Background/Objectives
Urban sewer pipelines, crucial for environmental protection, are nearing the end of their lifespan in both developed and developing regions. This situation poses significant challenges for asset owners in terms of budgeting for necessary repairs. The American Society of Civil Engineers (ASCE) rates this critical infrastructure at a D+, indicating poor condition. Furthermore, the impact of climate change, including natural disasters, is expected to exacerbate the vulnerability of sewer systems, accelerating deterioration and affecting their resilience. This presentation aims to address these challenges by exploring advanced methods to assist engineers in maintaining the structural integrity and operational efficiency of sewer systems in the face of climate impacts.
Approach/Activities
This study focuses on the application of artificial intelligence (AI) in sewer asset management. Despite AI's long-standing presence, its recent applications in infrastructure management are pioneering. The presentation will explore AI's role in enhancing sewer system resilience, with a particular emphasis on machine learning, computer vision, optimization, and expert systems. These technologies are employed to predict sewer conditions, guide optimized intervention strategies, and expedite the sewer inspection process for improved decision-making and aligning with the ASCE Vision of 2025 that focuses on digitization and advanced systems.
Results/Lessons Learned
The implemented work demonstrates significant improvements over manual processes in assessing sewers and developing capital improvement plans. A case study will be presented highlighting the effectiveness of AI in sewer asset management, showing how it enables the integration of asset management to improve climate change adaptation. The results indicate that AI can empower asset owners and engineers to make informed decisions, thereby maintaining critical infrastructure and designing resilient systems to face future challenges.
