Identifying and Prioritizing Critical Sewer Pipes Using GPAD and SSO Metrics

Houston's wastewater system serves 2.3 million residents through 38 treatment plants, over 375 lift stations, multiple wet-weather facilities, and more than 6,200 miles of pipes. As populations grow and climatic patterns shift, these networks face mounting challenges from aging infrastructure, climate change, and stringent regulations. This study applies a four-quadrant classification-combining Gallons Per Acre Per Day (GPAD) and Sanitary Sewer Overflow (SSO) data-to rapidly categorize basins by capacity usage and documented failure points. After pinpointing high-risk basins (e.g., High GPAD, High SSO), six peak-flow modeling scenarios validate and refine capacity assessments. By aligning the quadrant-based insights with multi-scenario hydraulic modeling outputs, the methodology highlights which basins or pipe segments are most prone to surcharge and overflows under routine and extreme conditions 2. Methodological Framework This study's methodology is organized into two phases: In the first phase, basins or pipes are categorized based on GPAD and SSO frequency. GPAD serves as a relative measure of system usage or inflow/infiltration under peak-flow conditions, while SSOs reflect historical failure points. By comparing each basin's GPAD against a chosen threshold (commonly 5,000 GPAD) and mapping its overflow history, the study assigns every segment to one of four quadrants: - High GPAD, SSO: Requires immediate interventions (e.g., upsizing, inflow reduction). - High GPAD, No SSO: Indicates a near-capacity system needing proactive measures (cleaning, targeted repairs). - Low GPAD, SSO: Suggests localized defects or blockages correctable via cost-effective fixes (e.g., CCTV inspections). - Low GPAD, No SSO: Denotes basins with sufficient capacity and minimal overflow risk. After the quadrant classification, a second phase uses peak-flow modeling to validate and refine capacity assessments. This phase applies six distinct flow scenarios, each designed to capture different aspects of hydraulic performance under routine and stressed conditions. A 4Q Design Flow scenario-multiplying the CAR-derived Dry Weather Flow by four-establishes a moderate wet-weather baseline, while Q_Nominal GIS, TCEQ, and IDM Slope scenarios use Manning's equation with different slope inputs. Finally, Q_ICM Peak Network SA and Q_ICM Peak Isolated Basin employ a 2Y6H ICM simulation for system-wide versus localized performance assessments. Cross-referencing the quadrant classification with model outputs offers a deeper view of capacity constraints: High GPAD/High SSO segments that surcharge in multiple scenarios become immediate rehabilitation priorities, while adequate-capacity segments flagged as High GPAD/Low SSO can initially be managed with preventive measures. 3. Data Collection and Classification Houston is leveraging advanced technologies such as data integration, real-time monitoring, and predictive analytics through the Advanced Infrastructure Analytics Platform (AIAP), a 'Platform of Platforms' built on AWS Data Lake. The key data sources- ICM (Provides peak-flow data for pipes ≥10 inches under 2y6hr rainfall, HouCAT (Supplies localized flow estimates for pipes <10 inches), CAR (Uses Dry Weather Flow (DWF) from ESUs and water consumption to calculate 4Q design flow) are integrated from this platform including GIS to evaluate baseline (dry-weather) and peak-flow conditions across entire Houston's sewer network. Pipes are then classified into four risk quadrants (High GPAD/High SSO, High GPAD/Low SSO, Low GPAD/High SSO, Low GPAD/Low SSO) based on their flow and overflow characteristics. Once GPAD values are computed from high peak flows from outfall pipes for study areas, they are compared against a threshold-5,000 GPAD often signifies heightened capacity utilization or significant I&I infiltration. Meanwhile, SSO incidents are mapped geospatially to the pipe or basin level, creating a clear picture of historically weak points. This database-driven approach ensures that each pipe or basin can be rapidly classified into one of the four quadrants before proceeding to scenario-based modeling. 4. Scalability and Industry Application A key advantage of this two-tiered approach is its scalability. Smaller utilities with limited data might initially apply only the GPAD&SSO quadrant analysis for quick wins, discovering major problem areas without needing expensive hydraulic models. As data collection matures (e.g., from installing more flow monitors), they can add nominal flow calculations and eventually incorporate advanced ICM simulations. Larger utilities can embrace the full spectrum of scenarios from the outset, fully integrating results into existing Geographic Information Systems (GIS) and Computerized Maintenance Management Systems (CMMS). This automates data ingestion, allowing real-time updates to flow conditions, SSO occurrences, and quadrant classifications. This method is clear and actionable for industry professionals- basins or pipes that consistently rank high across multiple flow scenarios and fall into the 'High GPAD, High SSO' quadrant can be immediately prioritized in capital improvement plans (CIPs) or scheduled for in-depth inspections and flow monitoring. 5. Results A four-quadrant classification framework was applied to 52 critical basins, categorizing them based on GPAD and SSO. The analysis revealed: - High GPAD (>5,000) & SSO: 27% -high-priority areas for intervention - High GPAD (>5,000) & No SSO: 21%- indicating high inflow rates - Low GPAD & SSO: 15%- localized structural and maintenance issues - Low GPAD & No SSO: 4%- need minimal attention - Other/Uncategorized:33%- recommended for further investigation For high-priority basins, utilities implemented targeted rehabilitation measures such as slip-lining and manhole repairs, achieving a 30% reduction in SSOs within one year in some cases while extending asset life. Smaller municipalities adopted streamlined strategies, focusing on cost-effective solutions like targeted I&I mitigation and modest pipe upsizing to address capacity challenges. Scenario-based modeling validated the framework which basins truly approached or exceeded capacity under peak conditions. The synergy between the quadrant classification and multi-scenario modeling helped utilities pinpoint basins where capacity upgrades were urgent versus those needing localized structural fixes. 6. Conclusion and recommendations To tackle regulatory pressures, financial limitations, and the uncertainties of a changing climate, wastewater utilities need a forward-thinking, data-driven strategy. The combination of the GPAD&SSO quadrant framework and peak-flow modeling offers a reliable way to pinpoint high-risk areas and enhance system performance. Proactive planning for growth and shifting rainfall patterns is essential, as is focusing on high-risk areas to minimize costs and extend the lifespan of critical assets. Addressing SSO hotspots ensures compliance with regulations, while designing for peak flows strengthens resilience against extreme weather events. Strategic investments in key upgrades further enhance long-term efficiency and sustainability.