Jump-Starting a Geotechnical Asset Management Program with Existing Data Darren Beckstrand and Aine Mines these cases, some geotechnical managers will want to use a sectionled approach to advance their management programs by adapting existing systems to maximize the benefits of asset management programs, demonstrate a forward-thinking approach for their area of responsibility, and effectively compete for limited funding. “Section” is analogous to the geotechnical or materials divisions, departments, bureaus, sections, and so forth that address geotechnical matters within a statewide, provincial, or regional transportation department. Although most states are implementing pavement and bridge asset management programs, only a few have instituted a program for geotechnical assets by using similar principles. However, many other states have used other systems to inventory and rank rock slopes and landslides. This paper focuses on unstable slope assets because the most data exist for such assets, but programs can be developed for a system’s other geotechnical assets, such as stable embankments, retaining walls, encroaching waterways, and off-ROW hazards (1). These systems, such as the pioneering rockfall hazard rating system (RHRS) (2), are well understood and adaptable and can be used to jump-start a geotechnical asset management (GAM) program that includes condition assessment, risk analysis, cost estimation, and lifecycle cost analysis. These approaches have been tested and implemented for rock slopes, soil slopes, embankments, and subsets of these by departments of transportation (DOTs) at various scales, such as in Alaska (3), Montana (4, 5), Idaho (6), and Colorado (7, 8) The benefits of transportation asset management principles have been proved for bridges and pavements. Some geotechnical section managers have recognized the value of applying these principles to a range of geotechnical assets. Current FHWA asset management requirements extend to bridges and pavements with optional recommendations for proactive asset management for all asset types, including geotechnical assets. Because the recommendations are optional, agency management may not be prompted to institute a state-level geotechnical asset management (GAM) program. A section-led approach can help such managers jump-start a GAM program with existing inventory data. This approach ensures that the developed program meets section needs and purposes, and the results may be used to influence future agency-level funding for GAM program implementation. The five-step process outlined in this paper walks interested section leaders through starting a GAM program in the absence of a top-down directive, focusing on unstable slope assets as examples and test cases. Existing data can be analyzed and used to develop decision support tools similar to those used in bridge and pavement asset management programs. The results will help the section identify high-risk locations or corridors and data gaps and guide improvements in collecting and integrating existing data sets. The results also will allow the section to develop and program risk-reduction measures and share these results with interested parties. Long term, a GAM program that is begun at the section level will guide agency-level development of best management practices that incorporate geotechnical assets. Transportation departments are increasing their use of transportation asset management (TAM) as a framework to project revenue needs, forecast future condition, select projects, and adopt smart business practices for the allocation of limited resources. The recognition of TAM as a best management practice has prompted the incorporation of asset management principles into the recent Moving Ahead for Progress in the 21st Century Act and Fixing America’s Surface Transportation Act (FAST act). Asset management programs typically are the result of executivelevel directives to develop and track the performance of the asset and plan interventions and investments to minimize long-term cost and promote wise fiscal management. Some states may be strictly adhering to federal directives about only developing TAM plans for pavements and bridges. Since the FAST act recommends asset management only for all other right-of-way (ROW) assets, a top-down requirement may not be forthcoming for geotechnical or other ancillary asset types. In Getting Started A section-led GAM program is customizable to suit the section’s needs with an eye toward eventual incorporation into an agencydriven comprehensive TAM plan. In the absence of top-level TAM directives, the section can benefit from the use of state-of-the-practice evaluation and analytical methods, ensuring that the section is fulfilling its duties and addressing potential geotechnical issues with minimal operational cost and higher performance. The following sections describe a five-step process for identifying the initial steps in a section-led GAM program. This process (Figure 1) was developed and refined during formulation of statewide GAM programs for Alaska (3) and Montana (4) and for federal land management agencies (9). Step 1. Identify Section’s Purpose and Need for GAM Landslide Technology, Inc., 10250 Southwest Greenburg Road, Suite III, Portland, OR 97223. Corresponding author: D. Beckstrand, darrenb@landslide technology.com. All DOT sections work to facilitate and support the continued function of the state’s transportation infrastructure. For direction, a section can evaluate its agency’s existing TAM plans, highway safety plans, and objectives that are tied to the economic support the highway system provides to users. Geotechnical assets either can support the continued Transportation Research Record: Journal of the Transportation