Data-Driven Water and Wastewater Asset Renewal Planning: A Path to Sustainable Infrastructure

Water and wastewater assets—pipes, pumps, treatment plants, and more—are critical to public health and environmental protection. However, much of this infrastructure, particularly in older cities, is reaching or exceeding its design life. The American Society of Civil Engineers (ASCE) gave U.S. water infrastructure a C- grade in its 2021 Infrastructure Report Card, highlighting the need for proactive renewal strategies.

Traditional asset management often relies on reactive maintenance or age-based replacement schedules, which can lead to inefficiencies, unexpected failures, and wasted resources. Data-driven planning shifts the paradigm by using real-time data, historical performance, and predictive analytics to prioritize renewals based on risk, condition, and criticality. This approach maximizes the return on investment while minimizing service disruptions and environmental risks.

Key Components of Data-Driven Asset Renewal Planning

1. Asset Inventory and Data Collection

The foundation of data-driven planning is a comprehensive asset inventory. Utilities must catalog all assets, including pipes, valves, pumps, and treatment facilities, with details like age, material, location, and maintenance history. Modern tools like Geographic Information Systems (GIS) and Computerized Maintenance Management Systems (CMMS) streamline this process by centralizing data and enabling spatial analysis.

Data collection should include:

• Condition Assessments: Use technologies like acoustic sensors, CCTV inspections, or smart meters to evaluate asset health.

• Performance Metrics: Track flow rates, pressure, leakage rates, and energy consumption.

• Environmental and Operational Data: Monitor factors like soil conditions, water quality, and usage patterns.

2. Risk-Based Prioritization

Not all assets are equal in terms of criticality or risk. Data-driven planning uses risk assessment models to prioritize renewals. Risk is typically calculated as:

Prioritisation Score = 0.7*Risk x 0.2*Criticality x 0.1*Growth Planning

Risk = Likelihood of Failure × Consequence of Failure

• Likelihood of Failure: Based on asset condition, age, material, and environmental factors (e.g., corrosion risk in acidic soils).This is best calculated as a Mean Time between Failure (MTBF)

• Consequence of Failure: Considers impacts like service disruptions, public health risks, environmental damage, or repair costs.

• Criticality is rated 1 to 5 and used to determine the importance of plant being assessed. The larger the number the higher the criticality.

• Growth planning is another 1 to 5 and used to weight the criticality on work for potential development need.

For example, a trunk main under a busy urban street has a higher consequence of failure than a distribution rural pipe, even if both are in poor condition. Analytics tools can quantify these factors, allowing utilities to focus on high-risk assets first.

3. Predictive Analytics and Modeling

Predictive models use historical and real-time data to forecast when assets are likely to fail. Machine learning algorithms can analyze patterns in maintenance records, sensor data, and external factors (e.g., weather or population growth) to predict deterioration rates. For wastewater systems, models can identify pipes at risk of blockages or overflows, reducing the likelihood of sanitary sewer overflows (SSOs).

Tools like hydraulic modeling software (e.g., InfoWorks or EPANET) can simulate system performance under different scenarios, helping utilities plan renewals that align with future demand.

Additional failure datasets (main breaks, leaks) should be displayed on a GIS and overlayed against pipe age, material type data and compared against models.

4. Cost-Benefit Analysis

Data-driven planning integrates financial considerations into decision-making. By combining lifecycle cost data with risk and performance metrics, utilities can evaluate whether to repair, rehabilitate, or replace assets. For instance, trenchless technologies like pipe lining may extend the life of a sewer main at a lower cost than full replacement. Software platforms like InfoAsset or Cityworks can optimize these decisions by comparing costs against expected benefits, such as reduced water loss or fewer emergency repairs.

5. Integration with Smart Technologies

Smart technologies enhance data-driven planning by providing real-time insights. Examples include:

• Smart Meters: Detect leaks and monitor water usage.

• IoT Sensors: Track pressure, flow, or structural integrity in real time.

• SCADA Systems: Provide centralized monitoring of treatment plants and pumping stations.

These tools feed data into analytics platforms, enabling dynamic updates to renewal plans as conditions change.

Benefits of Data-Driven Asset Renewal Planning

1. Optimized Budget Allocation: Focus resources on high-priority assets, reducing wasteful spending on low-risk replacements.

2. Reduced Service Disruptions: Proactive renewals prevent unexpected failures, ensuring reliable water and wastewater services.

3. Extended Asset Life: Targeted interventions, like relining or cathodic protection, can delay costly replacements.

4. Environmental Sustainability: Minimize leaks, overflows, and energy waste, reducing the ecological footprint.

5. Regulatory Compliance: Meet stringent regulations by demonstrating proactive asset management.

Steps to Implement Data-Driven Asset Renewal Planning

1. Build a Data Foundation: Invest in GIS, CMMS, and condition assessment tools to create a robust asset database.

2. Standardize Data Collection: Ensure consistent data formats and regular updates to maintain accuracy.

3. Adopt Analytics Tools: Use software like IBM Maximo, InfoAsset, or custom-built models to analyze data and prioritize renewals.

4. Engage Stakeholders: Collaborate with engineers, operators, and finance teams to align renewal plans with operational and budgetary goals.

5. Pilot and Scale: Start with a pilot project (e.g., a single water main or sewer basin) to test the approach, then scale up.

6. Monitor and Adapt: Continuously update plans based on new data, performance metrics, and emerging technologies.

The City of Atlanta implemented a data-driven asset management program for its water and wastewater systems. By integrating GIS, hydraulic modeling, and risk-based prioritization, the city reduced water loss by 20% and prioritized $500 million in capital improvements over a 10-year period. Sensors and predictive analytics helped identify critical pipes, preventing costly breaks and improving service reliability.

Data-driven asset renewal planning transforms how utilities manage water and wastewater infrastructure. By leveraging data, analytics, and smart technologies, utilities can make informed decisions that enhance reliability, reduce costs, and promote sustainability. As aging infrastructure and growing populations strain water systems worldwide, adopting a data-driven approach is not just an option—it’s a necessity for a resilient future.