Aging sewer systems are a growing challenge, with many networks over 100 years old. To tackle this, utilities are combining Geographic Information Systems (GIS) and digital twins to improve maintenance and planning. Here's how:
- GIS: Maps sewer assets like pipes and manholes, creating a detailed inventory of their location and condition.
- Digital Twins: Virtual models simulate real-time scenarios using data from sensors, inspections, and historical records.
Together, these tools help utilities predict failures, prevent costly repairs, and extend asset lifespans. For example, Miami-Dade County increased productivity by 60% using GIS-based prioritization, while Tennessee's White House Utility District cut water leaks significantly with a digital twin.
The integration of these technologies requires effort, especially digitizing old records, but the payoff includes better decision-making, efficient maintenance, and improved communication with stakeholders.
Ep 18: Accelerating Sewer Inspection with Artificial Intelligence and GIS
1. Digital Twins
A digital twin is essentially a dynamic, virtual model of a sewer system. It brings together data from sources like CCTV inspections, IoT sensors, SCADA systems, laser scans, and older documentation to create a comprehensive digital representation.
Data Integration
The magic of digital twins lies in how they layer diverse datasets into a single, unified model. These models typically include three main layers: landscape, network, and 3D structural models. AI and machine learning play a big role here, processing raw CCTV footage and assigning standardized NASSCO defect codes to specific pipe segments. Take the Town of Newburgh, Indiana, for example. In 2023, their team used the PipeAId AI platform to analyze inspection data for 40,000 linear feet of pipe. By integrating AI-generated defect codes into their ArcGIS-based digital twin, they eliminated subjective judgments and built a solid, data-driven foundation for maintenance planning. This layered approach not only captures the current state of the system but also sets the stage for predictive insights.
Predictive Maintenance
One of the standout features of digital twins is their ability to predict failures. By analyzing patterns in sensor readings and inspection footage, AI models can identify which pipe segments are at the highest risk of failure - and even estimate when it might happen. A great example is the White House Utility District in Tennessee. Faced with nonrevenue water rates nearing 50%, they developed a digital twin of their sectorized distribution network. By combining district metered areas with real-time flow sensors, they managed to lower their infrastructure leakage index to under 1.0 by 2020 - a significant achievement.
Asset Management
Digital twins are transforming how utilities approach asset management. Instead of relying on age-based schedules, they enable data-driven decisions based on the actual structural health of assets. For instance, Miami-Dade County improved productivity by 60% using flexible GIS solutions, which allowed for precise prioritization of rehabilitation efforts. In Germany, where sewage systems span nearly 600,000 km (about 373,000 miles) and have an average age of 37 years, this kind of data-driven approach is crucial. In cities, where 15% of the infrastructure is over a century old, prioritization based on real data becomes even more critical.
Stakeholder Communication
Making technical data understandable and actionable is a key benefit of digital twins. They transform complex information - like pipe conditions, flow rates, and defect codes - into visual dashboards and interactive maps. These tools help city leaders, community members, and field crews make informed decisions. For instance, SUEZ North America implemented a digital twin to streamline customer service across more than 1,000 employees. Using real-time GIS maps, representatives could view active construction and main breaks, resolving most water quality inquiries during the first call. Field crews also benefited, accessing asset histories and recording updates on mobile devices, ensuring seamless coordination between planning and on-the-ground operations.
2. GIS Platforms
GIS platforms form the backbone of digital twins, serving as a central hub that connects various datasets - like landscape information (elevation, hydrology), network models (e.g., pipe connectivity), and Building Information Modeling (BIM) for treatment facilities - into one cohesive system. By creating geodatabases, GIS links resources like CCTV inspection videos, high-resolution imagery, and sensor readings to specific pipe segments on a map. This integration supports tasks like data management, maintenance planning, asset tracking, and effective communication with stakeholders.
Data Integration
One of GIS's standout features is its ability to bring together diverse data types into a single, accessible format. Platforms like ArcGIS Pro use geoprocessing tools to merge laser scanning point clouds, historical structural data, and live sensor feeds. This "linked data" approach means field crews can instantly pull up details about a pipe segment - its age, material, past inspection results, and current flow rates - all in one interface.
