Structural Health Monitoring of Fatigue Cracks for Steel Bridges with Wireless Large-Area Strain Sensors.

This paper presents a field implementation of the structural health monitoring (SHM) of fatigue cracks for steel bridge structures. Steel bridges experience fatigue cracks under repetitive traffic loading, which pose great threats to their structural integrity and can lead to catastrophic failures. Currently, accurate and reliable fatigue crack monitoring for the safety assessment of bridges is still a difficult task. On the other hand, wireless smart sensors have achieved great success in global SHM by enabling long-term modal identifications of civil structures. However, long-term field monitoring of localized damage such as fatigue cracks has been limited due to the lack of effective sensors and the associated algorithms specifically designed for fatigue crack monitoring. To fill this gap, this paper proposes a wireless large-area strain sensor (WLASS) to measure large-area strain fatigue cracks and develops an effective algorithm to process the measured large-area strain data into actionable information. The proposed WLASS consists of a soft elastomeric capacitor (SEC) used to measure large-area structural surface strain, a capacitive sensor board to convert the signal from SEC to a measurable change in voltage, and a commercial wireless smart sensor platform for triggered-based wireless data acquisition, remote data retrieval, and cloud storage. Meanwhile, the developed algorithm for fatigue crack monitoring processes the data obtained from the WLASS under traffic loading through three automated steps, including (1) traffic event detection, (2) time-frequency analysis using a generalized Morse wavelet (GM-CWT) and peak identification, and (3) a modified crack growth index (CGI) that tracks potential fatigue crack growth. The developed WLASS and the algorithm present a complete system for long-term fatigue crack monitoring in the field. The effectiveness of the proposed time-frequency analysis algorithm based on GM-CWT to reliably extract the impulsive traffic events is validated using a numerical investigation. Subsequently, the developed WLASS and algorithm are validated through a field deployment on a steel highway bridge in Kansas City, KS, USA.

Soft Elastomeric Capacitor for Angular Rotation Sensing in Steel Components.

The authors have previously proposed corrugated soft elastomeric capacitors (cSEC) to create ultra compliant scalable strain gauges. The cSEC technology has been successfully demonstrated in engineering and biomechanical applications for in-plane strain measurements. This study extends work on the cSEC to evaluate its performance at measuring angular rotation when installed folded at the junction of two plates. The objective is to characterize the sensor's electromechanical behavior anticipating applications to the monitoring of welded connections in steel components. To do so, an electromechanical model that maps the cSEC signal to bending strain induced by angular rotation is derived and adjusted using a validated finite element model. Given the difficulty in mapping strain measurements to rotation, an algorithm termed angular rotation index (ARI) is formulated to link measurements to angular rotation directly. Experimental work is conducted on a hollow structural section (HSS) steel specimen equipped with cSECs subjected to compression to generate angular rotations at the corners within the cross-section. Results confirm that the cSEC is capable of tracking angular rotation-induced bending strain linearly, however with accuracy levels significantly lower than found over flat configurations. Nevertheless, measurements were mapped to angular rotations using the ARI, and it was found that the ARI mapped linearly to the angle of rotation, with an accuracy of 0.416∘.

Use of flexible sensor to characterize biomechanics of canine skin.

BACKGROUND: Suture materials and techniques are frequently evaluated in ex vivo studies by comparing tensile strengths. However, the direct measurement techniques to obtain the tensile forces in canine skin are not available, and, therefore, the conditions suture lines undergo is unknown. A soft elastomeric capacitor is used to monitor deformation in the skin over time by sensing strain. This sensor was applied to a sample of canine skin to evaluate its capacity to sense strain in the sample while loaded in a dynamic material testing machine. The measured strain of the sensor was compared with the strain measured by the dynamic testing machine. The sample of skin was evaluated with and without the sensor adhered. RESULTS: In this study, the soft elastomeric capacitor was able to measure strain and a correlation was made to stress using a modified Kelvin-Voigt model for the canine skin sample. The sensor significantly increases the stiffness of canine skin when applied which required the derivation of mechanical models for interpretation of the results. CONCLUSIONS: Flexible sensors can be applied to canine skin to investigate the inherent biomechanical properties. These sensors need to be lightweight and highly elastic to avoid interference with the stress across a suture line. The sensor studied here serves as a prototype for future sensor development and has demonstrated that a lightweight highly elastic sensor is needed to decrease the effect on the sensor/skin construct. Further studies are required for biomechanical characterization of canine skin.

