Ingestion, SQL QA, risk scoring, lifecycle cost analysis, capital allocation, and figure generation are connected through one pipeline.
PROJECT 12 / Infrastructure Analytics · Asset Management
Wastewater Infrastructure Analytics
A reproducible wastewater asset-management and capital-planning framework that connects utility data ingestion, SQL quality assurance, transparent risk scoring, lifecycle cost analysis, and constrained investment prioritization.
DATASETWastewater asset-management schema and synthetic CI fixture
SIZESynthetic validation fixture · utility-ready asset schema
MEASURED EVIDENCE
What can be verified.
Metrics and outputs drawn from the project artifacts—not estimates added for presentation.
$1M, $5M, $10M, and $25M planning budgets are configured for transparent portfolio testing.
Likelihood and consequence scores are combined with an explicit criticality multiplier rather than a hidden model score.
GitHub Actions runs unit tests and the complete wastewater pipeline against a synthetic asset fixture.
VALIDATION
The public repository includes unit tests, SQL quality checks, a synthetic smoke-test dataset, and GitHub Actions that execute the complete pipeline. The evidence validates reproducibility and workflow behavior; no utility-specific performance or capital outcome is claimed without verified source data.
PLAIN-LANGUAGE INTERPRETATION
Risk scores and funding scenarios are planning-support outputs. They are not regulatory determinations, certified condition assessments, failure probabilities, or final engineering cost estimates.
Why this project matters
Wastewater utilities often manage condition, inspection, work-order, GIS, cost, and criticality information across separate systems. The analytical challenge is to turn those disconnected records into a defensible view of which assets deserve engineering attention first and how limited capital can be allocated without hiding assumptions inside a black-box score.
What was developed
The project builds a configuration-driven Python and SQL pipeline that ingests asset records, preserves identifiers, validates structural quality, calculates likelihood-of-failure and consequence-of-failure risk with an explicit criticality multiplier, develops lifecycle cost scenarios, tests capital portfolios across multiple budget levels, and produces reviewer-ready outputs. Assumptions live in YAML configuration files so scoring and funding logic remain visible and adjustable.
What it means
The current repository delivers a tested end-to-end decision-support architecture rather than claiming utility-specific findings before real operational data are connected. It demonstrates how wastewater asset information can move from raw records to quality-controlled risk rankings, planning-level lifecycle economics, and transparent capital scenarios while keeping engineering judgment and data limitations explicit.
04 / WHERE THIS WORK APPLIES
From project to practical use.
The framework can support wastewater utilities, public works departments, consulting engineers, asset-management teams, capital-program offices, and infrastructure funding programs working with gravity sewers, force mains, manholes, pump stations, treatment assets, CCTV inspections, CMMS histories, and rehabilitation or replacement planning.
05 / LIMITATION
What the project does not solve yet.
The public project currently uses a synthetic fixture to validate the workflow. Utility-specific condition, failure probability, service consequence, project readiness, and cost conclusions require verified GIS, CMMS, CCTV, inspection, hydraulic, and financial data before they should guide real capital decisions.
06 / NEXT ITERATION
Future advancement
Connect verified utility GIS, CMMS, CCTV/PACP, work-order, hydraulic, overflow, and cost data; calibrate likelihood and consequence models with local history; add geospatial and network context; improve project bundling and dependency logic; and validate capital recommendations with engineering and operations stakeholders.
TECHNICAL TOOLKIT
PythonSQLPandasSQLiteYAMLMatplotlib