Group Method of Data Handling as a Tool to Determine Vertical Displacements

3rd World Multidisciplinary Civil Engineering, Architecture, Urban Planning Symposium

土木建筑工程;文学

Mrowczynska, Maria^1 ; Sztubecki, Jacek^2

Department of Land and Environment Engineering, Institute of Civil Engineering, University of Zielona Gora, prof. Z.Szafrana 1, Zielona Gora

65-516, Poland^1

Faculty of Civil and Environmental Engineering and Architecture, University of Science and Technology, prof. S.Kaliskiego 7, Bydgoszcz

85-796, Poland^2

关键词: Error of methods;    Geodetic monitoring;    Geometric modeling;    Group method of data handling;    Multiple inputs;    Optimal architecture;    Static neural networks;    Vertical displacements;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/471/5/052077/pdf
DOI  :  10.1088/1757-899X/471/5/052077

学科分类：土木及结构工程学

来源: IOP

【 摘 要 】

Surveying measurements carried out to determine displacements and deformations of existing civil structures and their surroundings provide information making it possible to represent their geometry in space and any changes it undergoes over time. Data acquired through geodetic monitoring can be modelled using artificial neural networks, capable of learning (adaptability) and quick operation and providing the possibility of visualisation by means of computer simulation. Neural networks, however, require specification of an optimal architecture by the user, as a result of which any resulting solutions are flawed by a difficult to identify error of method. Therefore, this article proposes an alternative approach in the form of the Group Method of Data Handling (GMDH) based on evolutionary algorithms. The article presents the fundamental assumptions for the GMDH and the principles of development and training of static neural networks with multiple inputs and one output. The GMDH network was used to develop a geometric model of vertical displacements determined on the basis of periodic measurements taken on civil structures.