Structural health monitoring is of great significance to ensure the safety of marine pipes, while powering the required monitoring sensors remains a problem because the ocean environment is not amenable to the traditional ways of providing an external power supply. However, mechanical energy due to the vortex-induced vibration of pipelines may be harvested to power those sensors, which is a convenient, economic and environmentally friendly way. We here exploit a contact-separation mode triboelectric nanogenerator (TENG) to create an efficient energy harvester to transform the mechanical energy of vibrating pipes into electrical energy. The TENG device is composed of a tribo-pair of dielectric material films that is connected to a mass-spring base to guarantee the contact-separation motions of the tribo-pair. Experimental tests are conducted to demonstrate the output performance and long-term durability of the TENG device by attaching it to a sample pipe. A theoretical model for the energy harvesting system is developed for predicting the electrical output performance of the device. It is established that the normalized output power depends only on two compound variables with all typical factors taken into consideration simultaneously. The simple scale law is useful to reveal the underlying mechanism of the device and can guideline the optimization of the device based on multi-parameters analyses. The results here may provide references for designing contact-mode TENG energy harvesting devices based on the vibration of marine pipes and similar structures.

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.

Computer vision-based structural deformation monitoring techniques were studied in a large number of applications in the field of structural health monitoring (SHM). Numerous laboratory tests and short-term field applications contributed to the formation of the basic framework of computer vision deformation monitoring systems towards developing long-term stable monitoring in field environments. The major contribution of this paper was to analyze the influence mechanism of the measuring accuracy of computer vision deformation monitoring systems from two perspectives, the physical impact, and target tracking algorithm impact, and provide the existing solutions. Physical impact included the hardware impact and the environmental impact, while the target tracking algorithm impact included image preprocessing, measurement efficiency and accuracy. The applicability and limitations of computer vision monitoring algorithms were summarized.

No abstract

The timely identification of differential settlement of track foundations is of great significance for the safety of train operation and the maintenance of track structures. However, traditional monitoring techniques cannot meet the requirements of efficient, real-time, and automatic monitoring of track foundation settlement. In order to solve these problems, a real-time identification method based on a gated recurrent unit (GRU) neural network is proposed for the differential settlement of track foundations monitoring. According to parameter sensitivity analysis, the vertical acceleration of the vehicle is selected as the known data fed into the GRU network for differential settlement identification. Then the GRU network is employed to establish the nonlinear relationship between the vertical acceleration of the vehicle and the differential settlement of the track foundation. The results indicate that the longitudinal continuous differential settlement distribution curve of track foundations could be accurately identified with GRU neural network through the real-time vibration response of the vehicle–track. The current method may provide a new means for the real-time and efficient identification of the differential settlement of track foundations.