A Method for Estimating the Diameter of Water Pipes Using Artificial Neural Networks of the Multilayer Perceptron Type
- DOI
- 10.2991/icaita-18.2018.13How to use a DOI?
- Keywords
- water distribution system; hydraulic calculations; diameter selection; artificial neural networks; classification
- Abstract
Designing a water distribution system is inherently associated with hydraulic calculations, the primary purpose of which is to select the diameters of water pipelines. Computer programs may choose diameters, but most often, this task is up to the designer. It is necessary to control flow velocity; at the same time, however, it is also necessary to minimise pressure losses in the water mains network. In order to improve the above process, an artificial neural network was designed, which, after hydraulic calculations, evaluates the accuracy of the diameters selected on a classification basis. The output layer of the neural network consists of ten neurons corresponding to the nominal diameters of the water pipes. Classification of the correct pipeline diameter is done using "one-of-N" encoding, that is, only one neuron from the output layer is activated, thus selecting the correct diameter. Based on data from hydraulic calculations, the neural network diagnoses the diameter of the pipelines along individual sections of the water supply network and proposes appropriate values or accepts the existing ones.
- Copyright
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Jacek Dawidowicz PY - 2018/03 DA - 2018/03 TI - A Method for Estimating the Diameter of Water Pipes Using Artificial Neural Networks of the Multilayer Perceptron Type BT - Proceedings of the 2018 2nd International Conference on Artificial Intelligence: Technologies and Applications (ICAITA 2018) PB - Atlantis Press SP - 50 EP - 53 SN - 1951-6851 UR - https://doi.org/10.2991/icaita-18.2018.13 DO - 10.2991/icaita-18.2018.13 ID - Dawidowicz2018/03 ER -