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Jason Amadori's LIDAR GIS BlogArchive for infrastructure
Automated Pavement Distress Analysis – The Final Frontier?
We have been working with some automated methods for quantifying crack measurements and have had some interesting results. How great would it be to collect pavement images, batch them on a server and have it spit out accurate crack maps that you can overlay in a GIS? The technology is here! Or, is it?
Most pavement inspections involve intricate processes where pavement experts rate segments visually, either from field visits or rating pavement images in the office. This introduces a lot of subjectivity in the rating results and typically culminates in a spreadsheet showing pavement ratings by segment. The data is then modeled using ASTM performance curves that have been built from industry proven pavement experiments.
There is no doubt that these curves are tried and true representations of how pavement performs in varying physical and environmental conditions and each project should take these factors into consideration when developing the preservation plans for an agency.
We have been working to develop a rating workflow that focuses on a combination of automated and manual processes to bridge the current gap of Quantitative and Qualitative pavement inspections. The way we are doing this is through the application of GIS to the automated rating process. Here’s how it works…
First, we begin with a pavement image from our LRIS pavement imaging system. Images are captured at a 1mm-pixel resolution and then analyzed through an automated image processing workflow.
The resulting image creates a “crack map” that identifies the type, severity and extent of the distresses on that section of pavement. The process is fully automated and handled by the computer.
Once we have the crack maps in place, we then apply a manual editing process that is GIS-centric by nature and the resulting crack map is a more accurate representation of the real-world conditions.
Once the edited crack maps are compiled, the data is exported to a GIS where the extents are calculated geospatially and then integrated with a pavement management system. This is where all of the Pavement Condition Indices (PCI) are calculated and applied to each agency’s specific pavement rating methodologies. Since the process is geospatial in nature, it is easily imported to ANY pavement management software and gives our clients the flexibility to apply any rating methodology they desire.
Of course, all agencies have a certain spending threshold and there are cases where automation is the only way to cost-effectively manage large volumes of data. We recognize this fact and are working hard to bridge the gap of available funding and high quality data.
What are you going to do with your NERC data?
So, you’ve collected your entire Transmission network using LiDAR, built your PLS-CADD models and identified your encroachments – what’s next? How about leveraging that data to manage the Work Activities required to upgrade/maintain your Transmission network?
We have all heard about Asset Management and how it can help an agency extend the useful life of its infrastructure. We all know that in principal it makes all the sense in the world, but the actual application of these concepts require investment in software, hardware and personnel. What we will never know is – How much should we invest in the management of our assets? Using the NERC regulation and the frenzied data collection going on in our industry as an example, consider the following.
Most Airborne LiDAR companies are collecting and delivering data in the $500 – $1,500 per linear mile range, depending on the downstream processing requirements. Most of this data is delivered to the end user as .LAS point clouds, PLS-CADD .BAK, files and some other CAD or GIS formats. Once it is delivered, the agency has a unique opportunity to leverage the delivered products for future value.
If we use Vegetation Encroachment data as an example, we can illustrate how the encroachment information can be used to create a vegetation Asset Class and managed throughout its life-cycle. Most likely, the data delivered to an agency will include .LAS point clouds with classified data reflecting terrain, conductors, towers, buildings, etc. In addition to this, vector data is also delivered and can be used to support maintenance management activities. The graphic below illustrates a common Transmission LiDAR deliverable.
Note the Red vegetation in the graphic above. It shows the vegetation points that have been flagged as encroachment violations based on its proximity to the conductors. These points can then be mapped in a GIS or Asset Management program for further analysis. In doing so, an agency can gather more value from this information. For example, the graphic below illustrates the “grow-in” (light blue) and “fall-in” (red) violations for a section of Transmission line.
GIS mapping provides the user the spatial context necessary to make informed vegetation management decisions. First, the location of vegetation encroachments are known and with a little manipulation, the volume and area of the vegetation can be determined very easily. This gives an agency the ability to control the costs associated with their vegetation management program. Asset management software that leverages GIS can provide the tools necessary to develop an immediate return-on-investment of the software purchase and associated data collection expenditures.
