Stability Analysis and Fault Detection of Telecommunication Towers Using Decision Tree Algorithm Under Wind Speed Condition

This paper presents a decision tree (DT) modeling technique to estimate any increase in the load on telecommunication towers. A structural analysis was done for the lattice and mono-pole towers using TNX Tower software to determine the basic features of the towers, such as tilt angle, deflection, tw...

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Bibliographic Details
Published in2022 4th Novel Intelligent and Leading Emerging Sciences Conference (NILES) pp. 45 - 49
Main Authors Samir, Raghda, Elhakim, Yasmin, Adel, Salsabil, Samy, Irene, Ismail, Tawfik
Format Conference Proceeding
LanguageEnglish
Published IEEE 22.10.2022
Subjects
Communications technology
Decision Tree
Fault Detection
Lattice
Machine Learning
Machine learning algorithms
Monopole
Poles and towers
Prediction algorithms
Software algorithms
Stability analysis
Telecom tower
TNX Tower
Wind speed
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Summary:This paper presents a decision tree (DT) modeling technique to estimate any increase in the load on telecommunication towers. A structural analysis was done for the lattice and mono-pole towers using TNX Tower software to determine the basic features of the towers, such as tilt angle, deflection, twist, and acceleration. The structure analysis generated a data set based on wind speeds. This data set was then used to train a machine-learning algorithm to estimate the loads on the structure. Any change in the applied loads greater than the loads considered in the design might be identified using such a predictor, allowing appropriate action to be taken in response. Finally, a classification is performed with the support of DT to keep track of the current condition of the tower and determine whether or not the applied load estimated by the algorithm is safe for the tower structure.
DOI:10.1109/NILES56402.2022.9942414