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Kohonen Artificial Networks for the Verification of the Diameters of Water-pipes

Dawidowicz Jacek, Czapczuk Andrzej, Kruszyński Wojciech

Rocznik Ochrona Środowiska

2021

Tom 23

835--844

The design of the water distribution system is inherently linked to the execution of calculations, which aim, among other things, to determine the flow rate through individual pipes and the selection of diameters at the appropriate speed. Each step in the calculations is followed by an evaluation of the results and, if necessary, a correction of the data and further calculations. It is up to the designer to analyse the accuracy of the calculation results and is time-consuming for large systems. In this article, a diagnostic method for the results of hydraulic calculations, based on Kohonen Network, which classifies nominal diameters [DN] on the basis of data, in the form of flows, has been proposed. After calculating the new variant of the water distribution system, the individual calculation sections are assigned to the neurons of the topological map of Kohonen Network drawn up for nominal diameters. By comparing the diameter used for the calculation, with the diameter obtained on the topological map, the accuracy of the chosen diameter can be assessed. The topological map, created as a result of labelling the neurons of the output layer of the Kohonen Network, graphically shows the position of the classified diameter, relative to those diameters with similar input values. The position of a given diameter, relative to other diameters, may suggest the need to change the diameter of the pipe.

Zastosowanie sztucznej sieci neuronowej Kohonena do monitorowania stanu infrastruktury drogowej

Mazurek G.

Logistyka

2015

nr 4

9466--9473, CD3

W artykule został wykorzystany algorytm samoorganizującej sieci Kohonena. Do analizy wybrano losowo sześć odcinków drogowych o identycznym natężeniu pojazdów, obszaru zagospodarowania oraz organizacji ruchu. Uwzględniając liczebność zbioru zastosowano mapę skupień o rozmiarach 1x3. Analiza typologiczna ujawniła zróżnicowane wzorce powstawania uszkodzeń w nawierzchni. Ponadto jej wynik jest efektywnym narzędziem wymagającym monitorowanie odcinków dróg silnie wytężonych.

In article was used an algorithm of self-organising Kohonen Network. Six road sections were choosed which characterized similar input data for example traffic volume, vehicle load range. In the experiment was considered an agglomeration map with dimension 1x3. An analysis revealed a few patterns refered to road damages. Moreover received results are good supporting tool in road monitoring.