Tag Archives: Asset Management

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Work Bundling for Utilities

Many utilities collect their infrastructure inspection data using a variety of techniques, sources and systems of record.  Having many different repositories of digital information makes it difficult to make informed decisions about where to spend operations and maintenance (O & M) and capital project dollars.  Having a “crystal ball” that aggregates all of this data into one single user interface could help these utilities make more informed decisions for their infrastructure as a whole, instead of using one inspection type to make these decisions.

For example, utilities typically collect information related to their structures and spans using one or a combination of these inspection techniques:

  1. Patrols
  2. Corona
  3. Infrared Inspections
  4. Climbing Inspections
  5. Walking Inspections
  6. Vegetation Points-of-Interest (LiDAR and Visual) Inspections
  7. NERC encroachments (LiDAR) Inspections
  8. Comprehensive Visual Inspection (CVI)

All of these inspections generate a large amount of data independent of one another and can be very useful if combined based on a unique structure or span number.  Once combined, this information can then be used to determine the best way to bundle work activities to achieve the greatest return-on-investment (ROI).

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Work bundling is a concept that has been well understood in the utility industry but not commonly practiced due to the disparate ways in which inspection data is collected and accessed from within a single agency.  Many work management systems only focus on the recording of work order information related to the labor, equipment and materials used to perform a project, but do not contain strategic planning tools.  These tools allow an agency to conduct “what-if” scenarios by applying different budget amounts against a planned work matrix.

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Once the optimal work matrix is determined, a workplan for that utility can then be planned and programmed, executed and tracked as a project or a series of projects for that planning horizon.  All costs related to that work matrix can be applied to each asset and tracked against an overall workplan budget.  These actual costs are then compared to the estimated costs to refine the planning matrix unit costs that are feeding the budget forecasting model.

As an agency completes the work for that particular period, it can then record the work activities against a particular asset which determines its next activity that is due in its life-cycle.  As this feedback loop is established, more cyclical work can be planned and programmed for future fiscal years and budget plans.

This concept has been applied at many utilities through the US using an asset management software called VUEWorks.  This software is GIS-centric at its core and allows users to connect their GIS data to their asset management system through the use of Esri GIS software.  The utility creates a map service which is consumed by VUEWorks and provides a mapping framework from which users can view inspection data from various sources.

For example, a helicopter inspection company collects CVI data by flying next to the transmission structures and collects high-resolution imagery of any defects located on that structure or its associated span.  Another vendor collects walking inspection information which includes subterranean excavations around a structure and its supports.  These inspections yield different defects which may require different types of activities to correct them.  This is where the concept of work bundling can be used.

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Since each inspection yielded different defects, the structure or span will need to be worked on at some point.  It is important that all departments responsible for line maintenance understand all of the defects present on a particular structure or span so that they can conduct all work activities at the same time.  In essence, VUEWorks provides this exact information, all in one place.  The utility has the ability to link all of this data together based on a structure or span ID and can then view all inspection data from one single user interface.

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This concept is important because if a utility needs to de-energize a line for maintenance or capital improvements, it will want to ensure that all issues are resolved during one outage.  Multiple outages cost money and this concept of work bundling is helping utilities achieve high ROIs for these projects by combining projects into one single project, instead of multiple projects.

In conclusion, the concept of work bundling saves utilities time and money through the aggregation of data into a single user repository.  This information can easily and effectively be used to make informed decisions and avoid multiple outage situations.  By combining multiple inspection data sets together, utilities can more proactively manage their assets cost-effectively while extending the useful life of their infrastructure investment.

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Transmission and Distribution Utility Infrastructure Capital Planning; A LiDAR and GIS-Centric, Data Fusion and Risk-Based Prioritization Approach

Introduction

Now that the NERC alert bubble has burst, the transmission and distribution sectors of the power industry has a wealth of information that can be leveraged to enhance their business operations. Most power companies are using LiDAR, Imagery and GPS data to collect detailed information about their infrastructure and this information can be leveraged to develop a GIS-centric Asset Management database. So, what can an agency do to leverage this information, especially when it comes from multiple vendors, sensors and vintages?

