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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Who is Checking Your LiDAR Data?

Throughout the years, I have seen many projects advertised, awarded, executed and then delivered to the client. The client receives the data, copies it locally and then final payment is made to the vendor and life goes on as usual. Then, someone actually checks the data and notices that there are many discrepancies associated with the scope of work and what was actually delivered. How does this happen and how can it be avoided?

Step 1 – Start with a Clear Scope of Work

The scope should define exactly what is going to be collected, how it will be collected and how it will be verified and checked after delivery. For example, a simple LiDAR scope must define the target point densities (LiDAR), hydro-flattening parameters, and accuracies (absolute and relative) for the project. The scope should also define how the client will be checking the data for final acceptance of the deliverables.

Step 2 – Process a Pilot Area

The pilot area should be representative of the overall project and should be processed and delivered as if it was its own project. This allows for the team to identify any processing issues or special techniques up-front so that the rest of the project can move forward in a linear fashion, thus limiting the re-visiting of the data to fix problems at a later date. Once the pilot area is delivered, it should be checked against the scope of work to ensure that all deliverables are being met in accordance with the client’s expectations.

Step 3 – Process the Entire Project

Final processing can occur once the pilot area is collected and accepted. This is a critical-path item that is the bulk of the project’s budget. Many projects will either be successful or a turn into a disaster during this phase. The risk is easily mitigated, though, as long as the first two steps of this process are in place and properly executed by the team. This is very reliant on communication between the vendor and the client and if these channels are in place, the project will most likely run smoothly since everyone is on the same page.

Step 4 – Data Validation and QA/QC

This is where the overall success of a project is either validated or issues are identified that must be resolved before final delivery is accepted. The processes for checking these data sets are specific for different type of deliverables – we will focus on some niche market deliverables and give examples of how to check their associated data elements.

LiDAR QA/QC

First off – make sure you have some kind of software that can open this data. Seems simple, but many clients do not have the most rudimentary piece of the puzzle – LiDAR viewing software. There are many commercial-off-the-shelf (COTS) products that can be used and each one has its strengths and weaknesses. The goal is to be able to load the entire project in one place and then use the tools within the software to verify the deliverables. The most important items to check include:

· Average Point Density across the project

· Relative (flight line to flight line) accuracies – this should be half of the stated RMSE for the project (e.g. 5cm for a 9.25cm RMSEz spec or 7.5cm for a 15cm RMSEz spec.)

· Absolute (overall project) accuracies against ground control. Ground control should be on a hard surface and un-obscured and is typically tested to a 95% absolute accuracy specification). A minimum of 20 points is required, since one point out of 20 will get you to the 95% specification. Larger areas can require significantly more control.

· Data classifications (e.g. Ground, Vegetation, Overlap Points, Low Point/Noise, etc.) as per the project specifications (ASPRS or USGS publishes these specifications).

· Check terrain edits (look for berms that are removed, building points in ground, low point noise and other anomalous data in the wrong classes).

· Projection information in the LAS file header.

· Verify Intensity TIFFs as per user-specified requirements.

· If breaklines are required, check the following:

          o Water bodies meet minimum size criteria,

          o Interior points classified to water class and

          o Client-specified buffers around these features

          o Single drains (streams) meet minimum length and width requirements and are buffered as per client specifications.

          o Double-line drains (Rivers) are monotonic (perpendicular elevations to remove leaning) and are buffered as per client specifications.

          o For all breaklines – check elevations are at or slightly below terrain for a sampling of tiles for the project (typically 10% of project).

· Review the survey report

· Flightline trajectories with appropriate metadata, flight logs, and other raw data collection activities (GPS, inertial, etc).

· Metadata for all project deliverables (this can be automated with a metadata parser).

In conclusion, it is important to check your data immediately upon receipt, so that all quality control and quality assurance activities can be performed and verified while the data is still relevant. Good luck!

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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Sign Retroreflectivity Compliance – A Geospatial Approach

We just completed a sign retroreflectivity shortlist presentation for the a client and discussed the options available for gaining compliance based on FHWA regulations as described in the MUTCD.  The client was sold on the “Blanket Replacement” method by a vendor who specializes in sign replacement.

MUTCD Retroreflectivity Guidelines

I was thinking “what a great selling strategy”, but then I thought twice about it.  This vendor had the ability to write their own ticket for selling their sign materials!  A great strategy for the vendor, but not a good option for the client.

We approached the presentation using a different approach – it combined the concept of risk with the general principles of Asset Management.  First, we would inventory their existing sign network to determine what they had and where it was.  Then, we would prioritize which areas were the most likely to fail based on the average age of the signs as well as the risk associated with the actual failure (e.g. pedestrian injury or vehicle damage due to an accident).

