7.2 Collecting Asset Data

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Section 7.2

Collecting Asset Data

This section discusses approaches to collecting and maintaining asset inventory and condition data. It builds on the discussion of monitoring requirements included in Chapter 6.


Section 7.2

Collecting Asset Data

This section discusses approaches to collecting and maintaining asset inventory and condition data. It builds on the discussion of monitoring requirements included in Chapter 6.


7.2.1

Deciding What Data to Collect


Many organizations have recognized that data should be viewed as an asset. Before acquiring new data, it is important to establish a clear statement of how the data will be used and what value it is expected to provide.


Deciding what data to collect involves identifying information needs, estimating the full costs of obtaining and managing new data and keeping it up to date, and then determining whether the cost is justified. Just as agencies don’t have unlimited resources to repair and replace their assets, there are also limitations on resources for data collection and management.

A 2007 World Bank Study summarized three guiding principles for deciding what data to collect:

  • Collect only the data you need;
  • Collect data to the lowest level of detail sufficient to make appropriate decisions; and
  • Collect data only when they are needed.

Chapter 6 can be used to help identify the information needed to track the state of the assets and investments to maintain and improve them. The basic questions one needs to answer to identify needed data are:

  • What decisions do we need to make and what questions do we need to answer that require asset data? Typically, an organization needs to be able to answer questions including but not limited to its asset inventory, the conditions and performance of the inventory, and how resources are being spent on its assets. Also, an organization needs to determine what work is needed and how much that work will cost.
  • What specific data items are required or desired? Next, one must identify the data required to meet the established information needs. There may be other data items that are not strictly required, but that may be useful if collected in conjunction with the required data. For instance, answering questions and making decisions regarding pavement an organization would typically want to have an inventory of existing pavement, details on paving materials used, and details on current conditions. Additional information on treatment history or substructure conditions might not be strictly required, but if available could enhance the decision-making process.

It is also important to incorporate standard data elements for location and asset identification into requirements, ensuring consistency with other asset data in the agency.

  • What value will each data item provide? It is important to distinguish “nice to have” items from those that will clearly add significant value. The cost of collecting and maintaining a data element should be compared with the potential cost savings from improved decisions to be made based on the element. Cost savings may be due to asset life extension, improved safety, reduced travel time, or internal agency efficiencies. In addition, proxy measures for information value can be considered such as the number and type of anticipated users, and the number and type of agency business processes to be impacted.
  • What level of detail is required in the data? Level of detail is an issue for all assets, but is particularly an issue for linear assets such as pavement, where one may decide to capture data at any level of detail. For instance, to comply with Federal reporting requirements for pavement condition a state must collect distress data at 1/10 mile intervals for one lane of a road (typically the outside line in the predominant direction). For other applications it may be necessary to collect data for additional lanes, or at some other interval.
  • TIP
    Look for ways to "collect once, use multiple times" by leveraging existing data and planning data collection efforts to capture information about multiple assets.

  • What level of accuracy is needed? The degree of accuracy in the data may have a significant impact on the data collection cost and required update frequency. Ultimately the degree of accuracy required in the data is a function of how the data are used. For instance, for estimating the clearances under the bridge for the purpose of performing a bridge inspection it may be sufficient to estimate the clearance at lowest point to the nearest inch using video imagery. However, more accurate data may be required when routing an oversize vehicle or planning work for a bridge or a roadway underneath it. If a high degree of accuracy is not required it may be feasible to use sampling strategies to estimate overall conditions from data collected on a subset of assets.
  • How often should data be updated? Is the data collection a one-time effort, or will the data need to be updated over time? If data will need to be updated should the updates occur annually, over a period of multiple years, or as work is performed on an asset?

Table 7.2 below illustrates examples of data collection strategies that might address different information needs.

7.2 Example Data Collection Strategies

Example Asset(s)Type of InformationExample DecisionsExample Data Collection Strategies
Pavement MarkingsTotal asset quantity by type, district, and corridor or subnetworkBudgeting for assets maintained cyclicallyEstimation based on sampling

Full inventory every 3-5 years with interim updates based on new asset installation
Roadside SignsInventory of individual assets – location and typeWork planning and scheduling for assets maintained cyclically

Project scoping
Full inventory every 3-5 years with interim updates based on new asset installation
GuardrailInventory + General Condition (e.g. pass/fail or good-fair-poor)Work planning and scheduling for assets maintained based on conditionInventory and condition assessment every 2-3 years

Inventory and continuous monitoring (e.g. from maintenance crews or automated detection)
BridgesInventory + Detailed ConditionTreatment optimization for major, long life cycle assetsInventory and condition assessment every 1-2 years + continuous monitoring (e.g. strain gages on bridges)

Once a general approach has been established, more detailed planning for what data elements to collect is needed. Prior to selecting data elements, identify the intended users and uses for the data, keeping in mind that there may be several different uses for a given data set. Identify some specific scenarios describing people who will use the information, and then validate these scenarios by involving internal stakeholders.