"GIS is able to take thousands of pieces of information and put them into a usable format so utilities can take advantage of the information they already have to complete cost-effective maintenance".
Predictive Maintenance
While digital twins simulate system performance, GIS provides the real-time data that powers these simulations. By tracking details like pipe size, material, age, and inspection history within a spatial framework, GIS helps utilities move from reactive fixes to proactive maintenance strategies. It identifies which assets are most likely to fail and when. For example, in 2023, Raleigh Water launched a vertical asset management program using GIS technology. This program gives a live view of maintenance activities and asset conditions, improving efficiency across the utility. Additionally, GIS platforms incorporate environmental and weather data, enabling utilities to anticipate how extreme conditions might impact their systems.
Asset Management
GIS builds on digital twins by offering a centralized data source for informed decision-making. Utilities refer to this as an "authoritative view" of their infrastructure. Gone are the days of outdated paper maps and scattered spreadsheets - GIS allows utilities to map every pipe, manhole, and pump station with detailed specifications. This centralized system enables smarter capital investments, whether that's rehabilitating aging pipes or replacing sections nearing the end of their lifespan. The ability to define subnetworks and analyze how localized issues could affect the broader system adds another layer of precision.
Stakeholder Communication
GIS shines in turning complex data into easy-to-understand visuals. Interactive maps and web-based dashboards transform raw data - like inspection results, AI-generated defect codes, and sensor readings - into formats that city leaders, community members, and field crews can quickly grasp. These tools, similar to those used in digital twins, foster real-time collaboration across teams. Field crews, for instance, can access asset histories and inspection videos on their mobile devices, enabling them to have informed discussions with residents about ongoing maintenance efforts.
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Advantages and Disadvantages
GIS vs Digital Twins vs Combined Solution for Sewer System Management
Digital twins and GIS each bring unique strengths and challenges to the table. When combined, they enable a proactive, data-driven approach to sewer management. Below, we dive into the advantages and limitations of each system, as well as their integration.
Digital twins shine in real-time simulation and predictive maintenance. They allow for "what-if" scenarios, predicting how systems might respond to extreme weather events or pipe failures. By incorporating AI-powered inspection tools, digital twins remove the subjectivity often associated with defect coding under NASSCO's extensive 230+ observation codes. However, they come with high computing requirements and rely on advanced multi-sensor robotic platforms to accurately capture the 3D geometry of buried assets. Setting up a digital twin can be particularly tricky for older systems that rely on century-old, hand-drawn records that must first be digitized.
GIS, as discussed earlier, excels in centralizing data, which is critical for enabling digital twins. It consolidates vast amounts of information - such as pipe specifications, inspection records, and sensor data - into a single accessible platform. GIS also supports advanced network tracing, helping to identify infiltration sources and simulate the effects of wet weather. However, traditional GIS models often fall short when it comes to real-time synchronization and behavioral modeling. They tend to function more as static record-keeping tools rather than dynamic, operational systems.
When integrated, these technologies create a powerful, spatially-aware digital twin capable of proactive management. Integrated systems streamline large-scale data analysis and enable automated, bidirectional data flow between physical assets and their virtual counterparts. This shift allows for proactive resilience management, moving away from reactive repairs.
The main challenge of integration lies in the complexity of the initial data setup. For example, in Jackson, Mississippi, extensive digitization of century-old records was required, making the process time-consuming and resource-intensive. Time is a significant concern for managers, who fear long implementation timelines could deplete budgets before delivering noticeable results.
The table below summarizes the key features, benefits, and drawbacks of GIS, digital twins, and their combined use:
| Feature | GIS Platforms | Digital Twins | Combined Solution |
|---|---|---|---|
| Primary Focus | Spatial location and asset inventory | Real-time behavior and simulation | Spatially-aware predictive operations |
| Data Type | Static/Historical | Dynamic/Real-time | Holistic (Static + Real-time + AI) |
| Maintenance Approach | Reactive/Scheduled | Predictive/Condition-based | Proactive Resilience Management |
| Key Benefit | Mapping hidden assets | "What-if" scenario testing | Unbiased, automated health monitoring |
| Main Drawback | Lacks real-time synchronization | Significant computing demands | Complexity of initial data integration |
Conclusion
By combining digital twins with GIS, utilities can revolutionize how they manage sewer systems. GIS serves as the spatial backbone, offering detailed maps, asset inventories, and connectivity insights that outline wastewater flow systems. Digital twins enhance this framework, integrating real-time sensor data, AI-driven inspections, and predictive modeling. The result? A shift from reactive fixes to proactive, resilience-focused management.
