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
Related Articles
- EP ID EP685946
- DOI -
- Views 122
- Downloads 0
Low Cost Temperature and Humidity Estimator with Atmega8 Microcontroller
This paper presents a efficient but also cost effective system to determine two parameters of the atmosphere, which are the temperature and humidity. The Digital Temperature and Humidity Sensor 22 DHT 22 is used to measu...
Performance Analysis of MIMO System on Different Modulation Scheme over Rician Channel
The wireless communications is one of the most active areas of technology development of our time. Recently, orthogonal frequency division multiplexing has been regarded as one of the core technologies for various wirele...
Internet of Things Insights of Applications in Research and Innovation to Integrated Ecosystem
In existing world IOT find a great attention from researchers, it becomes an vital technology that offers a well defined communications between objects and machines. That will offer immediate access to information about...
Experimental Behaviour of Steel Fiber Reinforced Concrete with partial Replacement of Fly Ash and Metakaolin
Concrete is generally used due to the abundance of uncooked material, low manufacturing and protection cost, excellence in compression, corrosion aspects, and versatility in forming more than a few shapes and its limitle...
Accessing the Internet Through Light Using LiFi
LiFi is a short form for Light Fidelity. This concept came in the picture during 2011 in a TED talk by Harald Hass, who is a German physicist. Because of him we came to know the concept of accessing the internet through...