Research Board, No. 2656, 2017, pp. 23–30. http://dx.doi.org/10.3141/2656-03 23 Acquire new data as needed, analyze and improve the program Step 5 Identify data gaps, analyze data to formulate relationships Step 4 Identify existing site-speciﬁc, adverse event, and cost data Step 3 Identify section's purpose and need for GAM plan Step 2 Transportation Research Record 2656 Step 1 24 Improve tracking tools, share data FIGURE 1 Five-step process for section-driven GAM program development. function of the transportation system or, in the absence of attention, can fail, causing major mobility delays, generating user costs in the hundreds of thousands to millions of dollars, and threatening user safety. Identifying and quantifying system vulnerability to higher operational cost, mobility interruptions, safety dangers, and unmanaged asset deterioration, and then reducing these risks in a cost-effective manner, are at the heart of implementing a GAM program. Typically, the section can use existing agency goals to define performance measures, levels of service, and condition targets to monitor geotechnical asset performance over time. The preparation of these overarching measures is not described in detail here but can be found in other publications (10–12) or within state-specific GAM plans, such as one in the final stages of development by the Alaska DOT. In an illustration of how to use existing data to support planninglevel decisions, the Montana DOT is developing decision support tools with data from its existing rock slope data set in addition to a selection of updated rock slope data. The results will allow the Montana DOT to use an existing geotechnical data set to meet statewide goals of efficient and cost-effective project selection and delivery. Coupling asset data with programmatic cost and risk estimates, the Montana DOT will be able to use risk-reduction, cost-benefit, and overall asset condition metrics to support efficient budget allocations and will be able to broadly define distinct performance goals that are based on corridor importance (e.g., Interstate versus minor arterial). Step 2. Identify Existing Data Existing Inventory Systems Many states, particularly the mountainous states, have implemented slope management systems (13). Most of these states (Figure 2) have fully or partially implemented an RHRS, and some, such as FIGURE 2 Highlighted states have implemented slope management systems (13). Washington, Alaska, Oregon, and Ohio, have performed landslide rating systems. A few states maintain retaining wall inventories that are independent of bridge abutments. By collecting this data, these states have overcome the primary hurdle to implementing a GAM program: completing an inventory and assessment of specific geotechnical assets. Some states may already have these data in an enterprise relational, form-based database system (e.g., Oracle) lacking the ability to query, analyze, and map trends. Others may use an internal geographic information system or online geographic information system databases, and still others may have the data in a spreadsheet file with a sole data gatekeeper maintaining and controlling access. Whatever the data storage medium, the ability to access and query this information is key to adapting valuable data into the next generation of a GAM system. Discussions with the agency’s information technology section will yield possibilities with a one-time data extraction or brief training. Geotechnical Event Data Sources Most states that have implemented inventory systems have not moved forward with detailed tracking of geotechnical events that lead to service disruptions or excessive maintenance. Some leadingedge states track specific job codes in their maintenance section’s management systems, and although the extraction of data into geolocated events and expenditure concentrations can be difficult, the results are rewarding. Alaska recently completed mining more than 7,000 entries from its maintenance management system for 2005 to 2016 that relate to geotechnical events, facilitating spatial and temporal analysis of events and accumulating costs statewide, resulting in a first-ever display of where the department is concentrating expenditures over time. Illustrating the benefits of performing this data extraction, a time-lapse video of the Alaska DOT results is available online (14). Without a mandated time-tracking system linked to activity codes, discussions and interviews with road maintenance personnel (e.g., Where are you cleaning rock off the road? Where are the ditches continually filling with landslides or rockfall debris? Which walls appear to be bulging or corroded?) are a first step in identifying areas of highest concern. Once mapped, these areas demonstrate to management the value of collecting and tracking such information. This approach also exhibits collaborative team efforts between the so-called boots on the ground and the geo technical personnel, facilitating future data gathering and reporting of adverse events. Other data sets may be available from partner government agencies. For instance, the Idaho State Patrol collects and stores road users’ reports of rock on the road. Filtering, geolocating, and heat mapping of these rockfall callout events, even on a corridor basis, indicate where rockfall has been serious enough to arouse public concern and where potential hazards exist (Figure 3). Beckstrand and Mines 0 1 2 mi FIGURE 3 Idaho State Patrol callouts for rockfall along mountainous road section in eastern Idaho (Landslide Technology/ Idaho Transportation Department: http://arcg.is/1obt74f). Programmatic Mitigation Cost Data Sources One of the primary functions of a GAM program is the ability to program costs for