Asset Management and ADA Compliance–Building a Risk Mitigation Strategy using VUEWorks–Part 2

Asset Management and ADA Compliance–Building a Risk Mitigation Strategy using VUEWorks–Part 2

In our last blog post, we introduced the concept of ADA compliance and discussed an approach to compliance through the utilization of Asset Management principles.  To recap, we talked about the following steps in the process and how each step leads to a more strategic approach to ADA compliance:

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  • Inventory – Utilizing GIS, mobile mapping and boots-on-the-ground inspection (where required).
  • Assess – Visually inspect infrastructure assets and quantify their compliance.
  • Prioritize – Develop a list of high-risk assets that need immediate attention.
  • Execute – Re-construct, upgrade or maintain infrastructure assets that are part of an annual work plan.
  • Rinse and Repeat – Execute work plan annually and re-assess the network of assets every 3-5 years to update the plan.

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Step 1 – Inventory

The initial inventory of your network can be accomplished is a variety of ways, but most common methods include Mobile Asset Collection and Boots-on-the-Ground techniques.  Mobile Asset Collection is a fast and cheap way to gather imagery of your street network, from which you can conduct an initial visual assessment of your ADA compliance.  Many agencies use this technology to establish the Location and Characteristics (attributes) of their Sidewalk and Curb Ramp infrastructure.  Knowing this information is half of the battle, since many agencies cannot answer some basic questions related to their infrastructure, including:

  • How many miles of sidewalk do we own and maintain?
  • What kind of condition are our sidewalks (asphalt or concrete) in?
  • How many curb ramps to we own and maintain?
  • Where are we missing curb ramps?
  • Where are our compliance issues located?
  • And many more…

By collecting this initial inventory information, an agency can start to develop its internal plan to gain compliance over time while developing a budget to help achieve this plan.

Step 2 – Assess

The assessment procedure involves a series of steps that are both automated and manual, depending upon the technology used to conduct them.  In most cases, mobile data collection is used to conduct the initial assessment of the assets and then a more rigorous boots-on-the-ground approach is used to fill in the gaps (obscured assets) and to collect data that requires precise measurement such as slope information  (Ramps) and trip hazards (Slab faults and Cracks).  This approach saves both time and money because it is basically a visual assessment that identifies major (Risky) issues and highlights areas that need immediate attention.  Therefore, an agency can lower their risk of litigation by taking measures that focus on short-term, high-risk assets while still providing support for the assessment of longer-term (lower Risk) assets.

The assessment process can be facilitated within the VUEWorks Asset Management system through the utilization of the Condition (Inspection) module.  As the inspector views the right-of-way imagery, they can record the assessment in a configurable condition form that is designed to record ADA compliance.  The form below illustrates how different Items can be inspected and divided into specific categories.  Each “Category” can be further broken down into specific inspection “Items” that can contain some kind of Condition rating (1-5, Good, Fair, Poor, 0-100, etc).  Each of the individual Items can then be queried individually or combined into an aggregate score by Category and then further rolled-up into an Overall Condition Index for the Asset.

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Once the Condition score is generated, the resulting Condition Indices can then be symbolized in the GIS as a visual representation of the Sidewalk/Ramp condition.

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VUEWorks also provides some useful tools, including integration with Esri Basemaps (ArcGIS Online) and Google StreetView.

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Step 3 – Prioritize

VUEWorks provides the ability to assess Risk based on the criteria that matter to your organization.  For example, would you go and fix a sidewalk or curb ramp on a road that was travelled by someone with a disability?  Or, would you spend that money elsewhere?  Risk can help you prioritize WHICH asset to fix and WHEN to fix it based on many different criteria.  For example, an agency can look at a few different things when determining WHAT to fix and WHEN to fix it.  They can observe the Consequences of Failure (What happens IF the asset fails) and the Failure Probability (Likelihood of Failure).  As illustrated in the graphic below, the Consequences of Failure can be measured and rated for different categories.  The Failure Probabilities can then be rated based on “How” an asset fails, or its Failure Modes.  Each Failure Mode can contain a different Probability of Failure which allows the agency to understand what the Influencing Failure Mode is when determining what type of maintenance to prescribe for that particular asset.

