Summary This article describes the experimental evaluation of the dynamic effects induced by wind on a high‐rise telecommunications tower based on a permanent monitoring system. Monte da Virgem telecommunications tower is located near the city of Porto (Portugal), and its structure consists in a reinforced concrete shaft and a steel mast, with a total height of 177 m. The monitoring system includes accelerometers, anemometers, and a meteorological station, allowing the characterization of the maximum accelerations of the structure and wind regimes during a period of 6 months. The analysis of the results enabled identifying specific events, denominated as critical events, for which the dynamic response of the tower under wind actions appears significantly amplified due to wind aeroelastic instability phenomena in the steel mast. The automatic identification of the critical events was based on the application to the acceleration's records of an autoregressive model and estimation of its optimal order number based on a singular value decomposition. The results proved the robustness and efficiency of the proposed technique in identifying the number, duration, and maximum amplitude of accelerations associated to the critical events, envisaging its potential integration in structural health monitoring systems.

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.

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Abstract. This paper is focused on the experimental calibration of the numerical model of a stand in

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