One common pitfall in identifying information needs is failing to distinguish requirements for network level and project level data. While advances in data collection technology make it feasible to collect highly detailed and accurate information, it is not generally cost-effective to gather and maintain the level of information required for project design for an entire network of assets.

A second pitfall is failing to consider the ongoing costs of updating data. The data update cycle can have a dramatic impact on data maintenance costs. Update cycles should be based both on business needs for data currency and how frequently information is likely to change. For example, asset inventory data is relatively static, but condition data may change on a year-to-year basis.

A third common pitfall is taking an asset-by-asset approach rather than a systems approach in planning for both asset data collection as well as downstream management of asset information.

Even when there is a strong business case for data collection, it is sometimes necessary to prioritize what data are collected given budget and staffing constraints. Some agencies do this by establishing different “tiers” of assets. For example:

  • Tier 1: Assets with high replacement values and substantial potential cost savings from life cycle management (such as pavements and bridges)
  • Tier 2: Assets that must be inventoried and assessed to meet legal obligations (such as ADA ramps, stormwater management features)
  • Tier 3: Assets with high to moderate likelihood and consequences of failure (such as traffic signals, unstable slopes, high mast lighting and sign structures)
  • Tier 4: Other assets that would benefit from a managed approach to budgeting and work planning (such as roadside signs, pipes and drains)

While updating data can be expensive, various strategies are available for combining data collection activities to reduce the incremental cost of collecting additional data. For instance, one approach to collecting data on traffic signal systems is to update the data when personnel perform routine maintenance work. Also, in some cases data can be extracted from a video log captured as part of the pavement data collection process.

Given limited resources for data collection, it may be helpful to formally assess the return on investment from data collection or prioritize competing data collection initiatives. A formal assessment may be of particular value when considering whether the additional benefits from collecting additional data using a new approach justify the data collection cost. NCHRP Report 866 details the steps for calculating the return on investment (ROI) from asset management system and process improvements, including asset data collection initiatives.

Oregon DOT

  • Tier 1: Assets with high replacement values and substantial potential cost savings from life cycle management (e.g. pavements and bridges)
  • Tier 2: Assets that must be inventoried and assessed to meet legal obligations (e.g. ADA ramps, stormwater management features)
  • Tier 3: Assets with high to moderate likelihood and consequences of failure (e.g. traffic signals, unstable slopes, high mast lighting, sign structures)
  • Tier 4: Other assets that would benefit from a managed approach to budgeting and work planning (e.g roadside signs, pipes and drains)

7.2.2

How to Collect Data


As technology continues to advance there are more methods available for collecting data related to assets. It is important for agencies to understand the technology and options available for data collection. Depending on the asset-type or data needed, a different data collection approach may be preferable. This section provides information on making that decision.


There are many different approaches to collecting asset and related data. Often a mix of approaches is used, including visual inspection, semi-automated and automated approaches. The technologies for data collection are advancing rapidly, allowing for increased use of semi-automated and automated approaches for collecting more accurate data at a lower cost. Examples of recent innovations include:

  • Improvements in machine vision that allow extracting some forms of asset inventory data from video or LiDAR.
  • Use of unmanned aerial vehicles (UAV, also called drones) for allowing bridge inspectors to obtain video of hard-to-reach areas of a bridge.
  • Improvements in non-destructive evaluation (NDE), allowing for greater use of techniques such as ground penetrating radar (GPR) for pavement and bridge decks and instrumenting bridges to monitor performance over time.
  • Improvements in hand-held devices allowing for increased field use, reducing cost and time of manual data collection.

Several of these technologies provide opportunities to save money by collecting data for multiple assets within a single collection effort. Table 7.3 provides a summary of potential data collection approaches for common roadway asset classes.

TIP
Before collecting new data, make sure you are leveraging data that already exists or is already collected, and coordinate with other agency groups that may have a need for the same data.