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Step 4 – Execute

The Budget Forecasting tool in VUEWorks allows user to develop “What-if” scenarios to plan and estimate the cost of projects based on the application of specific Jobs.  Projects can be prioritized based on the Failure Probability, Risk Factor, Criticality Factor or any combination of the above.  Once a project is involved in the plan, its Baseline Condition and the forecasted Condition can be viewed over its life cycle.  All of this information can be used together to develop a Strategic Asset Management Plan utilizing the Intelligence gained for each individual asset the agency maintains.

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At the end of the day, we understand that ADA compliance is a balancing act where limited resources are being applied against assets that are critical to the operation of an agency’s transportation network.  Although other critical infrastructure (Pavement, Signs, Signals, etc.) usually get the bulk of the funding, it is time to focus a portion of these resources against assets that are critical to the safety of our disabled citizens.

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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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Asset management strategies–which is right for me?

We get a lot of questions about developing the right strategy as it relates to assets that are managed by different agencies.  These questions are typically focused on “How” to manage assets, which typically comes after the agency decides “Why” to manage assets.

Here are some typical questions:

  1. When is the best time to manage my asset in its life-cycle?
  2. When do I rehabilitate my asset?
  3. What do I do to the asset?
  4. When do I replace my asset?
  5. Can I just let it runs its course and when it fails, replace it?
  6. Should I invest time and money in an asset early in its life-cycle or wait until it is in poor condition to fix it?

We always recommend starting this process by understanding a few things about the asset.

1.  Financial Considerations – How much does an asset cost to install and Maintain?  Is it capitalized or not?   In most cases, the cost of an asset has a large impact on how it is managed.  This is not the only consideration, but we can use it as a starting point.

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2.  Risk Considerations – What are the consequences to the agency if this asset fails?  Will someone get hurt?  Will it cause an accident?  These are closely tied to other financial considerations such as tort liability.

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3.  Life-Cycle Considerations – How does the asset typically deteriorate?  Is it straight-line deterioration or more of a polynomial-type of a curve?  This information helps determine what to do to an asset and when to do it (less cost when starting earlier in the process).  Programmatic treatments or inspection-driven treatments are common approaches to managing assets with this approach.

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Once an agency has a solid understanding of the Financial, Risk and Life-Cycle considerations related to an asset, they can begin to develop a management strategy specifically for the asset type to be managed.  Since every asset can be managed differently, we will focus on a couple of assets and their management strategy.

Pavement

  1. Financial – Capitalized asset – high cost to install and maintain.
  2. Risk – Critical to the movement of people and commerce – high consequence of failure.
  3. Life-Cycle – Long-term asset with long-term life expectancy – Can be managed using a life-cycle or Inspection-based approach.

Pavements have a long history of research and empirical data models that have been developed for Airports, Parking lots and Roads and a variety of software exists to support the maintenance of this asset.  Therefore, it is pretty easy to choose an approach to manage pavement based on an agency’s goals and priorities.  Typically this program is inspection-driven (every 3-5 years) and focuses on finding the best mix of Preservation and Rehabilitation activities designed to achieve their target Level-of-Service.

Signs

  1. Financial – Capitalized asset – low to high cost to install and maintain.
  2. Risk – Critical to the safety of people and commerce – low to high consequences of failure.
  3. Life-Cycle – Medium to long-term life-expectancy – Can be managed using a life-cycle or Inspection-based approach.

Signs have less empirical data collected for them and can have varied Financial, Risk and Life-cycle information compiled and available throughout the industry.  Strategies for management are typically focused on Life-Cycle and Risk and there are many methodologies that are accepted by FHWA.  These are outlined in their Manual on Uniform Traffic Control Devices (MUTCD) and are widely utilized throughout the US.

Light Poles

  1. Financial – Capitalized asset – medium cost to install and maintain.
  2. Risk – Semi-Critical to the safety of people and commerce – low to high consequences of failure.
  3. Life-Cycle – Medium to long-term life-expectancy – Can be managed using a life-cycle or Inspection-based approach.

Light poles are typically managed by inspection of their base attachments (every 10 years or so) but many agencies typically run these assets to failure (luminaire failure or pole failure).  This is another mixed bag of management because some light poles provide a critical safety function (DOT) and others just light the way for safety (walkways) and are not as critical to the daily operations of an agency.

These are just a few examples of strategy development – we would love to see comments related to the infrastructure that you manage and we will reply with some of the Industry’s Best-Management-Practices (BMPs) that are successfully used throughout the US.

Mobile LiDAR to Support Positive Train Control

This article was originally written in 2011, but is being re-posted based on recent events…

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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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.

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.

Bundling

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.

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.