Concrete Crack Detection and Monitoring Using a Capacitive Dense Sensor Array.

Cracks in concrete structures can be indicators of important damage and may significantly affect durability. Their timely identification can be used to ensure structural safety and guide on-time maintenance operations. Structural health monitoring solutions, such as strain gauges and fiber optics systems, have been proposed for the automatic monitoring of such cracks. However, these solutions become economically difficult to deploy when the surface under investigation is very large. This paper proposes to leverage a novel sensing skin for monitoring cracks in concrete structures. This sensing skin is constituted of a flexible electronic termed soft elastomeric capacitor, which detects a change in strain through changes in measured capacitance. The SEC is a low-cost, durable, and robust sensing technology that has previously been studied for the monitoring of fatigue cracks in steel components. In this study, the sensing skin is introduced and preliminary validation results on a small-scale reinforced concrete beam are presented. The technology is verified on a full-scale post-tensioned concrete beam. Results show that the sensing skin is capable of detecting, localizing, and quantifying cracks that formed in both the reinforced and post-tensioned concrete specimens.

An Experimental Study on Static and Dynamic Strain Sensitivity of Embeddable Smart Concrete Sensors Doped with Carbon Nanotubes for SHM of Large Structures.

The availability of new self-sensing cement-based strain sensors allows the development of dense sensor networks for Structural Health Monitoring (SHM) of reinforced concrete structures. These sensors are fabricated by doping cement-matrix mterials with conductive fillers, such as Multi Walled Carbon Nanotubes (MWCNTs), and can be embedded into structural elements made of reinforced concrete prior to casting. The strain sensing principle is based on the multifunctional composites outputting a measurable change in their electrical properties when subjected to a deformation. Previous work by the authors was devoted to material fabrication, modeling and applications in SHM. In this paper, we investigate the behavior of several sensors fabricated with and without aggregates and with different MWCNT contents. The strain sensitivity of the sensors, in terms of fractional change in electrical resistivity for unit strain, as well as their linearity are investigated through experimental testing under both quasi-static and sine-sweep dynamic uni-axial compressive loadings. Moreover, the responses of the sensors when subjected to destructive compressive tests are evaluated. Overall, the presented results contribute to improving the scientific knowledge on the behavior of smart concrete sensors and to furthering their understanding for SHM applications.

UAV rapidly-deployable stage sensor with electro-permanent magnet docking mechanism for flood monitoring in undersampled watersheds.

The availability of historical flood data is vital in recognizing weather-related trends and outlining necessary precautions for at-risk communities. Flood frequency, magnitude, endurance, and volume are traditionally recorded using established streamgages; however, the material and installation costs allow only a few streamgages in a region, which yield a narrow data selection. In particular, stage, the vertical water height in a water body, is an important parameter in determining flood trends. This work investigates a low-cost, compact, rapidly-deployable alternative to traditional stage sensors that will allow for denser sampling within a watershed and a more detailed record of flood events. The package uses a HC-SR04 ultrasonic sensor to measure stage, onboard memory for recording flood events, and an electropermanet magnet (EPM) to enable Unmanned Aerial Vehicle (UAV) deployments. Optional modules for solar panels and wireless communication can also be added to extend package longevity or allow wireless control of the EPM. The stage sensor package was found to have a range of 0.02 to 4 m with a 6.9 mm accuracy and capable of a 6.4 day long deployment. With the total cost of production at 271.37 USD, it is a cheaper and more flexible alternative to traditional stage sensors that will enable dense sensor networks and rapid response to flooding events.