MATEC Web Conf.
Volume 262, 201964 Scientific Conference of the Committee for Civil Engineering of the Polish Academy of Sciences and the Science Committee of the Polish Association of Civil Engineers (PZITB) (KRYNICA 2018)
|Number of page(s)||6|
|Section||Engineering of Building Enterprises|
|Published online||30 January 2019|
The use of evolutionary algorithms for designing an optimum structure of a geodesic measurement and control network
University of Zielona Góra, Faculty of Civil Engineering, Architecture and Environmental Engineering, ul. Prof. Z. Szafrana 1, 65-516 Zielona Góra, Poland
2 University of Science and Technology, Faculty of Civil and Environmental Engineering and Architecture, ul. Prof. S. Kaliskiego 7, 85-796 Bydgoszcz, Poland
* Corresponding author: firstname.lastname@example.org
The paper presents an attempt to determine an optimum structure of a geodesic measurement and control network used for geodesic monitoring to determine horizontal displacements of buildings. In geodesy, horizontal networks can be used to determine terrain deformations as well as displacements of engineering structures (dams, water reservoirs, open-cast mines). The network subjected to analysis is a directional network. In order to find a correct solution, its structure should include so-called supernumerary observations. An adequate number of observations should be carried out in the network to obtain a solution with reliable values of horizontal displacements. Moreover, the way in which the observations are carried out and their number should make it possible to show changes taking place in the object and meet the economic criteria of geodesic measurements. In order to optimize the structure of a geodesic measurement and control network, information entropy and evolutionary algorithms are used in the paper. Information entropy is a logarithmic measure of probability, and an optimum number of observations carried out in the network depends on the increment of the content of information in the observation system. Evolutionary algorithms were developed in the 1980s, and they are currently very popular and widely used. Their main principle is based on the evolution or behaviour of the best adapted individuals in subsequent computational cycles.
© The Authors, published by EDP Sciences, 2019
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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