partial or full mitigation to reduce long-term department operational costs and decrease user risk. However, geotechnical problems typically cannot be mitigated with a prescribed catch-all approach because of the wide variety of possible conditions requiring attention. Particularly challenged are geotechnical professionals who traditionally prepare cost estimates for individual problems and unique site geology. Addressing costs at a programmatic level does not require sitespecific mitigation designs for the hundreds or thousands of sites in the inventory. Transportation agencies maintain databases of unit prices and total construction costs, including plan, specification, and estimate and construction management, for slope mitigation and retaining wall projects that can be applied to a range of problems and various geologic materials. Following is a summary of data sources frequently available that can be used to draw relationships between the feature’s condition and size to extend these relationships to the rest of the section’s geotechnical features: • Inventory data source: – Rated asset inventory (e.g., RHRS), – Road-viewer-type program, – Maintenance personnel, – As-built plans, and – Topographic maps, • Event data source: – Maintenance records, – Geotechnical section records, – State police, and – Local agencies, and • Cost data source: – Bid tabs, – Published values, – Construction cost index, – Maintenance tracking, and – AASHTO. Data from other agencies may be used to develop useable programmatic mitigation costs. For example, the Alaska DOT does not have mitigation cost data for unstable slopes. To move forward with 25 its GAM program, the department used conceptual mitigation costs developed by the Montana DOT and the Washington State DOT (3). After implementing an RHRS system in 2004, the Montana DOT developed conceptual mitigation costs for the 100 highestscoring rock slopes in its data set (5). For more than a decade, the Washington State DOT has been developing conceptual mitigation costs for unstable soil and rock slopes as part of the state’s unstable slope management system. A site-specific conceptual mitigation approach is developed and estimated with standard unit costs if an unstable slope management system site meets specific condition and traffic volumes. These conceptual costs are sometimes sent to the regional office, which incorporates additional items into a final cost estimate (e.g., traffic control and ROW costs) (15). Because both Montana and Washington rated their geotechnical assets before developing conceptual costs, it is a small step from site-specific cost estimates to broad estimates of mitigation costs based on asset condition, which can be used in initial development of high-level plans (3). The Alaska DOT and other agencies administering assets with geologic conditions and construction costs similar to those of Montana and Washington may be able to use these condition state–mitigation cost correlations until sufficient agency-specific data become available for development of their own correlations. Step 3. Identify Data Gaps and Analyze Data to Formulate Relationships Relating Asset Evaluation Scores into Asset Condition Measures One of the first steps in starting a materials- or geotechnical-sectiondriven asset management program is to develop a method that inventories and evaluates assets. This differs from an agency-mandated program in which the typical first step is defining the agency’s goals and objectives within its TAM plan or other guidance documents. The Montana DOT is making its well-established RHRS program TAM compatible, and its experiences are used as examples throughout this section. Other landslide, embankment, or wall rating systems can be adapted by using similar techniques. If the agency is already using an RHRS-type program, existing asset evaluation scores incorporate both hazard (the likelihood that an adverse event will occur) and risk (the likely extent to which this adverse event will affect corridor function). Asset condition is derived from a subset of the hazard rating categories. As part of the Alaska DOT’s first-in-the-nation GAM program, researchers evaluated various methods for deriving rock slope condition from hazard rating category scores. It was determined that for rock slopes, asset condition is best described by rockfall activity and ditch effectiveness scores. The actual effects of other individual rating categories, such as slope height, geologic character, and rock size, are captured in these two categories. In other words, if a tall slope with a mitigated or apparently stable adverse geologic character is otherwise performing well by producing little rockfall activity and the ditch is designed to contain potential falling rocks, then the slope is in a good condition. Likewise, if a short slope with similar geologic characteristics has moderate rockfall activity and an inadequate ditch, the slope is in a fair to poor condition. Both slopes may have received similar RHRS scores, but one is in a worse condition state than the other (3). 