Statistical methodologies for removing the operational effects from the dynamic responses of a high‐rise telecommunications tower. Issue 4 (29th January 2021)

• Record Type: Journal Article
• Authors: Ribeiro, Diogo; Leite, Jorge; Meixedo, Andreia; Pinto, Nuno; Calçada, Rui; Todd, Michael
• Is Part Of: Structural control and health monitoring. Volume 28:Issue 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-01-29
• Subjects: autoregressive model -- continuous monitoring system -- crest factor -- operational effects -- principal component analysis -- telecommunications tower; Structural engineering -- Periodicals; Structural control (Engineering) -- Periodicals; Automatic data collection systems -- Periodicals; Detectors -- Periodicals; 624.17
• DOI: 10.1002/stc.2700
• Languages: English
• ISSNs: 1545-2255

Abstract

Summary: This paper describes statistical methodologies for removing the influence of operational effects from the dynamic responses of a telecommunications tower. The characterization of the dynamic responses of the structure, over a period of 3 months, was based on a continuous monitoring system that included accelerometers, anemometers and a meteorological station. The analysis of the results allowed identifying a significant number of critical events, for which the dynamic response under wind action is significantly amplified, as well as sporadic events, associated with high peak acceleration values, due to the influence of operational effects related to the movement of the lift, technical staff, and equipment. The automatic identification of critical events, based on extreme acceleration values, required the prior removal of operational effects from the records using two distinct methodologies, one based on the principal component analysis (PCA) and the other based on the crest factor (CF) and on autoregressive models (AR). Both methodologies showed efficiency and robustness in eliminating acceleration peaks due to operational effects; however, the methodology based on the CF and AR models proved to be computationally more efficient and resulted on a smaller number of false‐positive occurrences in the identification of critical events. The developed methodologies showed potential to be integrated in Structural Health Monitoring (SHM) systems to assess the structural safety and serviceability of telecommunications towers.