Abstract Manager

Effective infrastructure asset management requires comprehensive, accurate condition data to support evidence-based decision-making throughout a structure's lifecycle. Traditional bridge inspection methods, while valuable, face inherent limitations in defect quantification, repeatability, and data accessibility for longitudinal analysis. Point-in-time observations documented in text-based reports can make it challenging to track deterioration rates, compare discrete condition changes across inspection cycles, and prioritize maintenance investments based on objective metrics.

Digital photogrammetry and AI-powered defect mapping are transforming how engineers assess and manage bridge infrastructure. High-resolution 3D models provide permanent digital records of asset condition, enabling precise measurement and quantification of cracks, spalls, and delamination. These models serve as powerful visualization and analysis platforms that support multi-year deterioration tracking, facilitate collaborative engineering reviews, and provide the data foundation for predictive maintenance strategies. When combined with drone-based data collection, these technologies also address accessibility challenges for remote assets and hard-to-reach structural components, expanding the scope of what can be comprehensively inspected.

This presentation explores the integration of drone-based photogrammetry, AI-powered defect detection, and acoustic sensing technologies to compliment traditional bridge inspection practices. Through two distinct case studies; the Deh Cho Bridge in the Northwest Territories and the Hudson Hope Suspension Bridge in northern BC, we demonstrate how digital inspection methods deliver comprehensive, repeatable, and safe condition assessments while supporting Canada's infrastructure resilience.

The Deh Cho Bridge, a critical year-round link on the Yellowknife Highway replacing seasonal ferry crossings, shows the efficiency of digital methods in remote environments. A comprehensive drone-based inspection captured high-resolution imagery of major structural elements, with AI-powered defect detection identifying defects. The 206-metre Hudson Hope Suspension Bridge inspection integrated photogrammetric 3D modeling with a breakthrough technology: drone-equipped acoustic sensors for delamination detection in hard to access subdeck areas. 

The methodology introduces several advances for Canadian bridge management. Digital models enable precise year-over-year comparison for deterioration tracking and data-driven maintenance planning, while web-based visualization platforms provide permanent high-resolution records accessible to multidisciplinary teams.