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 Class | Data Collection Method | Data Collected | Notes |
|---|---|---|---|
| Pavement | Visual Inspection | Present 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 |
| Pavement | Automated data collection vehicle with laser scanning system | roughness, cracking, nutting | Includes 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 |
| Pavement | Light Detections and Ranging (LiDAR)/ Terrestrial Laser Scanning (TLS) | roughness, cracking, nutting | Provides a high resolution continuous pavement survey. Often inventory data for other assets can be extracted from the data set |
| Pavement | Falling weight deflectometer | strength/deflection | |
| Pavement | Locked wheel tester/spin up tester | skid resistance | |
| Pavement | Ground Penetrating Radar (GPR) | layer thicknesses, detection of voids and crack depth | |
| Pavement | Coring | layer thicknesses, detection of voids and crack depth | |
| Pavement | Smart phones | potholes, roughness | Includes systems for reporting of potholes and measuring roughness through crowdsourcing |
| Structures and Bridge | Sensors | inventory, condition ratings | Strain and displacement gauges; wired or wireless, |
| Structures and Bridge | Unmanned Aerial Vehicles (UAVs) | condition of non-bridge struc- tures (e.g. retaining walls) | |
| Structures and Bridge | LiDAR | Vertical Clearance | |
| Structures and Bridge | Visual | inventory, condition ratings | Can be supplemented using UAV and other technologies |
| Structures and Bridge | Acoustical (e.g., impact echo) | delamination, corrosion | |
| Structures and Bridge | Infrared/ Thermal Imaging | delamination, corrosion | |
| Structures and Bridge | GPR | concrete deck condition | |
| Structures and Bridge | Half Cell Potential Test | concrete deck condition | |
| Traffic Signs | Videolog | inventory, condition ratings | automated or semi-automated techniques available for classification |
| Traffic Signs | Mobile LiDAR | inventory, condition ratings | |
| Traffic Signs | Field Inspection – mobile application | inventory, condition ratings | |
| Traffic Signs | Retroreflectometer | retroreflectivity |
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.
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.