Development of Low-Cost Wireless Sensing System for Smart Ultra-High Performance Concrete.

This study proposes the development of a wireless sensor system integrated with smart ultra-high performance concrete (UHPC) for sensing and transmitting changes in stress and damage occurrence in real-time. The smart UHPC, which has the self-sensing ability, comprises steel fibers, fine steel slag aggregates (FSSAs), and multiwall carbon nanotubes (MWCNTs) as functional fillers. The proposed wireless sensing system used a low-cost microcontroller unit (MCU) and two-probe resistance sensing circuit to capture change in electrical resistance of self-sensing UHPC due to external stress. For wireless transmission, the developed wireless sensing system used Bluetooth low energy (BLE) beacon for low-power and multi-channel data transmission. For experimental validation of the proposed smart UHPC, two types of specimens for tensile and compression tests were fabricated. In the laboratory test, using a universal testing machine, the change in electrical resistivity was measured and compared with a reference DC resistance meter. The proposed wireless sensing system showed decreased electrical resistance under compressive and tensile load. The fractional change in resistivity (FCR) was monitored at 39.2% under the maximum compressive stress and 12.35% per crack under the maximum compressive stress tension. The electrical resistance changes in both compression and tension showed similar behavior, measured by a DC meter and validated the developed integration of wireless sensing system and smart UHPC.

Damage Localization and Severity Assessment of a Cable-Stayed Bridge Using a Message Passing Neural Network.

Cable-stayed bridges are damaged by multiple factors such as natural disasters, weather, and vehicle load. In particular, if the stayed cable, which is an essential and weak component of the cable-stayed bridge, is damaged, it may adversely affect the adjacent cables and worsen the bridge structure condition. Therefore, we must accurately determine the condition of the cable with a technology-based evaluation strategy. In this paper, we propose a deep learning model that allows us to locate the damaged cable and estimate its cross-sectional area. To obtain the data required for the deep learning training, we use the tension data of the reduced area cable, which are simulated in the Practical Advanced Analysis Program (PAAP), a robust structural analysis program. We represent the sensor data of the damaged cable-stayed bridge as a graph composed of vertices and edges using tension and spatial information of the sensors. We apply the sensor geometry by mapping the tension data to the graph vertices and the connection relationship between sensors to the graph edges. We employ a Graph Neural Network (GNN) to use the graph representation of the sensor data directly. GNN, which has been actively studied recently, can treat graph-structured data with the most advanced performance. We train the GNN framework, the Message Passing Neural Network (MPNN), to perform two tasks to identify damaged cables and estimate the cable areas. We adopt a multi-task learning method for more efficient optimization. We show that the proposed technique achieves high performance with the cable-stayed bridge data generated from PAAP.

SSVM: An Ultra-Low-Power Strain Sensing and Visualization Module for Long-Term Structural Health Monitoring.

Structural health monitoring (SHM) is crucial for quantitative behavioral analysis of structural members such as fatigue, buckling, and crack propagation identification. However, formerly developed approaches cannot be implemented effectively for long-term infrastructure monitoring, owing to power inefficiency and data management challenges. This study presents the development of a high-fidelity and ultra-low-power strain sensing and visualization module (SSVM), along with an effective data management technique. Deployment of 24-bit resolution analog to a digital converter and precise half-bridge circuit for strain sensing are two significant factors for efficient strain measurement and power management circuit incorporating a low-power microcontroller unit (MCU), and electronic-paper display (EPD) enabled long-term operation. A prototype for SSVM was developed that performs strain sensing and encodes the strain response in a QR code for visualization on the EPD. For efficient power management, SSVM only activated when the trigger-signal was generated and stayed in power-saving mode consuming 18 mA and 337.9 µA, respectively. The trigger-signal was designed to be generated either periodically by a timer or intentionally by a push-button. A smartphone application and cloud database were developed for efficient data acquisition and management. A lab-scale experiment was carried out to validate the proposed system with a reference strain sensing system. A cantilever beam was deflected by increasing load at its free end, and the resultant strain response of SSVM was compared with the reference. The proposed system was successfully validated to use for long-term static strain measurement.