Automated Detection and Classification of Infiltration in Sewer Pipes-Reference-Cited by-同舟云学术

Automated Detection and Classification of Infiltration in Sewer Pipes

Published:2005-09 Issue:3 Volume:11 Page:165-171
ISSN:1076-0342
Container-title:Journal of Infrastructure Systems
language:en
Short-container-title:J. Infrastruct. Syst.

Author:

Shehab Tariq¹²,Moselhi Osama¹²

Affiliation:

1. Assistant Professor, Dept. of Civil Engineering and Construction Engineering Management, California State University, Long Beach, CA 90840,formerly, Post-Doctoral Fellow, Dept. of Building, Civil and Environmental Engineering, Concordia Univ., Montreal PQ, Canada H3G 1M8

2. Professor and Chair, Dept. of Building, Civil and Environmental Engineering, Concordia Univ., Montreal PQ, Canada H3G 1M8.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%291076-0342%282005%2911%3A3%28165%29

Reference31 articles.

1. Abraham D. Chae M. and Gokhale S. (2000). “Utilizing neural networks for condition assessment of sanitary sewer infrastructure.” Proc. 17th Int. Conf. on Robotics and Automation in Construction International Association for Automation and Robotics in Construction (IAARC) Taipei Taiwan 423–427.

2. N0-dig gains ground;Aguiar L.;Civ. Eng. (N.Y.),1996

3. Intelligent Vision Systems for Industry

4. Neuro-Fuzzy Approaches for Sanitary Sewer Pipeline Condition Assessment

5. Collins C. (1998). “Image classification using neural network technology.” Proc. Symp. on Applications of Neural Networks Institute Fuer Neuroinfomatik Weinheim Kiel Germany 201–212.

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