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:

  1. Maximum Height of Encroachment Unit
  2. Average Height of Encroachment Unit
  3. Total Area (acres) of Encroachment Unit
  4. 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 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 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!