SewerNet Optimizer™ implements a set of revolutionary algorithms to support optimal renewal planning and achieve maximum return on investment in terms of improving network-level condition and reducing the risk of failure. The algorithms include inductive multi-variate deterioration modeling, risk assessment and prioritization, and multi-objective optimization. SewerNet Optimizer™ offers a rich set of features unparalleled by any other software in the market today.
Optimal Long-Term Renewal Plans
SewerNet Optimizer™ allows users to develop optimized network-level renewal plans under a range of scenarios, including: (1) scenarios to evaluate the impact of funding levels on network condition and risk levels; and (2) scenarios to evaluate funding requirements to achieve desired condition or risk objectives. Asset managers can define scenario parameters such as planning horizon, funding profile, budget splitting among renewal methods, annual inflation rate, etc. For each defined scenario, the software generates optimal and feasible project lists, on annual basis, where selected projects and are guaranteed to be optimal and to satisfy all defined constraints. Trade-off analysis functions (e.g., queries, charting functions, etc.) will enable asset managers to assess the consequences of various scenarios and policies on the condition and risk levels of the sewer network. This analysis will help engineers make rational and evidence-based decisions to determine the most cost-effective long-term renewal plan.
Reliable Prediction of Sewer Structural Deterioration
SewerNet Optimizer™ implements a revolutionary multi-variate inductive algorithm to accurately predict sewer deterioration by correlating a range of physical and performance variables and structural Condition Grades (SCG). Unlike other statistical approaches such as regression, Markov chain, or cohort survival modeling, which assume that the deterioration process follows a predefined “known” distribution function, our algorithm does not assume any “prior” knowledge of the deterioration function. Instead, it analyzes historical inspection data and correlates a range of independent physical and operational variables (e.g., age, diameter, material, depth, soil type, traffic loading, etc.) with sewers SCG to automatically “infer” (or “discover”) the distribution function that most closely captures the relationship between these variables and SCGs, and then use this function to predict future SCG of individual sewers. The algorithm is also resilient to possible data impreciseness typically associated with CCTV inspections and condition rating processes.
Unlike the commonly used condition-based (or worst-first) approach, SewerNet Optimizer™ implements a risk-based prioritization model that balances the need to address sewer condition state (or likelihood of failure) and the consequence of failure (or criticality). Risk is assessed to reflect sewers inadequacy to meet desired performance levels. For each sewer, a “risk index” is calculated as the product of the consequence of failure and the likelihood of failure. Forecasted SCGs are used as a proxy for likelihood of failure. Consequence of failure is determined by evaluating a set of rules and weights that involve static or time-dependent risk factors (e.g., sewer size and location, hydraulic criticality, and structural vulnerability due to traffic loading and soil characteristics,). Calculated risk indices are then used to prioritize sewers to reflect urgency of renewal actions.
Comprehensive Renewal Methods Database
SewerNet Optimizer™ maintains a built-in database of sewer renewal technologies, including open-cut replacement, trenchless replacement (e.g., pipe bursting, horizontal directional drilling, micro-tunneling), and trenchless lining methods (CIPP, slip lining, spiral wound, formed in place, etc.). The database also defines a set of customizable constraints and formulae for calculating costs and benefits, as well as to determine applicability criteria of each method. Applicability criteria can include information on sewer material, diameter, condition rating, soil type, and ground water level. Users can customize the database to reflect utility-specific policy constraints and best practices.
Advanced Data Analytics
SewerNet Optimizer™ maintains a centralized enterprise database to integrate sewer inventory and geospatial data, CCTV inspections and condition rating, hydraulic model data, and renewal planning data. The software offers data management services, and supports import/export functions from/to virtually all commonly used file formats. It also implements advanced analytics functions to support efficient query and manipulation of the data using a user-friendly Query Builder. Sewer groups can be easily created, stored, and analyzed. Users can use a wide range of charting functions (boxplots, scatter plots, histograms, and pie charts) to efficiently investigate data trends and relationships. Queries and charts can be created for individual sewers, sewer groups, or the entire sewer network. Historical data trends can be identified using linear, polynomial, or exponential curve fitting functions.
SewerNet Optimizer™ can be easily customized to reflect utility-specific data, renewal policies, planning constraints and scenarios, and various forms of required presentations and reporting. It can seamlessly integrate and exchange data with most commonly used software including ArcGIS, work management systems (e.g., CityWorks), CCTV inspection software (e.g., WinCan), and hydraulic modeling software (e.g., EPA SWMM).