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Jason Amadori's LIDAR GIS BlogArchive for pavement management
Another Cool Airport Mapping Project
We just recently completed a cool project for an airport client who was having issues with their concrete surface and “pop-outs” caused by extended freeze/thaw weather events. Pop-outs are caused when the surface of the concrete sheds pieces that are about an inch wide and can be anywhere from <1cm to 3cm deep. The following graphic shows what the pop-outs look like in the field.
The client was looking for a way to quantify the number of pop-outs per slab using an automated process to avoid having to survey every pop-out which would prove to be cost-prohibitive based on the overall size of the project.
Earth Eye deployed 2 teams of data collection vehicles to compare the imagery that could be obtained from our right-of-way cameras as well as from our pavement camera.
The pavement camera has a resolution of 1mm and gives us the ability to resolve the pop-outs from a nadir view, making it easier to automate the extraction of these features from the imagery. Also, the nadir view gives us more spatial accuracy, so the locations of the pop-outs can be accurately mapped and then compared with future imagery to help quantify the amount of new pop-outs that have arisen since the last inventory. Furthermore, the gray-scale image provided by the pavement camera provided more contrast between the concrete surface and the pop-out which is much lighter in color. It was determined that the nadir-view pavement camera provided the best starting point, from which to test the automated pop-out extraction process. The following image illustrates a sample pavement image – note the pop-outs are very visible without having to zoom into the image.
The next image shows the results of our automated classification routine without any manual augmentation of missed pop-outs. We are realizing a consistent yield of greater than 95% of pop-outs identified as compared to control slabs that were collected manually in the field. Being able to efficiently map the pop-outs with a very high-yielding and automated algorithm allows us to efficiently map the pop-outs to support maintenance operations for this airport facility.
All of the pop-outs are geospatially referenced, so we can export all of the pop-outs as polygons with an area measurement associated with them. This area can then be converted to a severity and used to prescribe a specific maintenance activity based on the size and depth of the pop-out. The goal of the project was to create a quantified measurement (count) of the pop-outs for this entire project and we successfully completed this task with high-yielding, geospatial results.
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.
Pavement Management Data Analysis
We’re just finishing up on a couple of pavement re-inspection projects. Project #1 was our 3rd re-inspection of the network and Project #2 was our second inspection of their network. Both projects are located in the Rocky Mountain region of the US and have pretty harsh freeze/thaw cycles, particularly in the past couple of winters. Our clients were mostly interested in how their network was performing over time as compared to our predictive models set up in their pavement management software.
The results are pretty interesting for Project #1 – as you can see, their network is deteriorating a lot faster these days based on a few harsh winter seasons. The Bright Green lines show the distribution of their pavement in 2010 as compared to the 2008 (Yellow) and 2005 (Red) inspections.
These differences are directly attributable to the past 3 winter seasons and their impact on their pavement infrastructure. This particular client plans on using this information to acquire additional funding for their pavement management program for the next few years to “catch up” with the maintenance on their network. The following graphic displays the current condition distribution for this client for their most recent inspection in 2010.
Most pavement prediction models utilize performance curves to predict pavement performance over time. These models hold true in the short-term, but can fluctuate based directly on weather events or other human factors such as changing traffic conditions. In some cases, re-inspection is necessary to adjust these estimates so an agency can fund its capital improvement program effectively. This was the specific intent of this client and there is no doubt why they are one of the premier places to live in because of their proactive asset management approach!
