MAP-21 Compliance for State DOTs – Risk-Based Prioritization using VUEWorks

MAP-21 addresses all things related to federal funding and oversight of our nation’s surface transportation and transit systems. The 581 pages of the act are broken down into eight major divisions. These divisions are further delineated into titles and subtitles. Although MAP-21 deals with numerous subjects from national freight policies to how transit funding is calculated for metropolitan planning organizations, our focus today is the portion that addresses how and to what extent State DOTs must proactively manage road and bridge networks through the use of risk-based asset management planning.

The FHWA has developed a proposed rule focused on clarifying and enacting the provisions of Section 1106. Section 1106, which requires a Risk-based Asset Management System, is influenced by Section 1203(a), which establishes national standards for performance management, targets and metrics. These performance measures are intended to provide standards for the inspection of infrastructure assets, pavement rating and maintenance for the National Highway System (NHS) non-Interstate pavements and NHS bridges. Section 1106 is also influenced by Section 1315(b), which requires State DOTs to conduct statewide evaluations to determine reasonable actions or corrections that can be taken on a project basis to alleviate the need for repeated repair or reconstruction of roads, highways or bridges that frequently require attention after an emergency event (i.e. weather event).

As part of the Asset Management Plan, the Notice of Proposed Rulemaking (NPRM) has outlined the following process for State DOTs to use in the development of their Asset Management Plans. This process will need to be documented and discussed in each State DOT’s initial submittal of the plan to the FHWA for program certification.

The State DOT will establish a process for conducting a statewide performance gap analysis of the state’s Interstate and National Highway System (NHS) road assets. The process must also address strategies for closing any identified gaps. A performance gap analysis identifies deficiencies in the areas of asset condition, capacity, design or travel safety that are below the desired system performance level for those assets on the NHS as established by the State DOT.

The following graphic illustrates how VUEWorks can provide multiple Budget Forecasting scenarios can be run against an Asset Class (Pavement, Bridges, Stormwater, etc.) to determine the level of funding required to maintain the system in a state of good repair.  The scenario can be run to see what funding is required as well as what existing funding will accomplish for the DOT’s pursuit to achieve a specific level-of-service (state of good repair).

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The State DOT will establish a process for conducting life-cycle cost analysis (LCCA) for the different asset classes that collectively make up the network in order to develop a Strategic Treatment Plan (STP) for the life of each asset – from the current state of the asset until its ultimate reconstruction, replacement or disposal. A Strategic Treatment Plan looks at all possible treatments over the life of an asset to keep the asset at a performance level that is cost-effective and does not compromise the network’s capacity, safety or long-term life-cycle cost.

As illustrated below, VUEWorks  can be utilized to develop a strategic treatment program for the life-cycle of an asset.  The current deterioration model and condition score for an asset can be compared to its projected life-cycle based on the results of each scenario.  Specific preservation or rehabilitation techniques can be specified to achieve a state of good repair.

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imageThe State DOT will establish a process for assessing risk related to a given NHS asset that could impact that asset’s physical condition, capacity or performance in emergencies or over the long-term. Risks to an asset’s physical condition or its ability to perform can include one or more factors including extreme weather and climate change, seismic activity, traffic volume, traffic loads, sub-par construction materials, time between treatments, etc. As part of the State’s Risk-based Asset Management Plan, the State DOT will be expected to develop an approach to monitor, measure and report on high-priority risks to an asset’s or network’s performance.

Here is an example of a true Risk matrix based on the requirements of MAP-21.  This matrix is reading information from multiple data sources (Linked Data, GIS data and Condition Data) that is tracking each Risk category against each section of road.  The matrix displays each individual category of Risk, ranks it on a scale from 0-10 and then summarizes the Road network as a whole for the DOT.

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  • Failure Modes
    • Age
    • Distresses
    • Deflection
    • Ride Quality
    • Rutting 
    • Work Orders/History
  • Consequences of Failure
    • Travel Delays
    • Rough Roads
    • Traveler Safety
    • Recovery Cost
    • Air Pollution
    • Traffic Congestion
    • Risk of Accidents
    • Traveler Fatalities
    • Climate Disturbance
    • Freight Delays

The State DOT will establish a process for developing, managing and updating a 10-year financial plan for the construction, maintenance, repair, rehabilitation, reconstruction or disposal of assets in the NHS. The process must allow the State to determine the estimated cost of future work based on the Strategic Treatment Plan (discussed in Item 2 above) and the estimated available budgets.

Budget scenarios can be run against any Asset for any planning horizon to establish a financial plan for each Asset Class and Asset Type.  Different strategies can be employed for each asset to identify the most effective maintenance, preservation or rehabilitation plan for the asset based on the best practices employed by the DOT.

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The State DOT will establish a process for identifying viable investment strategies for funding long-term operations. This is to ensure that assets along the NHS are maintained at a level that will help the State DOT achieve asset condition and performance targets in alignment with the national goals set forth under United States Code.

