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.