26 Transportation Research Record 2656 700 600 RHRS Total Score 500 400 300 200 100 0 100 90 80 70 60 50 40 Condition Index 30 20 10 0 FIGURE 4 Comparison of RHRS scores and condition index scores for sites in existing Montana DOT RHRS program. RHRS programs apply an exponential rating system, whereas a condition index approach uses a linear rating system in which a higher value indicates better asset condition, similar to the education grading system (3). To facilitate TAM compatibility, the exponential RHRS scores (3, 9, 27, 81, good to poor) were converted to linear condition index scores (100 to 0, good to poor). This change facilitates communication between technical and managerial personnel as well as with the public. With the condition index score, an asset is grouped into good (1), fair (2, 3), or poor (4, 5) condition states. The numerical condition (1 through 5) provides greater resolution for defining condition state–based models of risk or cost, while the good– fair–poor descriptors adhere to existing guidelines for pavement and bridge TAM programs (16). The total RHRS scores and condition index scores for the 869 sites rated in the Montana DOT’s original RHRS program are shown in Figure 4. As expected, higher RHRS scores indicate poorer asset condition, but high scores in traffic or roadway width categories (risk factors) can increase the RHRS score for a site that is performing well. Table 1 presents the average RHRS score for each condition state. The average asset RHRS score increases with deteriorating TABLE 1 Relationship Between Condition State, Condition Index, and Total RHRS Score in 2004 Montana DOT Data Set Condition Index Range Condition State High 1. Good 2. Fair 3. Fair 4. Poor 5. Poor 100 80 60 40 20 Analysis of Montana DOT RHRS Values by Condition State Group Low Average RHRS Score Average Score Percentile Standard Deviation 80 60 40 20 0 227 289 330 427 597 18 38 51 79 97 87 90 96 95 66 condition state, implying that the condition measures effectively capture asset performance. Percentiles were calculated for each average RHRS score to indicate where these average scores fall in the data set. The standard deviations between scores within the five condition state categories are presented to indicate the noise when total RHRS score is used to describe asset condition. Developing Average Mitigation Costs from Asset Condition In addition to identifying corridors with high-risk and poorcondition slopes, the GAM research is used to develop programmatic, network-level cost estimates for mitigation of a suite of sites according to site condition. These mitigation cost estimates have focused on unstable soil and rock slopes because of the availability of quality inventory, assessment, and conceptual mitigation costs. Following completion of the site evaluations for the Montana DOT, conceptual mitigation costs were developed for the 100 highestscoring sites in the 2005 study. To develop a mitigation cost per square foot for use in site comparisons, a random sample of the rated sites in Montana were then used to estimate an average rock face area as a percentage of slope length, height, and an average slope angle. Following examination of the selected sites, the applied estimate of slope face area was 65% of slope length times slope height, attributes measured in the field during the rating process, and factors in an area slightly more than a basic triangular area and an average slope cut angle of ½H:1V. As shown in Figure 5, there was no apparent correlation between RHRS score and mitigation cost per square foot of rock slope face. An asset’s total RHRS score is a poor predictor of projected mitigation cost. In contrast, plotting of asset condition versus mitigation cost shows a somewhat clearer correlation. This outcome further supports the rating categories chosen to define asset condition. The scatter present in both correlations is attributed to the variable geology underlying geotechnical assets and the escalating costs that more complex geology typically demands, variables that do not exist to the same degree in pavement or bridge management systems. Beckstrand and Mines 27 750 Condition Index RHRS Total Score 700 650 600 550 500 450 400 $0 $10.00 $20.00 $30.00 $40.00 Mitigation Cost per Square Foot of Rock Slope Face (U.S. dollars) 100 90 80 70 60 50 40 30 20 10 0 $0 $10.00 $20.00 $30.00 $40.00 Mitigation Cost per Square Foot of Rock Slope Face (U.S. dollars) (b) (a) FIGURE 5 RHRS score and mitigation cost and asset condition versus mitigation cost for 100 highest-scoring sites in Montana DOT’s RHRS program in 2003. With use of the condition–mitigation cost correlation, average mitigation costs can be developed that are based on rock slope condition, as detailed in other papers (15). The ongoing Alaska DOT GAM program provides an example of obtaining estimated improvement costs based on asset condition. During research work, the Alaska DOT obtained data sets of RHRS scores and conceptual mitigation costs from the Montana DOT (for rock slopes) and from the Wisconsin DOT (for soil slopes). Although not shown here, retaining wall data were obtained from the Alaska DOT’s bid tabs and analyzed in a similar fashion. Average condition states and average mitigation costs were calculated from existing data, and initial unit costs were developed for application to long-term planning and budget forecasting. The mitigation component costs for the Alaska DOT’s geotechnical assets are presented in Table 2 (3, 16). These average mitigation cost estimates are not applicable at specific sites because of the many potential problems, site access, choices of mitigation measures, and specific improvement objectives. However, these estimates are intended to accurately reflect the average cost of mitigation for a suite of unstable slopes with known condition states. As the number of unstable slopes under consideration grows to, for instance, 20 slopes along a 10-mi segment of TABLE 2 Estimated Unit Costs for Mitigation of Rock Slope and Soil Slopes Based on Condition State Number of Condition States Improved by Mitigation Activitiesa One Two Three Four Unit Cost of Geotechnical Mitigation Components Rock Slope (ft2) Soil Slope (ln ft) 3.56 7.12 10.70 14.20 1,170 2,330 3,500 4,670 Note: ln = linear. a Partial improvements may only improve slope condition one or two condition states (CS), while full mitigation or reconstruction may improve