Table 7.3 - Example Data Collection Approaches

Asset ClassData Collection MethodData CollectedNotes
PavementVisual InspectionPresent Serviceability Index (PSI)Often used in urban environments or for small networks where data collection using automated collection approaches is impractical – can be supplemented by UAVs
PavementAutomated data collection vehicle with laser scanning systemroughness, cracking, nuttingIncludes a range of 2D video and 3D laser-based systems. Many systems store video images and can capture additional measures, such as cross slope, gradient and curvature
PavementLight Detections and Ranging (LiDAR)/ Terrestrial Laser Scanning (TLS)roughness, cracking, nuttingProvides a high resolution continuous pavement survey. Often inventory data for other assets can be extracted from the data set
PavementFalling weight deflectometerstrength/deflection
PavementLocked wheel tester/spin up testerskid resistance
PavementGround Penetrating Radar (GPR)layer thicknesses, detection of voids and crack depth
PavementCoringlayer thicknesses, detection of voids and crack depth
PavementSmart phonespotholes, roughnessIncludes systems for reporting of potholes and measuring roughness through crowdsourcing
Structures and BridgeSensorsinventory, condition ratingsStrain and displacement gauges; wired or wireless,
Structures and BridgeUnmanned Aerial Vehicles (UAVs)condition of non-bridge struc- tures (e.g. retaining walls)
Structures and BridgeLiDARVertical Clearance
Structures and BridgeVisualinventory, condition ratingsCan be supplemented using UAV and other technologies
Structures and BridgeAcoustical (e.g., impact echo)delamination, corrosion
Structures and BridgeInfrared/ Thermal Imagingdelamination, corrosion
Structures and BridgeGPRconcrete deck condition
Structures and BridgeHalf Cell Potential Testconcrete deck condition
Traffic SignsVideologinventory, condition ratingsautomated or semi-automated techniques available for classification
Traffic SignsMobile LiDARinventory, condition ratings
Traffic SignsField Inspection – mobile applicationinventory, condition ratings
Traffic SignsRetroreflectometerretroreflectivity

Once data are collected, it is essential to put in place regular processes for updating the data. This can be accomplished through periodic data collection cycles, or through updating as part of asset project development and maintenance management processes.

Michigan DOT

Unmanned Aerial Vehicles (UAVs) offer several advantages for asset data collection. They can fly into confined spaces such as entrances to sewers and culverts to collect data and images. They can collect high resolution images, thermal images and LiDAR. LiDAR can be used to produce three dimensional images that allow for accurate measurements. Thermal images can be used to detect subsurface concrete deterioration.

Michigan DOT analyzed the benefits of using UAVs for bridge inspection, and concluded that using a UAV for a deck inspection of a highway bridge reduces personnel costs from $4600 to $250. A traditional inspection would take a full day and require two inspectors, and two traffic control staff to close two lanes of traffic. The same inspection using a UAV takes 2 hours and would require only a pilot and a spotter. An additional savings of $14,600 in user delay cost was estimated based on delays associated with shutting down one lane of a four lane, two way highway bridge in a metropolitan area for a bridge inspection.

Tennessee DOT

The Tennessee DOT uses an automated data collection van to collect pavement condition surveys each year in support of its pavement management system. In addition to the pavement sensors, the van also has high definition cameras and LIDAR sensors which scan the roadway and create a 3D model of the environment. As the surveys are conducted, inventory information for approximately 20 highway assets is extracted from photolog and LiDAR information. The inventory from the past data collection cycle is compared to the data collected during the current data collection cycle to determine any changes to asset inventory to keep the data up to date. Tennessee DOT summarizes this inventory data at the county level for planning and budgeting; however, they are currently working toward having the ability to report maintenance work at the asset level in the future.

Source: Federal Highway Administration (FHWA). Pending publication 2019. Handbook for Including Ancillary Assets in Transportation Asset Management Programs. FHWA-HIF-19-068. Federal Highway Administration, Washington D.C

7.2.3

Preparing for Data Collection


In order to get the most out of the data collection process, it is important for agencies to be thoughtful in the steps leading up to the actual collection of data. Three important steps to prepare for data collection include: coordinating with stakeholders, specifying exactly what data will be collected, and training staff to collect the data.


Once an organization has determined what data to collect and how to best to collect it, the next step is to prepare for data collection.

Step 1. Coordinate

An important step prior to collecting data is to coordinate with other stakeholders in the organization concerning the data collection effort. It may be possible, through such coordination, to identify opportunities for coordinating data collection activities to reduce costs. Alternatively, other stakeholders may identify needs for collecting related data to address other needs. Another possibility is that a different business unit in the organization has already collected data that may impact the data collection plan.

Step 2. Specify

In this step one must identify exactly what data will be collected, the means used to collect the data, and who will collect the data. If data collection is being outsourced, at this point it is necessary to establish contract specifications for data collection.

Also as part of this step one should establish the approach for quality assurance (QA)/quality control (QC). A QA/QC plan specifies the desired accuracy of the data to be collected, and describes the measures used to assure data are of the specific level of accuracy, review data quality as data are acquired, and address any data quality issues that arise. If data are collected using automated means, the plan should specify the approach for calibrating any measurement devices used for data collection. If data are collected through visual inspection the plan should detail training requirements.

