KEYWORDS: Wireless sensors, Energy efficiency, System identification, Batteries, Sensor networks, Sensors, Structural health monitoring, Mode shapes, Machine learning, Solar energy, Bridges, Power consumption
The battery-powered wireless sensor network (WSN) is a promising solution for structural health monitoring (SHM) applications because of its low cost and easy installation capability. However, the long-term WSN operation suffers from various concerns related to uneven battery degradation of wireless sensors, associated battery management, and replacement requirement, and ensuring desired quality of service (QoS) of the WSN in practice. The battery life is one of the biggest limiting factors for long-term WSN operation. Considering the costly maintenance trips for battery replacement, a lack of effective battery degradation management at the system level can lead to a failure in WSN operation. Moreover, the QoS needs to be ensured under various practical uncertainties. Optimal selection with a maximal number of nodes in WSN under uncertainties is a critical task to ensure the desired QoS. This study proposes a reinforcement learning (RL) based framework for active control of the battery degradation at the WSN system level with the aim of the battery group replacement while extending the service life and ensuring the QoS of WSN. A comprehensive simulation environment was developed in a real-life WSN setup, i.e. WSN for a cable-stayed bridge SHM, considering various practical uncertainties. The RL agent was trained under a developed RL environment to learn optimal nodes and duty cycles, meanwhile managing battery health at the network level. In this study, a mode shape-based quality index is proposed for the demonstration. The training and test results showed the prominence of the proposed framework in achieving effective battery health management of the WSN for SHM.
Steel bridges are susceptible to fatigue damage under traffic loading, and many bridges operate with existing cracks. The discovery and long-term monitoring of those fatigue cracks are critical for safety evaluations. In previous studies, the ability of the soft elastomeric capacitor (SEC) sensor that measures large-area strain was validated for detecting and monitoring fatigue crack growth in a laboratory environment. In this study, the performance of the technology is evaluated for field applications, for which an approach for long-term monitoring of fatigue cracks is developed. The approach consists of an integrated system, termed the wireless large-area strain sensors (WLASS), for wireless data collection and storage and a signal processing algorithm for monitoring fatigue cracks with bridge response induced by traffic loading. In particular, the WLASS consists of soft elastomeric capacitors (SECs) combined with sensor boards to convert capacitance to a measurable change in voltage and a wireless sensing platform equipped with event-triggered sensing, wireless data collection, cloud storage, and remote data retrieval. A modified crack growth index (CGI) is developed through detection of peak-to-peak amplitudes of the wavelet transform. Using the measurements from the WLASS, the modified CGI is able to obtain the crack status under various loading events due to random traffic loads. The performance of the developed approach is validated using a steel highway bridge.
A capacitance based large-area electronics strain sensor, termed soft elastomeric capacitor (SEC) has shown various advantages in infrastructure sensing. The ability to cover large area enables to reflect mesoscale structural deformation, highly stretchable, easy to fabricate and low-cost feature allow full-scale field application for civil structure. As continuing efforts to realize full-scale civil infrastructure monitoring, in this study, new sensor board has been developed to implement the capacitive strain sensing capability into wireless sensor networks. The SEC has extremely low-level capacitance changes as responses to structural deformation; hence it requires high-gain and low-noise performance. For these requirements, AC (alternating current) based Wheatstone bridge circuit has been developed in combination a bridge balancer, two-step amplifiers, AM-demodulation, and series of filtering circuits to convert low-level capacitance changes to readable analog voltages. The new sensor board has been designed to work with the wireless platform that uses Illinois Structural Health Monitoring Project (ISHMP) wireless sensing software Toolsuite and allow 16bit lownoise data acquisition. The performances of new wireless capacitive strain sensor have been validated series of laboratory calibration tests. An example application for fatigue crack monitoring is also presented.
Distortion-induced fatigue cracks caused by differential deflections between adjacent girders are common issues for steel girder bridges built prior to the mid-1980s in the United States. Monitoring these fatigue cracks is essential to ensure bridge structural integrity. Despite various level of success of crack monitoring methods over the past decades, monitoring distortion-induced fatigue cracks is still challenging due to the complex structural joint layout and unpredictable crack propagation paths. Previously, the authors proposed soft elastomeric capacitor (SEC), a large-size flexible capacitive strain sensor, for monitoring in-plane fatigue cracks. The crack growth can be robustly identified by extracting the crack growth index (CGI) from the measured capacitance signals. In this study, the SECs are investigated for monitoring distortion-induced fatigue cracks. A dense array of SECs is proposed to monitor a large structural surface with fatigue-susceptible details. The effectiveness of this strategy has been verified through a fatigue test of a large-scale bridge girder to cross-frame connection model. By extracting CGIs from the SEC arrays, distortion-induced fatigue crack growth can be successfully monitored.
KEYWORDS: Wind measurement, Structural health monitoring, Sensors, Visual optics, Sensor performance, Cameras, Image processing, Position sensors, Imaging systems, Video acceleration, Bridges, Optical filters, Information operations, Aerodynamics, Data modeling, Data acquisition
Dynamic displacement is one of the most important measurands in wind tunnel tests of structures. Laser sensors or optical sensors are usually used in wind tunnel tests to measure displacements. However, these commercial sensors have limitations in its use, cost and installation despite of their good performance in accuracy. RINO (Real-time Image- processing for Non-contact monitoring), an iOS software application for dynamic displacement monitoring, has been developed in the previous study. In this study, feasibility of RINO in practical use for wind tunnel tests is explored. Series of wind tunnel tests show that performances of RINO are comparable with those of conventional displacement sensors.
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