Damage Detection in Bolted Connections of Power Transmission Towers Using Machine Learning-Based Methods (Bagging Trees)

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Taghikhany Touraj; Binavayan Alireza

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Year:2024 | Volume:53 | Issue:4 Page(s): 53-64

Download Full-Text

Persian Verion

View:

Download:

Cites:

Information Journal Paper

Title

Damage Detection in Bolted Connections of Power Transmission Towers Using Machine Learning-Based Methods (Bagging Trees)

Author(s)

Taghikhany Touraj | Binavayan Alireza | Issue Writer Certificate

Keywords

power transmission towersQ4

splice connectionsQ4

Structural health monitoringQ4

natural frequenciesQ4

Mode shapesQ4

Abstract

There have been many studies regarding the health monitoring and detection of transmission towers, among which we can refer to the article by Yen et al. in 2009, who investigated damage detection in transmission towers with a limited number of sensors those modal parameters were obtained from the measured environmental vibration data. In their research, mode shape and frequency were used to identify the system. Also, stiffness reduction was also suggested to damage detection. Structural health monitoring by using features such as natural frequencies and Mode shapes has attracted the attention of a number of researchers. Damage detection in power transmission towers using these features and using soft computing methods was also considered by Skarbek et al. in 2014. They have investigated frequency damage indicators by simple processing and using neural network for power transmission tower. In 2019, Zhao et al obtained the natural frequency of the 110 kV power transmission tower using the sub-random space method and found that the detected natural frequency is dependent on the wind speed. However, the current article tries to damage detection in the connections due to the lack of access to real data and experimental data and based on numerical modeling of a limited number of possible damages that occurred in the legs of the splice connection of power transmission tower.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops