This paper describes a computing backend for a water pipe monitoring system. Today, most such systems are divided into event-triggered and continuous monitoring, but they all lack systematic handling of data. Many systems simply store data in files with specific naming conventions and ad hoc formats, making them difficult to retrieve, maintain, disseminate, and analyze. To address these problems, our backend supports data management and dissemination. Unlike previous systems that store data in files or conventional databases before analysis, our modular architecture not only saves data in efficiently searchable ways by indexing as a baseline dataset but also detected events in discrete time manner and other processed data. To facilitate analysis, we design a plug-in structure to allow processing modules to perform inline processing and shorten detection time. For data dissemination, our architecture can compose multiple visualizations including geographical maps to create powerful tools to yield new insight into massive datasets. The backend system enables Internet web service for visualization, data management, and remote sensor control for better integration. Our system is applicable to not only water pipelines but also bridges and civil structures in general. Our proposed backend system has been implemented and validated through field deployment. One such system has been running for over 1.5 years and has collected millions of records to date. A Google Map integrated visualization service has been developed to demonstrate lively collected records in real-time. This is expected to be more helpful for better understanding of civil structures’ behavior in the long term.
The purpose of this study is the remote structural health monitoring to identify the torsional natural frequencies and mode shapes of a concrete cable-stayed bridge using a hybrid networking sensing system. The system consists of one data aggregation unit, which is daisy-chained to one or more sensing nodes. A wireless interface is used between the data aggregation units, whereas a wired interface is used between a data aggregation unit and the sensing nodes. Each sensing node is equipped with high-precision MEMS accelerometers with adjustable sampling frequency from 0.2 Hz to 1.2 kHz. The entire system was installed inside the reinforced concrete box-girder deck of Hwamyung Bridge, which is a cable stayed bridge in Busan, South Korea, to protect the system from the harsh environmental conditions. This deployment makes wireless communication a challenge due to the signal losses and the high levels of attenuation. To address these issues, the concept of hybrid networking system is introduced with the efficient local power distribution technique. The theoretical communication range of Wi-Fi is 100m. However, inside the concrete girder, the peer to peer wireless communication cannot exceed about 20m. The distance is further reduced by the line of sight between the antennas. However, the wired daisy-chained connection between sensing nodes is useful because the data aggregation unit can be placed in the optimal location for transmission. To overcome the limitation of the wireless communication range, we adopt a high-gain antenna that extends the wireless communication distance to 50m. Additional help is given by the multi-hopping data communication protocol. The 4G modem, which allows remote access to the system, is the only component exposed to the external environment.
This paper discusses issues of using wireless sensor systems to monitor structures and pipelines in the case of disastrous
events. The platforms are deployed and monitored remotely on lifetime systems, such as underground water pipelines. Although
similar systems have been proposed for monitoring seismic events and the structure health of bridges and buildings,
several fundamental differences necessitate adaptation or redesign of the module. Specifically, rupture detection in water
delivery networks must respond to higher frequency and wider bandwidth than those used in the monitoring of seismic
events, structures, or bridges. The monitoring and detection algorithms can also impose a wide range of requirements on
the fidelity of the acquired data and the flexibility of wireless communication technologies. We employ a non-invasive
methodology based on MEMS accelerometers to identify the damage location and to estimate the extent of the damage.
The key issues are low-noise power supply, noise floor of sensors, higher sampling rate, and the relationship among displacement,
frequency, and acceleration.
Based on the mentioned methodology, PipeTECT, a smart wireless sensor platform was developed. The platform was
validated on a bench-scale uniaxial shake table, a small-scale water pipe network, and portions of several regional water
supply networks. The laboratory evaluation and the results obtained from a preliminary field deployment show that such
key factors in the implementation are crucial to ensure high fidelity of the acquired data. This is expected to be helpful in
the understanding of lifeline infrastructure behavior under disastrous events.