Sieci neuronowe w diagnostyce betonowych nawierzchni lotniskowych

Małgorzata Linek; Piotr Nita

doi:10.7409/rabdim.022.005

Published: 2022-03-31

Vol. 21 No. 1 (2022)

Neural networks in diagnostics of concrete airfield pavements

Małgorzata Linek , Piotr Nita

Section: Articles

https://doi.org/10.7409/rabdim.022.005

Abstract

Concrete airfield pavement maintenance encompasses many complex problems, which are difficult to identify using traditional diagnostic methods. Artificial neural networks may prove useful in understanding and solving of such problems. The article presents the nature of neural networks and the possible fields of their application in analysis of processes occurring in airfield surface layers and base layers during service. The presented concepts include the use of neural networks in repair prediction, identification of causes of the observed phenomena and diagnostic predictions for future maintenance and service. The aim of the work is to apply artificial neural networks to modeling of maintenance processes, including prediction of pavement evenness. A neural network model was prepared for assessment of pavement evenness based on data obtained from real pavement sections. Research methodology and the obtained field results were described. The structure of the neural network was designed and verified. Conclusions were formulated regarding suitability of neural modeling for pavement evenness prediction. The proposed methodology may complement the methods currently used in pavement diagnostics.

Keywords:

airfield pavements, artificial neural networks, concrete pavement diagnostics, concrete pavements, pavement evenness.

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Linek, M., & Nita, P. (2022). Neural networks in diagnostics of concrete airfield pavements. Roads and Bridges - Drogi I Mosty, 21(1), 81–97. https://doi.org/10.7409/rabdim.022.005

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Małgorzata Linek

linekm@tu.kielce.pl

Affiliation:

Kielce University of Technology, Faculty of Civil Engineering and Architecture, 7 Tysiąclecia Państwa Polskiego Av., 25-314 Kielce

Piotr Nita