Effective moisture tracking with TDR technology reduces pavement deterioration and maintenance costs, enhancing road longevity and performance.
Roads deteriorate 2.5x faster with excess moisture. This makes tracking subsurface water levels critical for extending pavement life and cutting maintenance costs by 12–30%. Time Domain Reflectometry (TDR) technology offers a fast, non-disruptive way to measure moisture, enabling smarter repairs and proactive planning.
TDR systems, like those used by Fulton Hogan, integrate seamlessly with road management software, offering real-time data for prioritizing repairs, verifying treatments, and ensuring construction quality. This approach helps prevent damage, improve road durability, and optimize budgets.
Moisture infiltration plays a major role in how pavements perform and how long they last. It reduces the pavement's ability to handle loads and speeds up damage caused by traffic. Grasping how this works is key to planning effective maintenance.
When too much moisture gets into pavement structures, it weakens the road base, making it more prone to damage from traffic. This can lead to issues like unstable base materials, surface cracks, and subgrade deformation. These problems often become worse during seasonal changes when moisture levels vary greatly.
Shifts in moisture levels throughout the year add another layer of complexity to pavement maintenance. These changes can worsen the types of damage mentioned earlier, creating a snowball effect on pavement wear. For example, winter freeze-thaw cycles and heavy spring rains have a big impact on moisture below the surface, requiring focused monitoring and specific maintenance strategies.
Tracking these seasonal moisture shifts is essential for using TDR data effectively in maintenance planning. By analyzing these patterns, teams can move from fixing problems after they occur to preventing them in the first place. This approach helps extend the life of pavements and makes better use of maintenance budgets.
Time Domain Reflectometry (TDR) technology brings a new level of precision to road maintenance by offering continuous, real-time moisture readings. It collects 5–20 samples per second, covering a 4-foot width and reaching depths of up to 10 inches. This process creates detailed moisture profiles without disrupting traffic flow.
By measuring the dielectric constant, TDR pinpoints areas with higher moisture levels. The data is time- and location-stamped, producing accurate, historical moisture maps. This constant monitoring helps maintenance teams plan ahead and address issues before they escalate.
TDR data plays a key role in improving maintenance planning. It allows teams to spot moisture problems early, preventing costly damage. By leveraging TDR insights, organizations can cut maintenance costs by 12% to 30%.
"Fulton Hogan is excited that this latest data will support works prioritisation, decision making and development of targeted outcomes with clients. Collecting moisture content of an entire network at high speed without disruption is a real advantage over previous test pit / lab test techniques. We're aligning our previous maintenance activities with the moisture readings to understand what methods and treatments are best at reducing moisture within the surface and pavement layers. Proving what works well in each region also provides us support for future maintenance activities."
TDR data supports maintenance teams in several ways:
These insights turn raw data into actionable strategies, extending pavement life and cutting down on overall maintenance costs.
The data is accessible through a user-friendly portal, offering both plan and linear views. This setup highlights patterns and connections between moisture levels and pavement performance, helping teams make informed decisions.
TDR offers a non-invasive way to detect moisture issues, helping organizations plan maintenance more effectively. By spotting moisture-related problems early, it allows for smarter strategies that extend pavement life and cut down on costs. Here's a quick look at the main advantages:
Benefit | Impact |
---|---|
Early Detection | Spots hidden high-risk moisture areas before visible damage appears |
Cost Reduction | Supports proactive maintenance to lower repair costs |
Quality Control | Ensures proper compaction and moisture levels during construction |
Treatment Verification | Evaluates how well maintenance treatments work using moisture data |
Work Prioritization | Helps schedule maintenance based on data-driven insights |
TDR data doesn’t just stop at moisture monitoring - it integrates seamlessly with road management software, improving decision-making. A great example is Fulton Hogan's use of moisture data within Junoviewer, which has enabled better analysis of road deterioration and smarter Forward Work Programs.
This integration enhances several critical tasks:
Fulton Hogan is excited that this latest data will support works prioritization, decision-making, and development of targeted outcomes with clients. Collecting moisture content of an entire network at high speed without disruption is a real advantage over previous test pit/lab test techniques... We're aligning our previous maintenance activities with the moisture readings to understand what methods and treatments are best at reducing moisture within the surface and pavement layers. Proving what works well in each region also provides us support for future maintenance activities.
– Craig Reed, Senior Technical Asset Manager, Fulton Hogan
TDR technology is transforming pavement maintenance by making it more efficient and cost-effective. Detecting moisture early plays a key role in preventing rapid road deterioration.
Organizations using continuous, non-invasive moisture readings have reported cost savings between 12–30% and increased road longevity. When TDR moisture data is integrated into management systems, it supports smarter, data-driven decisions.
By combining TDR moisture data with road management systems - using tools like TDRI's advanced monitoring solutions - companies such as Fulton Hogan can: