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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
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.
Acquire new data
as needed,
analyze and
improve the
Step 5
Identify data
gaps, analyze
data to formulate
Step 4
Identify existing
adverse event,
and cost data
Step 3
Identify section's
purpose and need
for GAM plan
Step 2
Transportation Research Record 2656
Step 1
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
2 mi
FIGURE 3 Idaho State Patrol callouts for rockfall along
mountainous road section in eastern Idaho (Landslide Technology/
Idaho Transportation Department:
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
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
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
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).
Transportation Research Record 2656
RHRS Total Score
Condition Index
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
Index Range
1. Good
2. Fair
3. Fair
4. Poor
5. Poor
Analysis of Montana DOT RHRS Values
by Condition State Group
RHRS Score
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
Condition Index
RHRS Total Score
$10.00 $20.00 $30.00 $40.00
Mitigation Cost per Square Foot of
Rock Slope Face (U.S. dollars)
Mitigation Cost per Square Foot of
Rock Slope Face (U.S. dollars)
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
Unit Cost of Geotechnical Mitigation
Rock Slope (ft2)
Soil Slope (ln ft)
Note: ln = linear.
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).
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
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).
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
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.
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.
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