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.
Bolted steel joints are one of the most common types of connections in steel structures. Due to significant loads carried over long-term operation, bolted steel joints are prone to structural damage. Monitoring bolted steel joints is critical to ensure their functionality and structural safety. Among all factors related with joint damage, bolt loosening has been reported as a main cause of the damage of bolted joints. Detecting bolt loosening is therefore critical for the heath assessment of bolted steel joints. Recently, computer vision-based structural health monitoring (SHM) methods have been proposed in many research fields due to the benefits of being low-cost, easy-to-deploy, and contactless. In this study, we propose an image-based feature tracking approach to detect bolt loosening in steel connections. The method relies on a feature tracking algorithm, through which densely distributed feature points can be automatically detected and tracked from multiple images taken at different times. A novel algorithm is established to rapidly search feature points and track the movement of these feature points between images. If the bolt is loosened, feature points associated with the loosened bolt would exhibit a unique rotational movement pattern. By highlighting these feature points, the loosened bolt can be successfully localized. The effectiveness of the proposed approach was verified by a laboratory test of a steel joint using a consumer-grade digital camera.
Fatigue cracks developed in metallic materials are of critical safety concerns for mechanical, aerospace, and civil engineering structures. For fracture-critical structures, if not appropriately inspected, excessive growth of fatigue cracks can lead to catastrophic structural failures. Current crack detection technologies developed for nondestructive testing (NDT) or structural health monitoring (SHM) often require costly equipment, extensive human involvement, or complex signal processing algorithms. Recently, computer vision-based methods have shown great promise in damage detection for being contactless, low cost, and easy-to-deploy. In this paper, we propose a novel computer vision-based method for detecting fatigue cracks in a video stream. This method is based on tracking the surface motion of structural members under crack opening and closing, and identifying fatigue cracks by extracting discontinuities in the surface motion caused by cracking. The effectiveness of this method was validated through an experimental test of a steel compact, C(T), specimen. Results indicate that the proposed approach can robustly detect the fatigue crack under ambient lighting condition, despite the crack was surrounded by other crack-like edges, covered by complex surface textures, or invisible to human eyes under crack closure.
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.
A large-area electronics (LAE) strain sensor, termed soft elastomeric capacitor (SEC), has shown great promise in fatigue crack monitoring. The SEC is able to monitor strain changes over a mesoscale structural surface and endure large deformations without being damaged under cracking. Previous tests verified that the SEC is able to detect, localize, and monitor fatigue crack activities under low-cycle fatigue loading. In this paper, to examine the SEC’s capability of monitoring high-cycle fatigue cracks, a compact specimen is tested under cyclic tension, designed to ensure realistic crack opening sizes representative of those in real steel bridges. To overcome the difficulty of low signal amplitude and relatively high noise level under high-cycle fatigue loading, a robust signal processing method is proposed to convert the measured capacitance time history from the SEC sensor to power spectral densities (PSD) in the frequency domain, such that signal’s peak-to-peak amplitude can be extracted at the dominant loading frequency. A crack damage indicator is proposed as the ratio between the square root of the amplitude of PSD and load range. Results show that the crack damage indicator offers consistent indication of crack growth.
A newly-developed soft elastomeric capacitor (SEC) strain sensor has shown promise in fatigue crack monitoring. The SECs exhibit high levels of ductility and hence do not break under excessive strain when the substrate cracks due to slippage or de-bonding between the sensor and epoxy. The actual strain experienced by a SEC depends on the amount of slippage, which is difficult to simulate numerically, making it challenging to accurately predict the response of a SEC near a crack. In this paper, a two-step approach is proposed to simulate the capacitance response of a SEC. First, a finite element (FE) model of a steel compact tension specimen was analyzed under cyclic loading while the cracking process was simulated based on an element removal technique. Second, a rectangular boundary was defined near the crack region. The SEC outside the boundary was assumed to have perfect bond with the specimen, while that inside the boundary was assumed to deform freely due to slippage. A second FE model was then established to simulate the response of the SEC within the boundary subject to displacements at the boundary from the first FE model. The total simulated capacitance was computed from the model results by combining the computed capacitance inside and outside the boundary. The performance of the simulation incorporating slippage was evaluated by comparing the model results with the experimental data from the test performed on a compact tension specimen. The FE model considering slippage showed results that matched the experimental findings more closely than the FE model that did not consider slippage.
Fatigue cracks have been one of the major factors for the deterioration of steel bridges. In order to maintain structural integrity, monitoring fatigue crack activities such as crack initiation and propagation is critical to prevent catastrophic failure of steel bridges due to the accumulation of fatigue damage. Measuring the strain change under cracking is an effective way of monitoring fatigue cracks. However, traditional strain sensors such as metal foil gauges are not able to capture crack development due to their small size, limited measurement range, and high failure rate under harsh environmental conditions. Recently, a newly developed soft elastomeric capacitive sensor has great promise to overcome these limitations. In this paper, crack detection capability of the capacitive sensor is demonstrated through Finite Element (FE) analysis. A nonlinear FE model of a standard ASTM compact tension specimen is created which is calibrated to experimental data to simulate its response under fatigue loading, with the goal to 1) depict the strain distribution of the specimen under the large area covered by the capacitive sensor due to cracking; 2) characterize the relationship between capacitance change and crack width; 3) quantify the minimum required resolution of data acquisition system for detecting the fatigue cracks. The minimum resolution serves as a basis for the development of a dedicated wireless data acquisition system for the capacitive strain sensor.