standards and Performance/Condition/Rating (PCR), to describe the performance condition rating of the assets. As these terms may be confusing or difficult to define across different agencies and asset types, the research team simplified and built in flex- ibility for user-defined terms to identify the key input variables. 2. Easier Navigation of the Demonstration Model. The ease of use of the model was improved, making it easier (a) to navigate by using fewer input tabs and (b) to enter the data needed. In particular, the research team simplified the inputs needed for setting up allocation weights. Addi- tional features were implemented to protect the formula cells in the Excel spreadsheet model. 3. Scalability and Adaptability to Variations in Terms and Scope. The scalability and adapt- ability of the model was enhanced so that the users can enter up to 15 AAGs and up to 15 districts. All AAGs and terms for them are defined by the user. 4. Improved Summary Tables. The model was updated to provide summary output tables for easy reading of results. The enhanced model summarizes the final allocation across differ- ent asset types and also estimates the expected rating result post-allocation. Tables show the allocation results; comparisons between current, target, and expected (after allocation adjust- ment) rating results; and comparisons between desired time and actual time to reach target rating. A feature to export the results to a new Excel workbook was also implemented, so that users can save the model results for future reference. 5. Flexible Taxonomy. The model now allows users the flexibility to set up asset/activity taxono- mies, units of measure for each AAG, and basis for rating. The new tool is flexible enough to allow users to set up multiple groupings of asset/activities, such as flexible pavements and rigid pavements, to accommodate significant variations in deterioration rates, unit costs, or asset inventories. For example, the user can now treat asphalt overlay and chip seal as separate AAGs. 6. Alternate Needs Computation and Optimization Depending on Data. There are added fea- tures in the model to address situations where data is available for only some of the AAGs. The user can provide needed data inputs to any number of the NBP groupings, and the model will compute preservation needs for those groupings. If necessary data is not available for a particular AAG, preservation needs can be computed based on historical allocations and escalation or as a percentage of the needs for a related asset. For example, guardrail preserva- tion could be treated as a percent of pavement preservation, based on historical patterns. 7. Optimizing Allocations When Funding Does Not Cover Deterioration During the Cycle. The model was also updated, so that in situations where available funds are less than suffi- cient to prevent an overall decline in asset condition, the allocation can still be performed to minimize impact and to compute the overall performance/condition result expected at the end of cycle. This is considered a key feature for decisionmaking on the overall funding for preservation, because it will clarify the performance impact of investment of scarce funds in programs other than preservation. The case analyses and workshop resulted in significant improvement in the user friendliness and adaptability of the computational model as well as the ability of the logic framework to pro- vide rational outcomes in a wider variety of plausible allocation scenarios. The computational model is described in Chapter 6.
