Along with the accelerating urbanization and the increasing population density, large numbers of super high-rise buildings have been built around the world in the past decades. Together with the successive construction of these buildings, the structural safety issues have received more and more attention from all walks of life. During the operation period of super high-rise buildings, the long-term effects of environmental degradation and abnormal loads usually lead to the occurrence of damages in local areas of the building structure, which after long time accumulation, would inevitably cause the degradation of structural performance, or even structural failures, severely threatening life and property to the country and people. Therefore, various structural damage identification techniques are applied to conduct structural health monitoring (SHM) for super high-rise buildings that under construction and completed in order to discover possible structural damages in time, and carry out safety assessments with disaster warnings for possible dangers and unfavorable conditions of the structures for determination of reasonable and economic maintenance periods. This paper gives a brief review on the existing signal processing techniques used for damage identification and SHM of super high-rise buildings, and further summarizes the future research trend of these techniques.
The load-carrying properties of super-long pile were important to bridge engineering in construction and operation phases, however, the research of its load-carrying properties was imperfect. The wireless monitoring system of super-long pile foundation in bridge engineering was presented in this paper. The intelligent vibrating wire steel stress gauges were used to reflect the axial stress of pile foundation, then the sensor data was acquired using wireless intelligent acquisition instrument. The LoRaWAN was used to establish the local area network, which was long range wide area network. Thus the monitoring data of each sensor was collected by Lora modules. Furthermore, the collected data was uploaded to the cloud platform using 4G wireless communication technology. Finally, the wireless monitoring system was applied in actual bridge engineering. The results indicated that the change of monitoring data was synchronized with the progress of actual working conditions, which could reflect the dynamic process of construction to a certain extent. It could provide an important reference for similar projects in the future.
Wireless intelligent health monitoring has been widely used in civil engineering. However, ill-conditioned data could be generated due to the vulnerability to external interference sometimes. The ill-conditioned data have great influence on damage identification and condition assessment. Hence, the prediction method of monitoring data based on data correlation was presented in this paper. The correlation degree between multi-channels data was established by BP neural network. Then the ill-conditioned data was predicted and corrected by the correlation degree between the data, and verified by the measured data. The results indicated that high accuracy and engineering requirement could be achieved using BP neural network prediction method considering the correlation degree between multi-channels data.
To overcome the disadvantages of poor adaptability and weak systematicity of the existing indoor environment monitoring instrument, a set of indoor environment monitoring system with dual data transmission mode based on the Interconnect of Things is designed in this paper, which can monitor seven different related parameters, i.e., fusion CO2, formaldehyde, volatile organic compounds, particulate matter, temperature, humidity and noise. Besides, it designs dual mode switch between GSM wireless upload and WiFi wireless upload to enhance the environmental adaptability of the monitor. It was demonstrated that, by combining with the he Internet of Things technology, the system works well to incorporate monitoring, assessment and early warning.
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