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NEXCO HIGHWAY SOLUTIONS OF AMERICA INC.
- A Subsidiary of NEXCO-Central
UPDATES
NHSA Newsletter
Current Edition: Volume 17
GIS Tips - Get to the Point with Point Mode!
Locating sporadic distress like potholes can be a common challenge for road maintenance teams. It can require you to check each street one by one, costing you time and energy. For efficient and timely maintenance planning, it’s important to have a clear picture of the road conditions across your city.
Why not use Smart Pavement Management (SPM) to identify sporadic distresses in your city? Each point in our GIS Point Mode is assigned a color based on the 0-100 scale rating correlated to the Pavement Condition Index (PCI). Green indicates good condition, while red highlights areas needing attention. This allows you to identify segments in need of repair work. Zoom in and click on the Point button to view snapshots captured every 33 feet. With Point Mode, you can get easy access to detailed information all at a glance, helping you make data-driven decisions.
Take advantage of our 5-mile free trial of SPM-PCI today and experience Point Mode firsthand!
![Image by Matheus Farias](https://static.wixstatic.com/media/nsplsh_676f4730526b554e31424d~mv2.jpg/v1/fill/w_123,h_153,al_c,q_80,usm_0.66_1.00_0.01,blur_2,enc_auto/nsplsh_676f4730526b554e31424d~mv2.jpg)
![pmg4710237.gif](https://static.wixstatic.com/media/f76d54_6be4cb0d86af44f7b9c6e1afd8db2df9~mv2.gif)
Other Articles
Volume 1: Optimizing Pavement Management Costs
Volume 2: Ranking Pavement Condition
Volume 3: Keeping Everyone Informed
Volume 5: Beat The Heat! GIS Map Modes
Volume 6: GIS Predictive Slider Tool
Volume 7: Addressing Individual Resident Concerns
Volume 8: SPM-IRI
Volume 9: Measuring Repair Output
Volume 10: Ratings and Repair Methods
Volume 11: Winter Storm Impacts Pavement Deterioration
Volume 12: Smoothness Starts With A Survey
Volume 14: Enhancing Efficiency with Budget Simulation
Volume 15: Capture the Consequences of Extreme Weather
Volume 16: 4A Values with AI-based Pavement Assessment
Commercial Projects
Updated on March 31, 2024
Texas
City of Anna
City of Bridgeport
City of Plano
City of Roanoke
City of Carrollton
City of Hamilton
City of Hutchins
City of Irving
City of Sachse
City of Terrell
City of The Colony
City of Whitesboro
City of Wilmer
City of Crowley
City of Commerce
City of Huntington
City of Lucas
City of Wichita Falls
City of McLendon-Chisholm
City of Joshua
City of Merkel
Oklahoma
City of Ardmore
City of Piedmont
And more...
Free Trial Run Project
Since SPM is a cutting-edge solution powered by the latest AI technology, a free trial run project would be necessary to better understand the benefits and values.
At the moment, more than 40 cities in North Texas and Oklahoma have commenced pilot projects.
Please contact us right now, to enjoy the opportunity.
![Working Together on Project](https://static.wixstatic.com/media/11062b_1bae4c1b9e17401eb83214230196c28c~mv2.jpg/v1/fill/w_123,h_82,al_c,q_80,usm_0.66_1.00_0.01,blur_2,enc_auto/11062b_1bae4c1b9e17401eb83214230196c28c~mv2.jpg)
What's New
![At Work](https://static.wixstatic.com/media/11062b_b3b44769b7b74bf3a7c3e620b32e71e5~mv2.jpg/v1/fill/w_123,h_82,al_c,q_80,usm_0.66_1.00_0.01,blur_2,enc_auto/11062b_b3b44769b7b74bf3a7c3e620b32e71e5~mv2.jpg)
SPM-PCI
SPM-PCI is NEXCO’s proprietary index system. Like Pavement Condition Index (PCI) using the ASTM 6433 standard, it is a numerical index between 0 and 100, which indicates the general condition of the surveyed pavement. SPM-PCI has a statistically significant correlation with PCI.
SPM-IRI
SPM-IRI is a simplified readability index leveraged by machine-learning technology. Based on the bump data recorded while data collection, NEXCO's provides a roughness index.
Deterioration Prediction
Using a standard deterioration curve, SPM provides a prediction of future pavement conditions as an add-on feature of SPM-PCI.
Budget Simulation and Repair Planning
Based on SPM-PCI and the predictive feature, our algorithm automatically offers a simulation of different budget scenarios over the years. The algorithm suggests the optimal combination of repair segments based on our mathematical model. Also, the segments located in an area where many others require the same maintenance category gain higher weights in the calculation to make the suggested work plan efficient and realistic. This entire process is designed to mimic the considerations of the engineers who conduct maintenance planning manually.