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The assembled tube-gusset K-joint by bolts is a commonly used connection form in steel tubular transmission towers. At present, main existing research or design codes for steel tubular transmission towers regard this K-joint as either rigid or pinned connections, which do not consider the semi-rigid behavior of K-joint. In this paper, the semi-rigid behavior of K-joint in steel tubular transmission towers is investigated and a direct prediction (DP) method is proposed to evaluate the semi-rigid behavior of K-joints based on the support vector regression (SVR) model, especially to predict the moment–rotation curve of semi-rigid K-joints. First, the establishment and validation of the finite element (FE) model of semi-rigid K-joints are conducted. Second, a dataset of 144 samples generated by the FE model is used to train and test the SVR model. Finally, the accuracy assessment of the proposed DP method and comparison with other existing methods, including the Kishi–Chen model, EC3 model and ANN-based two-step prediction method, are presented. The accuracy assessment shows that predicted values of the proposed DP method based on the SVR model exhibit good agreement with the numerical analysis values, which indicates the quite high accuracy of this method. Additionally, the comparison reveals that the proposed DP method based on the SVR model for predicting moment–rotation curves is rather more accurate than other aforementioned methods. Therefore, the proposed DP method based on the SVR model is of high reliability in predicting the semi-rigid behavior of K-joints in steel tubular transmission towers, which affords an alternative way for further engineering analysis and initial design purposes.

The transmission tower is an important infrastructure for transmission lines. To secure the operation of the power grid, it is particularly important to evaluate the safety of the in-service transmission tower under the action of random wind loads throughout their entire life cycle. Thus, this paper firstly establishes the time-varying equivalent performance function of the in-service transmission tower under the action of random wind loads. Then, in order to address the shortcomings of the traditional maximum entropy method, the high-order moments-based improved maximum entropy method (HM-IMEM) is proposed and extended to assess the wind resistance global reliability of the in-service transmission tower. Finally, the effectiveness of the proposed method is demonstrated evaluating the wind resistance global reliability of an in-service transmission tower in an engineering setting. Analytic results indicate that: (1) The proposed method can ensure a balance between calculation accuracy and efficiency. Compared with Monte Carlo simulation (MCS) method, the relative error is only 0.11% and the computational cost is much lower than that of the MCS method. (2) The reliability of the in-service transmission tower significantly decreased over time. In order to guide maintenance and reinforcement by predicting the time-varying performance of in-service transmission towers, it is of great engineering value to evaluate the wind resistance global reliability of the in-service transmission tower.

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