condition to a 1. For instance, simple ditch improvement on a CS 4 may only improve CS from a 4 to a 3 while further measures and expenditures may improve the slope to a 2 or 1. road, the estimate of the sum cost of improvement becomes more appropriate. These average costs will help with departmental planning and resource allocation. This data set should be updated periodically to incorporate new project cost data, leading to improved mitigation estimates over time. Combining deterioration rates (17), programmatic cost estimates, and inflation will permit forecasting of future asset condition given various investment scenarios. Assessing Future Monetary Risks to Current Asset Conditions In early 2016, the Montana DOT’s district geotechnical staff completed a survey on rockfall events that had affected the transportation system. System effects included road closures, traffic slowdowns, property damage, and injury. The final summary of adverse events included event location, RHRS section number, and a breakdown of event impacts. Respondents provided specific event dates, if available, or a date range. Slope condition states at the event locations were then calculated with use of the 2005 RHRS asset evaluation scores. District 1 (Missoula) provided the most thorough set of survey responses, and its responses were extrapolated to rock slopes in other parts of the state. Annualized rates for various adverse events were calculated for sites in the survey and grouped by Condition States 1 through 5. The annual likelihood of an event from each condition state group was calculated by analyzing event reports and the size and condition of the slopes they originated from. Dividing this annual event likelihood by the total inventoried square footage in each condition state generated an adverse event likelihood per square foot based on slope condition. The total square footage is the sum of the areas of all inventoried rock slopes in District 1, both those that generated adverse events and those that did not. This method captures the greater risk posed by larger sites. Plots of average adverse event likelihood versus condition state were developed for each adverse event type with linear best-fit equations. By using AASHTO or section values for event impact costs (e.g., detour length, travel time, additional mileage, accident costs), asset condition, and condition state–based likelihood, sections can develop the projected risk costs required for cost–benefit analysis in an asset management plan (18). 28 Step 4. Acquire New Data and Expand Program as Needed Once initial work has been completed, sections will determine how frequently existing data should be reviewed and new data acquired. New data can be incorporated on an ad hoc basis (i.e., following a specific mitigation project); however, to capture deterioration and change throughout the system, more extensive asset evaluations should take place at regularly scheduled intervals. Other data, such as incorporating geotechnical event data, may require focused effort on an annual basis. For example, a staff person may review events from the past year and call a maintenance supervisor to follow up on costs or traffic impacts after a specific event. Maintenance personnel who see the potential benefits of asset management may voluntarily contact section geotechnical staff with additional information and photos. During work on Steps 2 and 3, the section should identify how it plans to maintain existing data, improve or modernize the database, and incorporate additional data. In early TAM programs, significant unknowns with regard to costs, deterioration rates, and failure consequence, among other items, were overcome by applying expert opinions by using the Delphi process. As later data collection and tracking became common practice, actual condition data and deterioration models replaced the Delphi models. Similarly, after GAM databases become populated with repeat condition surveys, failure events, delays, and costs accumulation, analysis will help improve models. Though the highly varied nature of the geology underlying geotechnical assets will muddle even the most informed forecast models, the section will improve cost correlations, reduce uncertainties, and improve its GAM program by implementing the following: 1. Improve cost tracking of specific mitigation efforts and the degree of improvement offered. For instance, adding a roadside barrier to a CS4 may improve it to a CS3 and is a relatively common low-cost approach to incremental improvement. 2. Incorporate triggering event, location, cost, consequence, and response action tracking of adverse geotechnical events, possibly in the department’s maintenance management system (or equivalent). Sections should track routine maintenance costs in response to chronic problems, such as ditch cleaning and pavement patching. 3. Explore options for additional evaluation categories or performance measures that may better reflect certain geologic characteristics, such as photogrammetric change detection or quality indicators such as rock mass ratings (19) or geologic strength index (20) for estimating condition, performance, and cost to maintain rock slopes, for example. 4. Consider performing more complex mathematical analysis to determine the principle components leading to poorly performing geotechnical assets. 5. Assess system vulnerability and risk from large, infrequent geotechnical events that threaten the network or specific, highconsequence corridors. These could be large, marginally stable landslides that could close Interstates for months in the event of failure, rock slides that bury roads, or large debris flows from extreme storm events. The likelihood of these events occurring and resulting in worst-case consequences would require a thorough geotechnical investigation, risk assessment, and possibly early warning systems. Transportation Research Record 2656 Hazard mapping outside the ROW that includes features not currently affecting the roadway would be required. 