ID:
publications-4135
Type:
article
Year:
2009
Authors:
Ho, Cheng-I and Ho, Cheng-I and Lin, Minβ€Der and Lin, Min-Der and Lin, Min-Der and Lo, Shangβ€Lien and Lo, Shang-Lien and Lo, Shang-Lien
Title:
Prioritizing pipe replacement in a water distribution system using a seismic-based artificial neural network model.
Venue/Journal:
Environmental Engineering Science
DOI:
10.1089/ees.2008.0057
Research type:
Water System:
Technical Focus:
Abstract:
Abstract This work focused on developing an approach for prioritizing the order of pipe replacement in a water distribution system (WDS) using a seismic-based artificial neural network (ANN). The qualified earthquake data obtained from the Taiwan Water Corporation Leakage Repair Management System (TWC-LRMS) were classified to build the model that was analyzed by both backward propagation network (BPN) and radial basis function network (RBFN). Pipe diameter, pipe material, and the number of monthly magnitude-3+ earthquakes provide the input parameters of the seismic-based ANN model for anticipating the priority of pipe replacement. The WDS of Yilan County, which frequently suffers from earthquakes in northeastern Taiwan, was used as the object of the case study. A comparison of the accuracy and reliability of the prediction model between BPN and RBFN demonstrated that RBFN outperformed BPN. The seismic-based ANN model developed in this work is streamlined for establishing a priority project of pipe replace...
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