VUEWorks provides the ability to run budget scenarios for each Asset Class and Asset Type to determine the best investment strategies for the Asset’s Life-cycle cost.  Target Deteriorations can be set for the Asset Network (Pavement , Bridge, etc.) and VUEWorks will identify the Target Deterioration that can be achieved or the Funding Strategy required to achieve these goals.

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The State DOT will use a Pavement Management System (PMS) and a Bridge Management System (BMS) to analyze the condition of Interstate and NHS pavements and bridges to develop, manage and monitor targeted investment strategies.

VUEWorks provides a single, Enterprise Asset Management Solution for State DOTs.  Any asset can be managed within VUEWorks and the guiding principals of MAP-21 can be implemented as part of the DOTs day-to-day Asset Management activities.

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Visualizing Pavement Distress–The Complete Story of Pavement Inspection

Pavement management incorporates data collected utilizing various methods to gain a complete view of how the pavement is performing through its life-cycle.  One of the most common practices in pavement inspection is imaging utilizing high-resolution cameras mounted on vehicles outfitted with precision GPS and inertial navigation.  This imaging, when combined with laser profiling, constitutes a typical pavement inspection setup utilized by many DOTs as well as Local government agencies.

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Pavement Inspections tend to follow a process that in many cases is proprietary and “black box” in nature.  This makes it hard for the purchasing agency to see how their roads were inspected and how the resulting pavement condition scores were generated.  Our team of Engineers and GIS professionals have worked hard to develop a process to remove the “black box” related pavement inspection and to make it easy and simple to trace inspection results back to their originating distresses from the field.

First, our entire process is geospatial in nature from the get-go.  Our van’s location is tracked in six-dimensions in real-time and this information is used to calculate the exact location of pavement cracks in the resulting images.  Next, the pavement images are geospatially referenced in 3-d and 1mm-pixel resolution, making it easy to extract low-severity cracks in a true 3-d environment.  This process then allows us to create GIS vectors (points, lines and polygons) of each distress for each pavement image and deliver them to our clients as part of the pavement inspection deliverables.

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This is a crucial piece to the pavement inspection “story” because it shows the purchasing agency exactly what distresses were identified and measured when creating the pavement condition scores for a section of road.  Being able to see these distresses on a map helps to complete the story by providing the ability for a rigorous QA/QC process utilizing some simple GIS tools.

Each Section of road can be colored by the condition score and its range of values.  This tells one component of its story.  The underlying distress information tells the rest of the story related to “How” a section of road was scored and assigned its inspection score.  By having this information at their fingertips, pavement inspection personnel have a GIS-centric and user-friendly tool that allows them to QA/QC pavement inspection data efficiently.

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Utilizing Mobile LiDAR to Support Pavement Resurfacing

Many Departments of Transportation are looking for ways to save money while increasing safety on the roads. In order to do this, they are seeking out innovative ways to do this while utilizing new technology. Mobile LiDAR is being used to determine roadway geometry information for long stretches of roadways that are candidates for resurfacing. The typical DOT procurement process involves the selection of a resurfacing vendor through a competitive bid solicitation and then the selection of the most qualified and “cost-effective” bidder. As budgets have become leaner, the competition for these projects has increased and thus, drives the innovation curve to find the most cost-effective solution for the DOT.

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To achieve this goal, pavement vendors have sometimes turned to the use of LiDAR information to develop their bid packages for the DOT. Historically, vendors would use the as-built information that was available from the DOT which might be inaccurate, old or obsolete. This obviously leads to issues with the information that the pavement vendor uses to develop their bid packages. They are most interested in determining the correct amount of cut/fill needed to resurface the road while using the least amount of new material. One of the most important pieces of this puzzle relates to the cross-slop of the road which facilitates roadway drainage and ultimately makes a road safer for the traveling public.

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Mobile LiDAR provides a high-precision, digital terrain model of the roadway surface that can be used to generate very accurate cross-slope measurements at specific intervals. For example, the road surface is continuous for the entire length of the project. Cross-Slopes can be generated for each travel lane as well as for the shoulders. The extracted cross-slope is then compared to the design specification and colored based on whether it is in compliance or out of compliance.

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Once the areas have been identified that are out of compliance, it is easy for the pavement vendor to target those for the re-design effort. Instead of applying an average value across the entire section of road, specific areas can be identified and re-designed so that the pavement vendor can save the DOT money on materials. The ultimate benefit for both the pavement vendor and the DOT lies in the fact that everyone benefits – Pavement vendors can design roads more accurately and limit their risk of material over-runs while the DOT can select the most cost-effective vendor and have more budget available to pave their ever-increasing network mileage of roads.

