Obtaining Modal Parameters in Steel Model Bridge by System Identification using Artificial Neural Networks
Journal Title: International Journal of Trend in Scientific Research and Development - Year 2020, Vol 4, Issue 2
Abstract
Artificial Neural Networks are easy to build and take good care of large amounts of noisy data. They are especially suitable for the solution of nonlinear problems. They work well for problems where domain experts arent available or there are no known rules. Artificial Neural Networks can also be adapted to civil engineering structures and suffer from dynamic effects. Structures around the world were badly damaged by the earthquake. Thus, loss of life and property was experienced. This particularly affected countries on active fault lines. Pre and post earthquake precautions have been developed in the world. For these reasons, it is necessary to determine the dynamic performance of structures in the world. There are several methods to determine dynamic performance. System identification is one of these methods. The mathematical model of the structural system is obtained by system identification method. Artificial Neural Networks ANN is a system identification method. ANN can adapt to their environment, work with incomplete information, make decisions under uncertainties and tolerate errors. Steel Model Bridge was used in this study. The system identification of the steel model bridge with the ANN method of 0.90 was made successfully. As a result of this study, ANN approach can provide a very useful and accurate tool to solve the problem in modal identification studies. Hakan Aydin ""Obtaining Modal Parameters in Steel Model Bridge by System Identification using Artificial Neural Networks"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30013.pdf Paper Url : https://www.ijtsrd.com/engineering/civil-engineering/30013/obtaining-modal-parameters-in-steel-model-bridge-by-system-identification-using-artificial-neural-networks/hakan-aydin
Authors and Affiliations
Hakan Aydin
Keywords
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