Bearing Capacity of Transmission Poles under Combined Wind and Rain Excitations Based on the Deep Learning Method

Abstract

Generally, only wind effect is considered in the collapse evaluation of transmission poles (TPs). However, during a typhoon landing, the rainfall is usually very heavy and the influence of the wind and rainfall should be considered simultaneously. To estimate the bearing capacity of TPs accurately during a typhoon, this paper proposes a method for predicting the water content distribution of soil based on the deep learning method. Then, the earth pressure distribution related to the rainfall is obtained. Furthermore, according to the static balance principle, the ultimate overturning moment of the TPs is obtained, and the corresponding critical wind speed is determined via a numerical simulation. Finally, a case study is tested and the collapse evaluation of TPs under the combined effects of wind and rainfall is obtained. The results show that the critical wind speed when considering rainfall is less than the value when ignoring the rainfall effect. The traditional method overestimates the structural safety of the TPs because the rainfall has a significant reduction on the bearing capacity of TPs.

A Data-Mining Interpretation Method of Pavement Dynamic Response Signal by Combining DBSCAN and Findpeaks Function

“…This will have an adverse effect on driving comfortability and minimize the service life of asphalt pavements. In previous research [1][2][3][4], some researchers employed macroscopic approaches to assess pavement performance under traffic loads. For instance, a combination of high-definition cameras and digital-image processing methods can be employed to detect pavement damage and assess surface texture.…”