Abstract
In order to assess the water pipe condition without excavating, artificial neural network (ANN) model was developed and applied to real-world case in South Korea. for the input in this ANN model, 11 factors such as (1) pipe material, (2) diameter, (3) pressure head, (4) inner coating, (5) outer coating, (6) electric recharge, (7) bedding condition, (8) age, (9) trench depth, (10) soil condition, and (11) number of road lanes were used; and, for the output, overall pipe condition index was derived based on 5 factors such as (1) outer corrosion, (2) crack, (3) pin hole, (4) inner corrosion, and (5) H-W C value. for the ANN computing, each factor was normalized into the range of 0 to 1. the ANN model could find better results than those of multiple regression model in terms of statistical correlation between observed and computed data. © 2007 ASCE.
Recommended Citation
Z. W. Geem et al., "Trenchless Water Pipe Condition Assessment using Artificial Neural Network," Pipelines 2007: Advances and Experiences with Trenchless Pipeline Projects - Proceedings of the ASCE International Conference on Pipeline Engineering and Construction, p. 26, American Society of Civil Engineers (ASCE), Nov 2007.
The definitive version is available at https://doi.org/10.1061/40934(252)26
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-078440934-3
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 American Society of Civil Engineers (ASCE), All rights reserved.
Publication Date
27 Nov 2007