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Since mobile LiDAR data is very cumbersome to manage (2Gb/mile) it is important to deliver the data in a format that is usable by the client. Sometimes raw LAS files work and sometimes the client can only deal with vector files that will be used in GIS, Autocad or Microstation, to name a few. We have found that KMZ files are useful as a delivery mechanism because they can be easily loaded and viewed by the client in very short order. Any derivative of these delivery mechanisms will work – it just depends on the expertise of the client and their computing environment.

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Future discussions will focus on the DOTs and their collection of mobile LiDAR data so that they can provide it to all of the pavement vendors and receive the most cost-effective bid packages. Although there is an up-front cost associated with the LiDAR collection, it is believed that the downstream cost savings for both the DOT and the pavement vendor will more than outweigh the up-front cost of collecting the mobile LiDAR data.

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

Chasing Cracks…

Not the same crack that is in the news, but it is pretty addicting…

We’ve built a bunch of new tools centered on pavement crack assessment and we’re excited about how it will increase the transparency related to pavement assessments.  In the past, pavement assessments have been more about delivering segments with PCI values attached to them and less about the actual measurements that were used during the creation of this data.

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Our clients are always quick to say “We went out and checked a few segments and our assessments were different than what was reported”.  This lead to an educational discussion about how the ratings were created and how we applied the ASTM methodology to arrive at these results.  Most of the time we all agreed that there was always some subjectivity in the ratings, but that the standard rating methodology had been applied the same way throughout the network.

Our goal has always been to increase the transparency related to pavement inspections and this new approach has helped us to take a step in that direction.  The process is GIS-centric, as it is with all of our processes and involved a ton of tool development that will continue to evolve over time.  So, here’s what we’re doing…

First, we are collecting crack images using a downward-facing 4k linescan camera system with laser illumination.  This ensures that all of the pavement images are uniform and are not subject to low-lighting or shadows from natural and man-made features.  These images are 1mm resolution, allowing us to see the detailed cracking – especially at the lowest severity levels.

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The following graphic illustrates the output from the crack mapping software we are using.  Cracks are identified in the imagery automatically from the software and are exported as geospatial points, lines and polygons.

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The software does a great job of identifying longitudinal, transverse, and alligator cracking.  Once we have the initial crack map, our team of compilers goes in and edits the crack maps as needed.  Typically, we are editing out false-positives and adding in other distresses as dictated by the scope of work.  This editing is done within our EarthView software and is completely geospatial in nature.  In other words, we can export these cracks, so they can be viewed in a GIS.  This is pretty exciting because all of these cracks can be mapped and themed in a GIS based on their severity levels.

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This process gives the end user of the data a simple QA/QC process that can be used to understand the specific issues related to each segment.  Furthermore, this data is then combined with other GIS data sets (Functional Classification, Traffic Counts, etc.) so that a more holistic approach can be taken towards the determination of which segments need in terms of repair methods.  This data can also be exported to Google Earth for easy viewing and display in a non-GIS software.

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We hope that this provides the end user with more tools in their GIS arsenal to better plan, bid, and execute their Capital Improvement Planning for the year.  In other words, our clients will be able to do more with their limited funding than ever before!

Mobile LiDAR to Support Roadway Resurfacing

We just completed a mobile LiDAR project that was designed to support a roadway resurfacing project in Orlando.  The project was centered on the use of mobile LiDAR to generate roadway profile data that Engineers could use to design a resurfacing project.  Obviously the data would need to be accurate and we were able to hit the mark and best of all – prove it!

Overview of SR417 Project

We collected the data using the new Riegl VMX-250 mobile LiDAR unit using a single pass in the north and southbound directions.  We only required one pass in each direction to collect the road data which makes it very efficient from the data collection standpoint.  In the past, we had to collect “strips” of data and then “sew” them all together during the calibration process.  In this case, we took the opposing (NB and SB) strips and calibrated them relative to one another and then they are brought down to control as a final step.

LiDAR Coverage by Flight Line

Most of our clients are interested in the overall accuracies of the data, so we have built accuracy assessment tools that make it easy to review the LiDAR against survey control.  The tool is simple to use and allows us to sort the results and dig deeper into the least accurate points to see why there might be discrepancies in the control vs the TIN surface.

417 Accuracy Control Report

For this project, we achieved an RMSE of .0525 ft – calculated by comparing the control elevations (Z) against the TIN elevations (Z TIN).  This is important because we can check the point cloud against known control that was collected throughout the project and provide detailed information about the accuracy of the data.

Once the data has been calibrated sufficiently, we can then generate all of the derivative products for this project.  We generated the following data for our roadway engineers:

  • Pavement Cross-Slope
  • Shoulder Cross-Slope
  • 3D Roadway Markings
  • Edge of Friction Course

3D Vector Data

This data set also supports detailed engineering analysis related to guardrail height above the roadway.  This is an important factor to consider because there are specific standards that define where the guardrail is placed, more specifically, its height above the roadway, to corral vehicles that end up impacting the guardrail in an accident situation.  The following graphic displays how this measurement can be made in the point cloud data.

Guardrail Height Measurements