A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Automated Condition Assessment of Sanitary Sewer Pipelines
Advancements in optical sensors and computer technologies have paved the way for the development of innovative underground utility inspection systems. Some of these developments include those inspection technologies that require no human entry and those having possibilities to be fully automated from data acquisition to data interpretation. This paper describes the framework for an automated assessment system for sanitary sewer infrastructure. The proposed system enables fast and accurate assessment, which is significant in building a sewer condition database for asset management. The inspection system obtains optical data from the Sewer Scanner and Evaluation Technology (SSET) which is currently the leading edge technology in sanitary sewer inspection. The data is then analyzed and to assess the condition of the pipe. Artificial neural networks and fuzzy logic systems are integrated with concepts of pattern recognition and learning capabilities. Multiple neural networks are developed to classify the pipe defect features and a fuzzy logic system suggested to filter and fusion the multiple neural network outputs.
Automated Condition Assessment of Sanitary Sewer Pipelines
Advancements in optical sensors and computer technologies have paved the way for the development of innovative underground utility inspection systems. Some of these developments include those inspection technologies that require no human entry and those having possibilities to be fully automated from data acquisition to data interpretation. This paper describes the framework for an automated assessment system for sanitary sewer infrastructure. The proposed system enables fast and accurate assessment, which is significant in building a sewer condition database for asset management. The inspection system obtains optical data from the Sewer Scanner and Evaluation Technology (SSET) which is currently the leading edge technology in sanitary sewer inspection. The data is then analyzed and to assess the condition of the pipe. Artificial neural networks and fuzzy logic systems are integrated with concepts of pattern recognition and learning capabilities. Multiple neural networks are developed to classify the pipe defect features and a fuzzy logic system suggested to filter and fusion the multiple neural network outputs.
Automated Condition Assessment of Sanitary Sewer Pipelines
Chae, Myung Jin (author) / Abraham, Dulcy M. (author)
Eighth International Conference on Computing in Civil and Building Engineering (ICCCBE-VIII) ; 2000 ; Stanford, California, United States
2000-08-04
Conference paper
Electronic Resource
English
Automated Condition Assessment of Sanitary Sewer Pipelines
| British Library Conference Proceedings | 2000
Structural Condition Assessment of Sewer Pipelines
| Online Contents | 2010