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Jason Amadori's LIDAR GIS BlogArchive for budget forecasting
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.
Risk Analysis for Asset Management
One of the pet-peeves I have with asset management software is that they are basically Access on steroids. There’s not much to the inner workings of the software other than showing you information about an asset – its location, street name, type and maybe some historical information. Once you get past the attributes, the applications get very complicated because they need to handle some business logic and one-to-many relationships, etc. The list of attributes can be exhaustive, but how much of it is useful when making a decision about what to do to an asset with limited funding.
I’m working with a vendor who understands this relationship and the role it plays in prioritizing asset rehabilitation and we are getting some promising results. Imagine trying to prioritize which roads you will be resurfacing next year based on their condition alone. The worse condition they are in, the higher priority they will receive when you are ranking them based on condition alone.
What if you added risk analysis into the mix?
Then, you can ask these questions of your data – If this road fails, what kind of impact will it have on the travelling public? Does the road carry large volumes of traffic, or is it in the boonies?
If you can answer these questions, you add another level of intelligence to your data. Start incorporating traffic volumes, functional classification, detour and access constraints into your model and you’re on your way to prioritizing with intelligence.
