Reliability assessment of guyed transmission towers through active learning metamodeling and progressive collapse simulation

This manuscript presents a novel approach to the reliability of guyed transmission line towers (TLTs) considering complete geometric and material non-linear analyses, bolt slippage effects, and progressive collapse simulation, using active learning Kriging to handle the computational burden. In the existing literature, such issues have been considered separately, with the main focus on self-supporting towers. Herein, novelty arises from the combined consideration of the above and the addressing of guyed TLTs. Yet, because the wind speed variability is much higher than those related to the mechanical model, a modified initial sampling plan strategy is successfully proposed to reduce the computational time required. The application case-study addressing long transmission lines in midwest Brazil is also relevant, considering the recent updating of design wind speeds, with increases of up to 25%. Failure probabilities obtained using code resistance models indicate the need for tower retrofitting. However, the more accurate assessment via collapse simulation shows that the support is still safe, even for the updated wind speeds. Results demonstrate the accuracy and efficiency of the proposed analysis framework and show the importance of using accurate numerical models in retrofit evaluations.