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Structural-response reconstruction is of great importance to enrich monitoring data for better understanding of the structural operation status. In this paper, a data-driven based structural-response reconstruction approach by generating response data via a convolutional process is proposed. A conditional generative adversarial network (cGAN) is employed to establish the spatial relationship between the global and local response in the form of a response nephogram. In this way, the reconstruction process will be independent of the physical modeling of the engineering problem. The validation via experiment of a steel frame in the lab and an in situ bridge test reveals that the reconstructed responses are of high accuracy. Theoretical analysis shows that as the sensor quantity increases, reconstruction accuracy rises and remains when the optimal sensor arrangement is reached.

Under the trend of the rapid development of the internet of things (IoT), sensing for dynamic behaviors is widely needed in many fields such as traffic management, industrial production, medical treatment, building health monitoring, etc. Due to the feature of power supply independence and excellent working performance under a low-frequency environment, triboelectric nanogenerators (TENGs) as sensors are attracting more and more attention. In this paper, a comprehensive review focusing on the recent advance of TENGs as sensors for dynamic behaviors is conducted. The structure and material are two major factors affecting the performance of sensors. Different structure designs are proposed to make the sensor suitable for different sensing occasions and improve the working performance of the sensors. As for materials, new materials with stronger abilities to gain or lose electrons are fabricated to obtain higher surface charge density. Improving the surface roughness of material by surface engineering techniques is another strategy to improve the output performance of TENG. Based on the advancement of TENG structures and materials, plenty of applications of TENG-based sensors have been developed such as city traffic management, human–computer interaction, health monitoring of infrastructure, etc. It is believed that TENG-based sensors will be gradually commercialized and become the mainstream sensors for dynamic sensing.