First, it is important to find the common denominator between all of the data the agency is working with. Utility data typically uses a Structure ID or Span ID that can be used to tie all of this information together from a database perspective. The location of the Structure or Span can also be used to tie information together geographically from a mapping perspective as well as temporally for those agencies collecting information annually or as part of a particular inspection time series.

Next, the agency can visualize all of this information spatially utilizing a GIS so that spatial patterns can be observed. Typical spreadsheet-based deliverables are missing the spatial relationships that can be used to develop better maintenance and operation plans by observing how assets interact with one another. This spatial perspective adds another valuable dimension to help agencies prioritize where to spend their limited resources.

Finally, a Risk-Based prioritization model can then be developed to help the agency decide where to spend their limited funding resources. The assets that pose the highest risk score based on the Probabilities of Failure and the Consequences of those failures can be prioritized, thus limiting the risk to the agency based on these types of failures.

LiDAR Data Collection, Utility Asset Extraction, and Inspection Data Aggregation

LiDAR data can be captured from fixed-wing aircraft or helicopter platforms, depending on the required resolution of the data. Most agencies are interested in capturing information about features that are located within the right-of-way of a powerline or its associated structures. These features are classified in the point cloud and then modeled using encroachment measurement criteria to identify potential hazards to the powerline infrastructure.

The LiDAR point cloud can be used to model the existing as-built structures, tops of towers, conductors, as well as the bare-earth ground model of the area. This information is then loaded into PLS-CADD software and modeled at a maximum load (sag) and maximum blowout conditions. Any LiDAR features that intersect with these “safe zone envelopes” are flagged as encroachments and will be highlighted in the PLS-CADD reports. These reports are exhaustive in terms of the amount of good information contained within them, but can be overwhelming to an agency when trying to figure out “where” to start focusing their time and resources on corrective actions.

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Once all of this analysis has been performed, these encroachment features can be geospatially located and mapped for further analysis. For example, vegetation encroachments can be identified as either “grow-in” or “fall-in” potentials and these points are classified as such.

Vegetation Encroachment Management

GIS mapping provides the user the spatial context necessary to make informed Operations and Maintenance decisions.  As an example, the location of vegetation encroachments is 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.  Since the agency knows so much about their encroachments, they can very accurately determine the volume of vegetation that needs to be removed.

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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.  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.

Risk-Based Asset Prioritization of Work Activities

Once your agency has identified where the encroachment issues are, how do you design a plan of action that gives your agency the biggest bang for your buck? In other words, there may be a section of powerline that contains many different encroachment types – Vegetation, Building, Ground Clearance, etc. Another section of line may only have Vegetation encroachments. The agency is most likely handling the corrective actions for these issues out of multiple departments and for good reason. Each type of encroachment brings its own set of design standards or engineering challenges to the table and all of these needs to be considered when designing a corrective action program for the facility.

One criterion 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.

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Risk models can be very complicated or very simple. It is dictated based on the information you wish to maintain moving forward and can use multiple automated inputs to help ease the data management strain moving forward. For example, an agency is using their LiDAR information to calculate the risk to a facility based on the number of LiDAR points that have been identified as encroachments as well as their height above ground; the higher the point, to more risky it is to the facility. In other words, the higher the vegetation feature, the more risk it poses to the facility. Since LiDAR data is composed of 3D points, the densities of these points can be applied to the facility’s risk score and then used to help prioritize the facilities that need the most work immediately.

Developing a Project Matrix and Estimating Costs Using Budget Forecasting

Once the facilities have been prioritized using the Risk concepts described above, the agency can then start planning for the actual work activities that will need to happen as part of their annual capital improvement planning activities. This can be achieved by using the Risk scores to determine which facility needs to be worked on and how much it will cost to improve that facility.

