How SPM Works
3 Easy Steps
1
Data Collection
Leveraging our patented technologies and advanced AI systems, we conduct comprehensive surveys of your roads' pavement condition.
2
Data Processing
Our AI utilizes advanced algorithms to process your data, and our in-house engineers verifies and checks everything for accuracy.
3
Data Summary
Data is aggregated and delivered to you for enabling better decision making and project planning of your pavement rehabilitation plan.
Smart Pavement Management
A tool to take a snapshot of your pavement condition
Smart Pavement Management (SPM) provides a pavement condition rating with a map display, verified by in-house engineers, that allows for timely, cost-effective analysis.
Turnkey
We take care of everything including data collection.
Accessible
The online map is a great communication tool that bridges the public works department and council and residents.
Accurate
All outputs will be reviewed by our in-house engineers to ensure the highest values for clients.
Affordable
You would be surprised at our pricing. Please prepare the bills for the past pavement assessment survey.
Auxiliary
SPM provides data-driven and organized decision-making that allows better life cycle cost management.
Key Features
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.
Deterioration Prediction
Using a standard deterioration curve, SPM provides a prediction of future pavement conditions as an add-on feature of SPM-PCI.
SPM-IRI
SPM-IRI is a simplified readability index leveraged by machine-learning technology. Based on the bump data recorded during data collection, NEXCO's provides a roughness index.
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.
Recruitment
