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WASTEWATER
Eyes Underground
Cities are using AI, acoustic sensors, and integrated data systems to uncover
hidden risks, optimize budgets, and keep wastewater systems 昀氀owing
BY CORINNE LYNDS
Getty Images
O
UT OF SIGHT doesn’t mean
out of mind—not when a
hidden crack can cost millions
overnight.
Across Canada, wastewater
infrastructure is aging fast, and climate
pressures are only making things worse.
Sudden pipe failures, combined sewer
overflows (CSOs), and environmental
spills aren’t just costly, they’re increasingly
common. To get ahead of these
underground surprises, municipalities are
turning to new inspection tools, ranging
from smart sensors and acoustic devices
to GIS-integrated asset management and
early-stage real-time reporting systems.
These technologies, and the growing
datasets behind them, are transforming
how cities monitor and maintain their
wastewater networks. The goal? Better
visibility, smarter decisions, and fewer
emergency excavations.
According to the Canadian Infrastructure Report
Card, nearly 40 per cent of wastewater infrastructure
is in “fair to very poor” condition, highlighting the
urgency of preventative maintenance. Whether
powered by AI or enabled by smart planning, new
approaches are helping cities improve service, reduce
risk, and stretch limited budgets further.
Here’s how four Canadian cities—Edmonton,
Calgary, Hamilton, and Toronto—are using
modern inspection technologies to better
understand what’s happening underground, before
problems rise to the surface.
Corinne Lynds is Vice President,
Content and Partnerships at
Actual Media, and Editor of
Water Canada.
WAT E R C A N A D A . N E T
EDMONTON
AI-powered CCTV analysis for smarter pipe assessment
Technology providers: EPCOR internal development
Project status: Ongoing since 2018
Application: Automated defect classification, asset condition
prioritization
IN EDMONTON, EPCOR and the City’s Drainage Services
team are rethinking how inspections are done—starting
with artificial intelligence. Since 2018, their closed-circuit
television (CCTV) inspection program has evolved to
include automated defect classification based on PACP
(Pipeline Assessment Certification Program) standards (see
sidebar: What is PACP?).
Traditionally, CCTV inspection footage was reviewed
manually by trained technicians, a time-consuming
process subject to human fatigue and variation in
judgment. Now, an AI-powered video analytics system
processes the footage, frame by frame, to identify and
categorize potential defects such as cracks, root intrusions,
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