 

Risk Assessment for Signs

 

Sample Replacement Cost Calculation

This approach takes into consideration the entire segment of a road instead of considering an individual asset.  The client believes that it is more cost effective to replace the worst signs along a segment using a single mobilization of field crews, rather than jumping around and fixing signs based solely on their condition.  Therefore, we are combining the geospatial location, condition, age, value and MUTCD to develop a risk score for each individual sign.

Project Life Cycle

This analysis is used to create the biggest bang for the buck for our client by reducing risk related to accidents caused by failing signs.  Since all agencies have to be compliant with Regulatory, Guide and Warning signs by 2015, this approach will support a phased approach while taking care of the highest risk signs and working through the lower risk signs until all non-compliant signs have been replaced or are scheduled for replacement.

Compliance Dates for Sign Retroreflectivity

 

Valuation of Sign Asset

In conclusion, the use of Risk to support the prioritization of asset maintenance serves an appropriate role in saving clients time and money.  By replacing the highest risk assets first, an agency can reduce their exposure to lawsuits related to failing infrastructure.

Executive Dashboard

URISA Caribbean Conference in Trinidad

I am here for 3 days supporting the URISA Caribbean conference and presented to a group of Utilities who were interested in Asset Management of their electrical infrastructure.  It was interesting to hear that they have the same issues that we encounter in the US related to infrastructure preservation funding – Limited budgets, underfunded programs and failing infrastructure.

We always do our best to stay “realistic” when it comes to Asset Management.  It has not been a priority in the past and will not be one in the future – until things start failing.  The funding will never be there in the amount that it is needed, so what do you do then?

Start Small…

We had a long discussion in this workshop about starting small and working your way into a larger AMS implementation.  By starting small, you can increase the probability of success of your project and “correct the path of the ship” if something was missed or overlooked.  Select one Asset Type and focus on building it into a comprehensive database of information.  By selecting one Asset Type, you can limit the amount of effort and risk it will take to build it into your AMS.

Show it Off…

Once you have some good asset data, show it off to everyone and make sure the decision-makers understand that it is available for use.  Build charts, graphs and reports about the Asset and build a plan around getting that Asset into the work cycle for maintenance and operations.  One of the most important things I recommend is to identify the “Worst” Assets and get them fixed immediately.  These typically create liability for your agency and if left unattended, could cost more than the AMS itself.

Make it Indispensable

Once the AMS is used to make decisions and answer questions related to infrastructure, it will become the “Go To” system that your agency will use moving forward.  At that time, you will be able to show the value of the system and gain leverage in terms of future funding, new assets, etc.  At this point, no one would make the call to shut this system down because of the value it adds to everyday decision-making.

Those are some recommendations that we discussed this week – looking forward to 2 more days of collaboration and learning about Trinidad’s Utility industry!

Mobile / Bathymetric Data Fusion

When we were at the ILMF conference, I had someone stop by and ask us about fusing Mobile LiDAR and Bathymetric data.  So, I asked him for an XYZ file and we made the import into EarthView.  The data looked great calibration-wise and the data sets seemed to line up pretty well from a high level.  His main concern was related to data editing portion of the project.

As with any LiDAR project – it is pretty easy to collect and calibrate the data, but making it useful for analysis is the hard part!  The Bathy portion of this project had a lot of noise in the point cloud and required some re-classification for sure as shown in the graphic below.  The Blue data is the Mobile data and the Green data is the Bathy data – each is colored to its corresponding point cloud.

Mobile and Bathymetric Data by Line

The Floating blue points are mostly the water surface, but some of it is floating above the surface and needs to edited out of the point cloud.  We handle this by using our editing tools to re-class those points into the “Water” class so that it has a home in the point cloud.  The next graphic shows how we can re-class those points with editing and the resulting “Hole” in the point cloud where the Water class has been turned off.

Mobile/Bathy Water Edits

Please note that all of the data hasn’t been edited for this demo, just a subset to show the editing tools.

Here is a profile view of some boats parked in their slips – this shows above-ground features and underwater features simultaneously.

Boat Slips Above and Below Water Line

Once again, this is all “cool” in terms of pictures, but there is a lot of noise in the data that needs to be hand-edited before a true surface can be created with the data.  We’re working this data as we speak and I’ll post more about it when we’re finished editing!

I’m back…

I have taken some time to get advice from my buds in Ft. Collins (Dave Bouwman and Brian Noyle) to get some blog advice.  They got me in touch with Nick Armstrong to give my blog a face-lift and give me some street-cred in the blogosphere!  Thanks gents!