Health monitoring of ultra high fiber performance reinforced concrete communication tower using machine learning algorithms

Saleem, Sarah; Hejazi, Farzad; Ostovar, Nima

Abstract

Within the last decades, the needed for communication towers has accelerated with the requirements for effective communication, especially for radio, radar, and television. The complexity configuration of the tower and limit access to the structure body especially inner part of the tower with hollow section is led the health monitoring of tower as the main challenging issue to maintenance during its function. The change of natural frequencies can be considered as one of the prevalent damage detection methods in structural assessment procedures. Therefore, the main aim of present research is to develop health monitoring system for Ultra High Fiber Performance Reinforced Concrete (UHPFRC) communication tower based on frequency domain response. Since the frequency data of tower is mostly noisy and interpreting of frequency in different modes in variant case of tower damage. The hybrid algorithm based on the Adaboost, Bagging and RUSBoost algorithms are implemented to identify the damage in the UHPFRC communication tower using frequency domain data. The training samples for the algorithm are obtained from a finite element simulation and full-scale experiment testing is also performed to generate the testing samples. The finite element simulation dynamic frequency results are verified through conducting a full-scale experimental test on 30 m height UHPFRC communication tower. For this propose, frequency Response Functions (FRF’s), for healthy and damaged structures were obtained by exciting of tower by an impact hammer and the acceleration response recorded by three accelerometers sensors attached in suitable positions. The developed hybrid algorithm to identifying the damage is tested and verified by considering the part of tower segments 2–3 and conducting experimental testing on the healthy structure as well as a damaged structure which caused using dynamic actuator. The testing results proved the accuracy of the developed optimized hybrid algorithm to identify damage in the tower structure in variant condition.

Citation

Saleem, S., Hejazi, F., & Ostovar, N. (2023). Health monitoring of ultra high fiber performance reinforced concrete communication tower using machine learning algorithms. Journal of Civil Structural Health Monitoring, 13(4-5), 1105-1130. https://doi.org/10.1007/s13349-023-00688-3

• Journal Article Type: Article
• Acceptance Date: Mar 21, 2023
• Online Publication Date: Apr 24, 2023
• Publication Date: Jun 30, 2023
• Deposit Date: Apr 27, 2023
• Publicly Available Date: Oct 18, 2023
• Journal: Journal of Civil Structural Health Monitoring
• Print ISSN: 2190-5452
• Electronic ISSN: 2190-5479
• Publisher: Springer (part of Springer Nature)
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Issue: 4-5
• Pages: 1105-1130
• DOI: https://doi.org/10.1007/s13349-023-00688-3
• Keywords: Health Monitoring, Communication Tower, Vibration, Ultra High Performance Fiber Concrete, Dynamic Load
• Public URL: https://uwe-repository.worktribe.com/output/10716532
• Publisher URL: https://link.springer.com/article/10.1007/s13349-023-00688-3