First, the user creates the geospatial layers from the classified point cloud. Vegetation violations can be exported as points and then aggregated into vegetation encroachment units. These units are then integrated with the Work and Asset management system through the use of GIS. Since the geometry of the encroachment units are known based on its GIS attributes, an agency can then determine the following characteristics about their encroachments:
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Maximum Height of Encroachment Unit
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Average Height of Encroachment Unit
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Total Area (acres) of Encroachment Unit
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Total Area (acres) of Encroachment Units along a particular circuit
Since the agency knows so much about their encroachments, they can very accurately determine the volume of vegetation that needs to be removed. The agency also knows other geospatial characteristics of the vegetation units and can then apply specific cost factors to the removal process. In addition, GIS also provides a great way to provide contractors with maps and exhibits that will help them generate more accurate bids based on relevant information. The graphic below shows a KMZ export of Vegetation Encroachments that can be provided to field units in charge of vegetation removal.
A typical vegetation removal contract is assigned to a forestry company who heads to the field and clears vegetation based on their perception of what needs to be removed. Now, agencies can tell the forestry companies exactly how much (estimated) vegetation needs to be removed and WHERE it is. Pretty amazing concept to embrace because now an agency can accurately predict the costs of their vegetation management program.
Another factor that can be applied to this information is the concept of Risk. Risk takes into consideration the consequences of failure of a particular asset and then provides a Criticality Index for specific Asset Classes and Asset Types. The more critical the Asset – the higher the priority it gets when determining an agency’s primary work focus. In other words, this concept helps to identify the most critical components of your infrastructure and helps you to prioritize its maintenance over less critical assets. By prioritizing using Risk, an agency can take measures to minimize the Risk that exists in its Asset portfolio by fixing these pieces and parts first.
None of this stops once you get to the Work Management piece of the puzzle. I’ll be providing more information related to tracking the work activities as they are completed in the field and using this information to develop more accurate budget forecasts for the future.
Mobile LiDAR to Support Positive Train Control
DTS/Earth Eye just completed a positive train control (PTC) project for a national train company who was evaluating the differences between Airborne LiDAR and Mobile LiDAR to support the collection of PTC data. They are currently collecting airborne data for approximately 15,000 linear miles of rail. In certain areas, the airborne data does not provide enough fidelity to accurately map the rails or the asset infrastructure that support the railroad operations.
From Wikipedia – “The main concept in PTC (as defined for North American Class I freight railroads) is that the train receives information about its location and where it is allowed to safely travel, also known as movement authorities. Equipment on board the train then enforces this, preventing unsafe movement. PTC systems will work in either dark territory or signaled territory and often use GPS navigation to track train movements. The Federal Railroad Administration has listed among its goals, “To deploy the Nationwide Differential Global Positioning System (NDGPS) as a nationwide, uniform, and continuous positioning system, suitable for train control.”
The project involved the collection of Mobile LiDAR using the Riegl VMX-250 as well as forward-facing video to support PTC Asset Extraction. The system was mounted on a Hi-Rail vehicle and track access was coordinated through the master scheduler with the Railroad company. Once we had access to the tracks, we had one shot to make sure the data was collected accurately and we had complete coverage. All data was processed on-site to verify coverage and we had a preliminary solution by the end of the day that was checked against control to verify absolute accuracies. We collected the 10-mile section of rail in about 2 hours and this timing included a couple of track dismounts required to let some freight trains move on through.
The following graphics illustrate the point cloud coverage colored by elevation (left) and Intensity (right).
Mapping the rails in 3D was accomplished by developing a software routine designed to track the top of the rail and minimize any “jumping” that can occur in the noise of the LiDAR data. Basically, a linear smoothing algorithm is applied to the rail breakline and once it is digitized the algorithm fits it to the top of the rail. The following graphic illustrates how this is accomplished – the white cross-hairs on the top of the rail correspond to the breakline location in 3D.
