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Volume 20, Issue 42 (4-2024)
Marine Engineering 2024, 20(42): 13-23 |
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Gharechae A. Utilizing the method of random composition of incomplete noisy data in the health monitoring of structures using the frequency response function. Marine Engineering 2024; 20 (42) :13-23
URL: http://marine-eng.ir/article-1-1090-en.html
URL: http://marine-eng.ir/article-1-1090-en.html
Chabahar Maritime University
Abstract: (676 Views)
Health monitoring and damage detection of structures have always been one of the concerns of engineers and researchers. In the meantime, some structures are of vital importance at the national level of countries and their health should be evaluated periodically. In structural health monitoring, field measurement data of the existing structure is used to find the location and extent of failures. Field data are collected by installing measuring equipment in a limited number of structural degrees of freedom. These data are always associated with a percentage of error or noise. In this research, the ability of the random composition of measurement data in structural damage detection has been evaluated using the frequency response function. For this purpose, the developed method was applied to a high-voltage power transmission tower in different damage scenarios. The results show the high ability of this method to predict the amount and location of the damage in the presence of noisy data. When the amount of noise in the data is less than 5%, the results of the above method are completely consistent with the previous conventional methods. When up to 35% of the noise is spread in less than 50% of the data, while conventional methods fail to predict the amount and position of the damages, the present method detects the position and amount of damage with high accuracy. Numerical examples using noisy data confirm that the proposed method can be used in all methods of damage detection in structures.
Type of Study: Research Paper |
Subject:
Marine Structures and near shore
Received: 2023/12/25 | Accepted: 2024/04/8
Received: 2023/12/25 | Accepted: 2024/04/8
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