Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/32186
Title: PROGNOSIS ANALYSIS OF ARCH DAM BEHAVIOR BY HYBRID MODEL
Authors: Frosina Panovska, Stevcho Mitovski, Ljupcho Petkovski
Keywords: arch dam, numerical model, FEM, SOFiSTiK, neural network, prognosis modelling
Issue Date: 2022
Publisher: Gdańsk University of Technology Publishing House, Gdańsk 2022
Conference: 17th International symposium Water Management and Hydraulic Engineering –WMHE 2022
Abstract: The assessment of the structural stability and behavior of the dam during construction, at full reservoir and during the service period is of paramount importance for such structures. Each dam, in dependence of the type and dimensions, has installed system for technical monitoring that enables tracking of the dam behavior and assessment of the dam state throughout registration and interpretation of various data such as displacements, stresses, seepage etc. The case study is double curvature concrete arch dam, with asymmetric shape due to the valley topography. In the paper are systemized acknowledgments for the comparison of the recorded data from technical monitoring of the dam and output results from spatial (3D) numerical model, created by application by application of SOFiSTiK code, based on Finite Element Method. In addition, a Neural Network model is created by recorded data from the technical monitoring. The both models create the so called hybrid model. For both models are used input parameters such as variation of water level in the reservoir and air temperature, and the output results are compared with recorded values for water level in piezometers at specified nodes of the dam. The aim of the task is to compare and calibrate the output results from the both models and recorded values from the technical monitoring of the dam and to carry out prognosis modeling for the future behavior of the dam.
URI: http://hdl.handle.net/20.500.12188/32186
Appears in Collections:Faculty of Civil Engineering: Conference papers

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