Note that given data QA/QC is an area of particular concern for pavement condition data collection, given the expense involved in collecting this data and increased reliance on automated data collection techniques. The Federal performance management requirements described previously include a requirement for State DOTs to establish a QA/QC plan for pavement data collection.

Step 3. Contract

This step involves determining whether to outsource data collection and to contract for services if applicable. Decisions to outsource are typically made to tap into a vendor with specialized equipment and experience with a particular data collection technique, and to enable accomplishing a major collection effort within a compressed timeframe, which would not be possible using internal staff resources. Some agencies may implement a hybrid approach, hiring a contractor while using internal staff (or a separate independent contractor) for supervisory or QA functions.

TIP
Understand your audiences and the questions they are trying to answer.

Step 4. Train

The last step prior to collecting data is to train the staff involved in data collection and review in how data collection should be performed, as well as in their specific roles and responsibilities. Training is important for any data collection effort, but is particularly important in cases where the collection effort relies on visual inspection (for inspecting bridges). In these cases, the training requirements for inspectors should be carefully established and implemented. Even where there are no formal requirements for inspectors, it can be highly valuable to assemble inspectors prior to the start of data collection to review the data to be collected, walk through the data collection process, and perform inspections in a test scenario to ensure consistent interpretation of condition assessment language and other areas where differences in human judgement may impact how data are collected.

Once these steps have been performed the next step is to collect data, following the approach established in Step 2 for data collection and QA/QC.

Utah DOT

Utah DOT started capturing LiDAR data for multiple assets in 2011. Several different business units within the agency provided funding for the effort, which has included collection of inventory data for bridges, walls, signs, signals, barriers, power poles, striping, curb cuts, drainage, shoulders and ATMS devices – as well as pavement condition and roadway geometrics. UDOT has leveraged this integrated pool of asset data for several different applications, including one which creates a draft cost estimate for asset installation for project scoping, based on existing inventory.


Oregon DOT

  • Tier 1: Assets with high replacement values and substantial potential cost savings from life cycle management (e.g. pavements and bridges)
  • Tier 2: Assets that must be inventoried and assessed to meet legal obligations (e.g. ADA ramps, stormwater management features)
  • Tier 3: Assets with high to moderate likelihood and consequences of failure (e.g. traffic signals, unstable slopes, high mast lighting, sign structures)
  • Tier 4: Other assets that would benefit from a managed approach to budgeting and work planning (e.g roadside signs, pipes and drains)

Michigan DOT

Unmanned Aerial Vehicles (UAVs) offer several advantages for asset data collection. They can fly into confined spaces such as entrances to sewers and culverts to collect data and images. They can collect high resolution images, thermal images and LiDAR. LiDAR can be used to produce three dimensional images that allow for accurate measurements. Thermal images can be used to detect subsurface concrete deterioration.

Michigan DOT analyzed the benefits of using UAVs for bridge inspection, and concluded that using a UAV for a deck inspection of a highway bridge reduces personnel costs from $4600 to $250. A traditional inspection would take a full day and require two inspectors, and two traffic control staff to close two lanes of traffic. The same inspection using a UAV takes 2 hours and would require only a pilot and a spotter. An additional savings of $14,600 in user delay cost was estimated based on delays associated with shutting down one lane of a four lane, two way highway bridge in a metropolitan area for a bridge inspection.

Tennessee DOT

The Tennessee DOT uses an automated data collection van to collect pavement condition surveys each year in support of its pavement management system. In addition to the pavement sensors, the van also has high definition cameras and LIDAR sensors which scan the roadway and create a 3D model of the environment. As the surveys are conducted, inventory information for approximately 20 highway assets is extracted from photolog and LiDAR information. The inventory from the past data collection cycle is compared to the data collected during the current data collection cycle to determine any changes to asset inventory to keep the data up to date. Tennessee DOT summarizes this inventory data at the county level for planning and budgeting; however, they are currently working toward having the ability to report maintenance work at the asset level in the future.

Source: Federal Highway Administration (FHWA). Pending publication 2019. Handbook for Including Ancillary Assets in Transportation Asset Management Programs. FHWA-HIF-19-068. Federal Highway Administration, Washington D.C

Utah DOT

Utah DOT started capturing LiDAR data for multiple assets in 2011. Several different business units within the agency provided funding for the effort, which has included collection of inventory data for bridges, walls, signs, signals, barriers, power poles, striping, curb cuts, drainage, shoulders and ATMS devices – as well as pavement condition and roadway geometrics. UDOT has leveraged this integrated pool of asset data for several different applications, including one which creates a draft cost estimate for asset installation for project scoping, based on existing inventory.