So, back to the discussion about Airborne PTC vs Mobile PTC data. Here is a signal tower collected by Airborne LiDAR. The level of detail needed to map and code the Asset feature is lacking, making it difficult to collect PTC information efficiently without supplemental information.
The next graphic shows the detail of the same Asset feature from the mobile LiDAR data. It is much easier to identify the Asset feature and Type from the point cloud. In addition to placing locations for the Asset feature, we also provided some attribute information that was augmented by the Right-of-Way camera imagery. By utilizing this data fusion technique, we can provide the rail company with an accurate and comprehensive PTC database.
This graphic shows how the assets are placed in 3D, preserving the geospatial nature of the data in 3D which is helpful when determining the hierarchy of Assets that share the same structure.
One last cool shot of a station with all of the furniture, structures, etc that make it up – pretty cool!
Mobile LiDAR and Cross-Slope Analysis
DTS/EarthEye just completed a 9-mile mobile LiDAR scan of I-95 here in Florida and provided one of our partners with cross-slope information in a period of days. The data was collected with our buddies at Riegl USA using their VMX-250 mobile LiDAR. This information will be used to generate pavement resurfacing plans for the Florida Department of Transportation (FDOT).
This project shows the value that this type of project can provide to the end user on both sides of the fence.
First, the paving contractor can use this data to develop their 30% plans for submittal to FDOT when bidding on a resurfacing or re-design contract. Having accurate and relevant data related to the roadway’s characteristics gives the paving contractor an edge over the competition because they know what the field conditions are before preparing an over-engineered design specification. This happens all of the time because the detailed field conditions are unknown while they are preparing their plans and they only have historical information to work from.
On the other side of the fence resides the FDOT. They can benefit from this information because if they can provide this detailed information as part of a bid package, they can reap the benefits that are gained from better information. If all contractors have the detailed as-built information (or in this case, accurate cross-slopes), they can all prepare their submittals using the same base information. This will provide the FDOT project manager with more accurate responses based on true field conditions, resulting in more aggressive pricing and decreased project costs.
Here are some screenshots of the information.
LiDAR Data Viewed by Intensity and Corresponding Cross-Slope Profile
Once the data has been collected and calibrated, we generate cross-slopes at a defined interval and export those out as 3D vectors.
These vectors are then symbolized based on their cross-slope percentages and exported as a KML file for ease of use.
Although this is a pretty simple step, the presentation of the data in Google Earth makes it easy for the end-user to visually identify problem areas and design the corrective actions according to field measurements.
Pavement Management for Cities/Counties
I just spent a great week in Klamath Falls, OR training their staff on pavement management techniques with our Engineer, Will Cook. We deployed an asset management solution for their Pavement, Curb & Gutter, and Sign infrastructure. We were able to determine the amount of funding necessary to keep their pavement network at steady-state and how fast it is deteriorating at their current funding levels.
Believe it or not, most agencies have no idea what they own, what it is worth, and how much funding they really need to maintain it at a specific level-of-service.
Inventory
It all begins with a Network-Level inventory of everything which gives us an idea of what they own.
Condition Assessment
Then, we need to know what its condition is. We use this data to help prioritize assets for repair and rehabilitation.
Budgeting
Once the prioritization is complete, we apply budgeting scenarios to determine what gets fixed and when. This leads into Capital Improvement Plan (CIP) development which evolves into an agency’s work program.
This discussion makes it all sounds easy, but at this point, it is imperative to have the local subject-matter-experts make real-world decisions on what to really do. We’ll never rely completely on computer modeling to make these decisions, but they do help with a lot of the heavy lifting involved with managing large asset networks.
I will be posting a few articles next week detailing this process with some case studies of clients who I have worked with in the past…Stay tuned!
