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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Roadway Characteristics Inventory for DOTs Using Mobile LiDAR Technology

Roadway Characteristics Inventory for DOTs Using Mobile LiDAR Technology

DOTs across the Country are mandated by the Federal Government to keep track of their roadway assets and to report against these assets to receive Federal funding for their maintenance and repair. Many DOTs conduct Roadway Characteristics Inventories (RCI) on an annual basis to update and maintain their data relative to these assets. Traditionally, this has been completed using a boots-on-the-ground approach which has been very effective at building these inventories. Many DOTs are experimenting with other technologies, namely mobile LiDAR, to conduct these inventories and to achieve many other benefits from the 3D data captured in the process.

The next graphic illustrates the typical technology solution utilized for these projects. It is composed of the Riegl VMX-450 LiDAR unit, coupled with High-definition Right-of-Way (ROW) imagery. This system can collect at rates up to 1.1 KHz (1,100,000 pts/sec) at a precision of 5mm. It collects points in a circular (360-degree) pattern along the right-of-way from 2 scanner heads facing forward and to the rear of the vehicle in a crossing pattern. The laser captures 3D points at a density of 0.3 foot at speeds up to 70mph. This scanner can be adjusted to scan at a rate that is applicable for the project specifications to limit the amount of data collected and to ensure that the resulting point cloud data is manageable.

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Right-of-Way imagery is also co-collected along with this LiDAR point cloud data. These images are used to identify appropriate attribution for each feature type being extracted from the point cloud. In this example, the DOT has digitized Shoulder, Driveway Culvert Ends, and Drainage Features (Culverts, Ditches and Bottom of Swale). Additional Features such as Signs, Signals, Striping, and Markings will also be extracted and then reported to the Feds on an annual basis. The mobile LiDAR data provides a 3D surface from which to compile the data and then the ROW imagery can be used for contextual purposes to support attribution. This methodology provides an effective process that can be used to create 3D vector layers and accurate attribution used to build a robust Enterprise GIS.

Both the ROW imagery and the mobile LiDAR can be used to collect and extract the RCI data efficiently for the DOTs and provides the DOT with a robust data set that can be leveraged into the future. The ROW imagery is typically used to map features at a mapping-grade level while the LiDAR can vary a bit in accuracy. Since the relative accuracy inherent in the LiDAR is very precise, it is used to conduct dimensional measurements related to clearances, sign panel sizes, lane widths, and other measurements that require a higher precision.

The DOT utilizes the derivative products from this RCI exercise to report to the Feds in a way that is pretty basic, but effective to achieve their level of funding. For example, the data capture is very technical in nature and focuses on high precision and accuracy. Then, the RCI data is extracted from this source data, maintaining a level of precision that is dictated by the source data. Then, the DOT takes this precise data and aggregates it up to a higher level and reports the total number of Signs or the lineal feet of guardrail. Even though the reporting of this data is pretty basic in nature, the origins of the data can still have precision and accuracy and can be used for other purposes related to Engineering Design or Asset Management.

In conclusion, mobile LiDAR and Right-of-Way imagery are a safe and accurate way to collect and report against RCI variables for DOTs. This methodology promotes a safe working environment for both the DOT worker and the traveling public. It is also a cost-effective way to collect large amounts of 3D point cloud data which can be utilized for other purposes within the same Agency.

Sign Retroreflectivity Compliance and Asset Management

Over the past few years, there have been many projects designed to determine an agency’s sign retroreflectivity compliance across their road network. Each project has been unique in terms of how the agency collected the data and how they ultimately managed the data into the future. Recent MUTCD regulations require the development of an inventory management program that documents the installation, maintenance and construction characteristics of sign infrastructure. Many agencies are faced with the daunting task of funding a replacement program that will comply with these new regulations into the future. Ultimately, the replacement plan needs to address non-compliance issues that are identified during the inventory/inspection process.

Step 1 – Sign Inventory

The first step in the compliance process begins with an accurate inventory. Signs can be collected utilizing many different techniques and each technique can have its pluses and minuses. Field collection programs can involve inspectors walking the roads, mobile imaging vehicles taking pictures of the roads as well as other collection techniques designed to identify compliance issues along the road. No matter which solution is selected, it needs to satisfy the overall goals and objectives of the project while providing an accurate inventory of the agency’s sign infrastructure.

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Next, an agency needs to be able to match their available funding to the technology solution that achieves their project goals and objectives. It also needs to understand the trade-offs that are the necessary evil in projects like this – available funding typically dictates the quality of the solution that can be provided by the service provider. Furthermore, the quality of the data collected and its usefulness can be impacted by the choice of the solution and available funding.

Remember that the ultimate goal of retroreflectivity compliance is centered on the replacement of signs once they fall below the minimum reflectivity standard as defined by FHWA. Many agencies would rather start replacing signs today instead of spending money to create their inventory and a management plan. This makes sense economically in the short-term, but can introduce problems from a long-term management perspective.

Step 2 – Estimating the Replacement Cost of the Sign Network

The next graphic illustrates the total replacement cost as calculated using the FHWA “Sign Retroreflectivity Guidebook” for an agency with a 4,383 centerline mile road network.

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The cost to replace all signs for this agency approaches $17.5 million dollars. Please note that this does not include the cost of the labor, equipment and other material costs incurred for the actual installation of these signs. The inventory of signs for this agency cost approximately $800k or roughly 5% of the total replacement cost for these signs. Although significant, this investment is crucial to ensure the longevity of the Sign Management program designed to manage these assets throughout their life-cycle.

Step 3 – Choosing a FHWA-Approved Sign Management Methodology

The chart below illustrates the advantages and disadvantages related to a few of the FHWA-recommended methodologies. Most of these methods have been implemented in one way or another at various agencies across the Country.

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The “Measured Retroreflectivity” method is popular at many DOTs and Toll Authorities. I believe this is the case because these agencies typically manage facilities that carry higher volumes of traffic that operate at higher speeds, thus increasing the risk and potential consequences of an accident. Many County and City agencies are utilizing the “Visual Nighttime Inspection, Expected Life, Control Sign, or Blanket Replacement” methods to manage their sign infrastructure. Each mentioned method is used for different reasons (financial vs. headcount) and has a lot to do with legacy management techniques (“We’ve always done it this way”).

There really isn’t a management method that can be considered “The Best” or “The Most Cost-Effective”. It is solely dependent upon an agency’s goals and objectives for the management of their sign infrastructure. I typically recommend conducting an inventory first and then implementing a management plan that uses the concepts of Condition, Risk, and Valuation to help prioritize which signs should be replaced along with the best timing for the replacement. This can prove very valuable since the highest risk signs can be replaced first and the least risky signs can be programmed for replacement as funding becomes available.

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Finally, I also recommend that agencies utilize asset management software to manage the work performed on their sign infrastructure so that all replacements can then be managed according to their useful life and actual condition rating. This information can then be used in concert with one another to help develop a capital improvement plan that details the planned fiscal expenditures for the next 10 years, which is the typical life-cycle of a sign.

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.

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.

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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.

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These vectors are then symbolized based on their cross-slope percentages and exported as a KML file for ease of use.

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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.

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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!

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.

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