Leveraging AI, Cloud Technology, and Advanced Analytics for Sewer Condition Assessment and System Management
Publication: Pipelines 2024
ABSTRACT
The use of Artificial Intelligence (AI), Machine Learning (ML), and other advanced analytical tools has already made a profound impact on the ability to collect condition data on gravity sewers and verify its integrity more cost effectively with elevated quality standards than traditional processing techniques. The most prominent tool utilized to date has been Automated Defect Recognition (ADR) which has enabled attaining increased quality in programmed SCA far more efficiently than traditional methods. Recent advancements in cloud computing enable the use of ADR at scale, drastically reducing the amount of time that lapses from CCTV inspection to utilize the data for informed business decisions even when presented with large volumes of data. When coupled with defect cluster analysis tooling, configured to match defect patterns to suggested rehabilitation techniques, the process directly results in relating observed condition to their capital cost ramifications. Intelligent use of ADR has also facilitated accessing large volumes of legacy data from programmed CCTV work with no coding to uncoded inspections from routine maintenance. The collection of sewer condition data in conjunction with age, era, and other readily available exposure data also allows the development of deterioration models that provide considerable insight into the manner and rate of degradation for various cohorts throughout the system. The combination of spatial and temporal knowledge enables the use of other advanced modeling tools, such as Genetic Algorithms, Monte Carlo Simulation, and other advanced analytical techniques to provide Asset Managers with consummate answers to relate how much is spent, on what, over what time frame, and what is the resulting benefit or risk involved.
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REFERENCES
Kleiner, Y. (2001). “Scheduling Inspection and Renewal of Large Infrastructure Assets”, ASCE Journal of Infrastructure Systems, Volume 7, Issue 4, December 2001.
Macey, C. C., and Croft, B. (2017). “Using Risk Models and Automated Defect Characterization Algorithms to convert PACP™ data into Capital Upgrading Programs for ALCOSAN” published in the proceedings of UESI Pipelines 2017.
Macey, C. C., and Davidson, J. (2023). “Using Artificial Intelligence and other Advanced Analytics for Program Optimization and Long-Term Capital Planning of Gravity Sewer Collection Systems”, in the proceedings of No-Dig North, October 2023.
Vollertsen, J., et al. (2011). “Modeling the corrosion of concrete sewers”, 12th International Conference on Urban Drainage, Porto Alegre/Brazil, 11-16 September 2011.
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Published online: Aug 30, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Business management
- Computer networks
- Computer programming
- Computing in civil engineering
- Data collection
- Decision making
- Decision support systems
- Defects and imperfections
- Engineering fundamentals
- Infrastructure
- Lifeline systems
- Materials characterization
- Materials engineering
- Methodology (by type)
- Practice and Profession
- Research methods (by type)
- Sewers
- Systems engineering
- Systems management
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