Abstract
With the advancement of society, the significance of Artificial Intelligence (AI) and big data in facilitating the intelligent development of bridges is increasing. Bridges function as crucial urban links, and ensuring their safe and stable operation is paramount. Therefore, this paper proposes an Artificial Intelligence enabled self-powered sensing wind energy harvesting system (WEHS) designed specifically for bridges. The system mainly consists of two electromagnetic generators (EMG1 and EMG2) and a rolling ball triboelectric nanogenerator (RB-TENG). WEHS achieves the recycling of ambient wind energy through the lower electromagnetic generator (EMG2). A single-channel triboelectric nanogenerator (RB-TENG) enables the sensing of wind speed within the system, while the top electromagnetic generator (EMG1) utilizes a single magnet with coils of different diameters to realize wind direction sensing. Reynolds wind tunnel that the WEHS has an average power output of 392 mW and a power density of 414.86 W/m3, which is adequate to power the sensor. The accuracy of wind speed and direction prediction based on the Artificial Intelligence GRU deep learning algorithm model is 96 % and 99.04 %, respectively. Furthermore, by implementing deep learning models for wind speed and direction, it becomes feasible to forecast the variations in wind direction and speed in the vicinity of bridges. The WEHS exhibits significant potential for bridges application and can contribute to developing AIoT-based smart bridges.
Published Version
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