Starting fresh isn’t necessary. Christa Campbell and David Wachal from Esri's Water Industry team emphasize this point:
"The best place to start is where you already are. Most utilities have already made significant investments in data and digital systems... Starting from scratch, or trying to tackle everything at once, is both unwise and unnecessary".
The key lies in building step by step. Utilities can leverage their existing GIS investments while gradually incorporating real-time data and AI tools to address specific challenges. This phased approach enables consistent, data-driven progress.
Pilot projects are a smart first step. Focusing on high-impact initiatives provides measurable results and demonstrates the value of integrating digital twins with GIS solutions.
With nearly one-third of the 1.7 million water industry professionals in the U.S. approaching retirement within the next decade, capturing and digitizing institutional knowledge is more urgent than ever. On top of that, climate-related disasters, which average $12 billion in annual costs, underscore the need to modernize aging infrastructure. Breaking down data silos between IT, operations, and engineering teams is crucial for creating a unified view of utility networks.
Ultimately, integrating these technologies reshapes decision-making processes. Prioritize critical data for real-time monitoring and ensure field teams have mobile access to digital twin insights to improve communication and efficiency. Together, GIS and digital twins provide a robust framework for sustainable, cost-effective sewer management.
FAQs
How do digital twins help with predictive maintenance in sewer systems?
Digital twins combine real-time sensor data, GIS platforms, and AI-powered models to simulate sewer networks dynamically. These simulations can forecast potential pipe failures, assess the remaining lifespan of infrastructure, and pinpoint maintenance needs before problems develop.
With this condition-based maintenance approach, digital twins help prevent unexpected breakdowns, streamline repair schedules, and extend the life of sewer systems. This not only improves resource management but also reduces disruptions for the surrounding communities.
What challenges do utilities face when integrating GIS with digital twins for sewer systems?
Integrating GIS with digital twins for sewer systems comes with its fair share of challenges. One of the biggest hurdles is data quality and inconsistency. Utilities often rely on various data sources - laser scans, CCTV footage, historical records, and BIM models - which are stored in different formats. These datasets can be riddled with gaps or errors, making it tough to create a reliable digital twin that mirrors the actual sewer system.
Another obstacle is technology compatibility and interoperability. GIS platforms and digital twin systems often operate on different standards, which means custom solutions are usually needed to enable seamless, real-time data sharing. On top of that, outdated, static documents lacking detailed context about infrastructure conditions make it even harder to develop and maintain a live, up-to-date digital twin.
Lastly, there are methodological challenges to consider. Keeping the digital twin synchronized with real-world field operations and establishing proper validation protocols are critical but tricky tasks. Tools like those provided by Anvil Labs can help tackle these issues. They centralize data management, offer annotation and measurement tools, and ensure secure, cross-device access - making it easier to maintain an accurate, dynamic digital representation of sewer systems.
How can utilities integrate digital twins with their existing GIS systems?
Utilities can take their GIS systems to the next level by integrating digital twins as a core spatial tool for managing assets. The first step is to assess and update GIS data layers - like pipes, manholes, and pump stations - to ensure they're accurate and reliable. Once refined, these layers can be enriched with detailed 3D models created from LiDAR scans, Building Information Modeling (BIM), or photogrammetry. Platforms such as Anvil Labs make it simple to host, view, and manage these models alongside traditional GIS data. This setup also supports the addition of inspection videos, annotations, and other vital information, all in one place.
After linking 3D assets, utilities can connect real-time sensor data - such as flow meters or pressure monitors - and AI-driven inspection insights to create a dynamic, constantly updated view of the sewer system. A phased rollout is often the most effective approach. Start with planning and standardizing data, then move on to high-resolution scanning and modeling. The final step involves deploying tools like dashboards and scenario analysis to make the system actionable. Launching with a small pilot area allows utilities to fine-tune their methods before scaling up, paving the way for better predictions and smarter decisions across the entire network.
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