First, the facility components can be modeled from the LiDAR point cloud. As a simple example, we can imagine a distribution facility composed of a wood pole, conductors, cross-arm, guy wires and associated hardware. Each one of these facility components has a cost component associated with it based on the materials used and the characteristics of how it was constructed. The cost of materials can then be applied to each component and an overall facility cost can then be determined for the asset.

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Once the facility templates are constructed, the agency can then start developing projects to improve or replace these facilities based on the results of the inspection information. This activity will allow the agency to determine the cost of a project in relation to their annual maintenance and operations budgets and then determine what they can improve for that fiscal years’ time frame.

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All of this information can then be used to determine future years’ capital improvement plans based on funding availability and projected costs over time. This helps the agency to plan for future fiscal expenditures using a repeatable and defensible model that can be applied to different Asset Classes and Asset Types. In other words, multiple, disparate data sources can be fused to support the risk-based prioritization of work activities.

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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.

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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.

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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.

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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.

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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.

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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

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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.

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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.

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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.

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Mobile LiDAR to Support Positive Train Control

DTS/Earth Eye just completed a positive train control (PTC) project for a national train company who was evaluating the differences between Airborne LiDAR and Mobile LiDAR to support the collection of PTC data.  They are currently collecting airborne data for approximately 15,000 linear miles of rail.  In certain areas, the airborne data does not provide enough fidelity to accurately map the rails or the asset infrastructure that support the railroad operations.

From Wikipedia – “The main concept in PTC (as defined for North American Class I freight railroads) is that the train receives information about its location and where it is allowed to safely travel, also known as movement authorities. Equipment on board the train then enforces this, preventing unsafe movement. PTC systems will work in either dark territory or signaled territory and often use GPS navigation to track train movements. The Federal Railroad Administration has listed among its goals, “To deploy the Nationwide Differential Global Positioning System (NDGPS) as a nationwide, uniform, and continuous positioning system, suitable for train control.”

The project involved the collection of Mobile LiDAR using the Riegl VMX-250 as well as forward-facing video to support PTC Asset Extraction.  The system was mounted on a Hi-Rail vehicle and track access was coordinated through the master scheduler with the Railroad company.  Once we had access to the tracks, we had one shot to make sure the data was collected accurately and we had complete coverage.  All data was processed on-site to verify coverage and we had a preliminary solution by the end of the day that was checked against control to verify absolute accuracies.  We collected the 10-mile section of rail in about 2 hours and this timing included a couple of track dismounts required to let some freight trains move on through.

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The following graphics illustrate the point cloud coverage colored by elevation (left) and Intensity (right).

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Mapping the rails in 3D was accomplished by developing a software routine designed to track the top of the rail and minimize any “jumping” that can occur in the noise of the LiDAR data.  Basically, a linear smoothing algorithm is applied to the rail breakline and once it is digitized the algorithm fits it to the top of the rail.  The following graphic illustrates how this is accomplished – the white cross-hairs on the top of the rail correspond to the breakline location in 3D.

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So, back to the discussion about Airborne PTC vs Mobile PTC data.  Here is a signal tower collected by Airborne LiDAR.  The level of detail needed to map and code the Asset feature is lacking, making it difficult to collect PTC information efficiently without supplemental information.

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The next graphic shows the detail of the same Asset feature from the mobile LiDAR data.  It is much easier to identify the Asset feature and Type from the point cloud.  In addition to placing locations for the Asset feature, we also provided some attribute information that was augmented by the Right-of-Way camera imagery.  By utilizing this data fusion technique, we can provide the rail company with an accurate and comprehensive PTC database.

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This graphic shows how the assets are placed in 3D, preserving the geospatial nature of the data in 3D which is helpful when determining the hierarchy of Assets that share the same structure.

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One last cool shot of a station with all of the furniture, structures, etc that make it up – pretty cool!

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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!

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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