6. Complete the modeling process to include deterioration, lifecycle cost analysis, and budget or condition forecasting to determine funding levels to achieve or maintain performance goals. Identifying a program to which the agency already has access will save on implementation costs and make any required information technology support easier to maintain. For example, many transportation agencies already have subscriptions to geospatial software hosted online, such as ESRI’s ArcGIS Online (AGOL). These programs often include access to templates that help users quickly build applications introducing the new GAM program. North Carolina is developing a GAM program entirely within AGOL, to which the North Carolina DOT already has a subscription (21). However, the section must remain aware of the limitation of the tools it uses for acquiring new data. For example, the AGOL program used by the North Carolina DOT cannot perform calculations in the field, which could be an issue if the section’s existing RHRS program has calculated category scores. Attentive consideration of offline capabilities and data layers is required. Step 5. Improve Tracking Tools and Access to Data Within a section-led program, the products developed in the previous steps can be a powerful argument for increased attention and program funding. Examples of data compilation and display in an easily accessible format make for powerful arguments and nearly self-evident conclusions. This clear communication helps justify programs to address data gaps or to improve tracking tools. For example, as part of a pilot program, the Colorado DOT recently provided maintenance personnel with mobile devices so that service disruptions could be photographed and uploaded to the geotechnical event database in near real time (22). The results of an emerging GAM program should be shared in a variety of ways, from articles in agency publications to embedding in web pages. Figure 6 contains the first page of a so-called story map interactive application used by the Alaska DOT to introduce its new GAM program to intradepartment personnel. Because data that are hidden or difficult to access will undermine asset management programs, results should be shared through a portal that is easily accessible by other section and agency members and heavily marketed to planners and designers. In this way, deteriorated geotechnical assets can be incorporated into rehabilitation projects, which over time will save the agency money. Alaska’s GAM program is already paying dividends through data sharing. Existing GAM data guided the field reconnaissance for a highway improvement project, resulting in savings to the department (23). Conclusion The five-step process outlined in this paper guides DOT sections in applying asset management principles to management of their geotechnical resources. Although developing an asset management program may appear daunting, the data required to commence development are frequently already within the section’s grasp. It may require only diligence and some creative thinking to bring disparate data sources Beckstrand and Mines 29 FIGURE 6 Interactive online GIS application for program introduction and mapping. together to meet the section’s decision-making needs. Building out from existing data is cost-effective, and it provides a strong argument for applying asset management principles to geotechnical assets. This initial work may be used to acquire support from upper levels of management, while work continues to fill in data gaps, improve tracking systems, and integrate geotechnical assets into the agency’s overarching TAM system. Relationships between asset condition, mitigation costs, and risk can be derived from the analysis of existing data or by building on data previously collected and analyzed by another section or agency. Although these correlations do not replace detailed, site-specific evaluations, when properly applied in the context of an asset management program, they provide estimates that can be used as decision support tools in the initial phases of budgetary planning for large projects. In the long term, compiling existing asset data and working to reevaluate asset condition at regular intervals will help the section develop deterioration curves. Combining these deterioration curves with mitigation cost estimates will enable the section to forecast future asset condition based on current and future investments. From that point on, the section is integrated into an agency-led asset management program. Acknowledgments This paper was prepared with insight gained during federally funded and state-funded research and planning projects for the transportation departments of Alaska, Montana, and Idaho, as well as with the Western Federal Lands Highway Division of FHWA. The Washing- ton State, Alaska, Montana, and Idaho Departments of Transportation provided data for inclusion in the analysis. The authors thank the anonymous reviewers and the TRB Subcommittee on Geotechnical Asset Management for review and insight. References 1. Anderson, S. A., V. R. Schaefer, and S. C. Nichols. Taxonomy for Geotechnical Assets, Elements, and Features. Presented at 95th Annual Meeting of the Transportation Research Board, Washington, D.C., 2016. 2. 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Presented at 67th Highway Geology Symposium, Colorado Springs, Colo., 2016. The Geological and Geoenvironmental Engineering Section peer-reviewed this paper.