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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Pavement Management Data Analysis

We’re just finishing up on a couple of pavement re-inspection projects.  Project #1 was our 3rd re-inspection of the network and Project #2 was our second inspection of their network.  Both projects are located in the Rocky Mountain region of the US and have pretty harsh freeze/thaw cycles, particularly in the past couple of winters.  Our clients were mostly interested in how their network was performing over time as compared to our predictive models set up in their pavement management software.

The results are pretty interesting for Project #1 – as you can see, their network is deteriorating a lot faster these days based on a few harsh winter seasons.  The Bright Green lines show the distribution of their pavement in 2010 as compared to the 2008 (Yellow) and 2005 (Red) inspections.

These differences are directly attributable to the past 3 winter seasons and their impact on their pavement infrastructure.  This particular client plans on using this information to acquire additional funding for their pavement management program for the next few years to “catch up” with the maintenance on their network.  The following graphic displays the current condition distribution for this client for their most recent inspection in 2010.

Most pavement prediction models utilize performance curves to predict pavement performance over time.  These models hold true in the short-term, but can fluctuate based directly on weather events or other human factors such as changing traffic conditions.  In some cases, re-inspection is necessary to adjust these estimates so an agency can fund its capital improvement program effectively.  This was the specific intent of this client and there is no doubt why they are one of the premier places to live in because of their proactive asset management approach!