Image Registration-Based Bolt Loosening Detection of Steel Joints

A grant from the One-University Open Access Fund at the University of Kansas was used to defray the author's publication fees in this Open Access journal. The Open Access Fund, administered by librarians from the KU, KU Law, and KUMC libraries, is made possible by contributions from the offices of KU Provost, KU Vice Chancellor for Research & Graduate Studies, and KUMC Vice Chancellor for Research. For more information about the Open Access Fund, please see http://library.kumc.edu/authors-fund.xml.Self-loosening of bolts caused by repetitive loads and vibrations is one of the common defects that can weaken the structural integrity of bolted steel joints in civil structures. Many existing approaches for detecting loosening bolts are based on physical sensors and, hence, require extensive sensor deployment, which limit their abilities to cost-effectively detect loosened bolts in a large number of steel joints. Recently, computer vision-based structural health monitoring (SHM) technologies have demonstrated great potential for damage detection due to the benefits of being low cost, easy to deploy, and contactless. In this study, we propose a vision-based non-contact bolt loosening detection method that uses a consumer-grade digital camera. Two images of the monitored steel joint are first collected during different inspection periods and then aligned through two image registration processes. If the bolt experiences rotation between inspections, it will introduce differential features in the registration errors, serving as a good indicator for bolt loosening detection. The performance and robustness of this approach have been validated through a series of experimental investigations using three laboratory setups including a gusset plate on a cross frame, a column flange, and a girder web. The bolt loosening detection results are presented for easy interpretation such that informed decisions can be made about the detected loosened bolts

Monitoring Fatigue Cracks in Steel Bridges using Advanced Structural Health Monitoring Technologies

Fatigue cracks that develop in steel highway bridges under repetitive traffic loads are one of the major mechanisms that degrades structural integrity. If bridges are not appropriately inspected and maintained, fatigue cracks can eventually lead to catastrophic failures, in particular for fracture-critical bridges. Despite various levels of success of crack monitoring methods over the past decades in the fields of structural health monitoring (SHM) and non-destructive evaluation (NDE), monitoring fatigue cracks in steel bridges is still challenging due to the complex structural joint layout and unpredictable crack propagation paths. In this dissertation, advanced SHM technologies are proposed for detecting and monitoring fatigue cracks in steel bridges. These technologies are categorized as: 1) a large-area strain sensing technology based on the soft elastomeric capacitor (SEC) sensor; and 2) non-contact vision-based fatigue crack detection approaches. In SEC-based fatigue crack sensing, the research focuses are placed on numerical prediction of the SEC’s response under fatigue cracking and experimental validations of sensing algorithms for monitoring fatigue cracks over long-term. In vision-based fatigue crack detection approaches, two novel sensing methodologies are established through feature tracking and image overlapping, respectively. Laboratory test results verified that the proposed approaches can robustly identify the true fatigue crack from many non-crack edges. Overall, the proposed advanced SHM technologies show great promise for fatigue crack damage detection of steel bridges in laboratory configurations, hence form the basis for long-term fatigue sensing solutions in field applications

Fatigue Crack Monitoring under High-cycle Fatigue Loading Using Large-area Soft Elastomeric Capacitive Sensor

Fatigue cracks under high-cycle fatigue loading due to normal traffic are one of the major damage modes of steel bridges. Monitoring these cracks is of great importance especially for fracture-critical bridges in order to ensure safe operation by preventing catastrophic failure due to excessive damage. A newly developed soft elastomeric capacitive (SEC) sensor [1] is able to monitor strain changes over a large area of structural surface and resist large deformation due to cracking without being damaged. To examine the feasibility of monitoring fatigue cracks under high-cycle fatigue loading using the SEC sensor, a compact tension specimen is tested under cyclic tension loads with varying load ranges (Fig. 1), which are designed to ensure realistic stress level, hence the size of crack opening, one would see in real bridges. The measured capacitance time history from the SEC sensor is converted into power spectral densities (PSD), such that the amplitude of the signal can be extracted at the dominant loading frequency. A crack damage indicator is proposed as the ratio between the amplitude of PSD and load range. Results show that the crack damage indicator offers consistent indication of crack growth (Fig. 2). A network of SEC sensors will be designed accordingly to monitor crack propagation in steel bridges based on the proposed crack damage indicator

Numerical simulation and experimental validation of a large-area capacitive strain sensor for fatigue crack monitoring

A large-area electronics in the form of a soft elastomeric capacitor (SEC) has shown great promise as a strain sensor for fatigue crack monitoring in steel structures. The SEC sensors are inexpensive, easy to fabricate, highly stretchable, and mechanically robust. It is a highly scalable technology, capable of monitoring deformations on mesoscale systems. Preliminary experiments verified the SEC sensor’s capability in detecting, localizing, and monitoring crack growth in a compact specimen. Here, a numerical simulation method is proposed to simulate accurately the sensor’s performance under fatigue cracks. Such a method would provide a direct link between the SEC’s signal and fatigue crack geometry, extending the SEC’s capability to dense network applications on mesoscale structural components. The proposed numerical procedure consists of two parts: (1) a finite element (FE) analysis for the target structure to simulate crack growth based on an element deletion method; (2) an algorithm to compute the sensor’s capacitance response using the FE analysis results. The proposed simulation method is validated based on test data from a compact specimen. Results from the numerical simulation show good agreement with the SEC’s response from the laboratory tests as a function of the crack size. Using these findings, a parametric study is performed to investigate how the SEC would perform under different geometries. Results from the parametric study can be used to optimize the design of a dense sensor network of SECs for fatigue crack detection and localizatio

Model calibration for a soft elastomeric capacitor sensor considering slippage under fatigue cracks

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

A large-area strain sensing technology for monitoring fatigue cracks in steel bridges

This paper presents a novel large-area strain sensing technology for monitoring fatigue cracks in steel bridges. The technology is based on a soft elastomeric capacitor (SEC), which serves as a flexible and large-area strain gauge. Previous experiments have verified the SEC\u27s capability to monitor low-cycle fatigue cracks experiencing large plastic deformation and large crack opening. Here an investigation into further extending the SEC\u27s capability for long-term monitoring of fatigue cracks in steel bridges subject to traffic loading, which experience smaller crack openings. It is proposed that the peak-to-peak amplitude (pk–pk amplitude) of the sensor\u27s capacitance measurement as the indicator of crack growth to achieve robustness against capacitance drift during long-term monitoring. Then a robust crack monitoring algorithm is developed to reliably identify the level of pk–pk amplitudes through frequency analysis, from which a crack growth index (CGI) is obtained for monitoring fatigue crack growth under various loading conditions. To generate representative fatigue cracks in a laboratory, loading protocols were designed based on constant ranges of stress intensity to limit plastic deformations at the crack tip. A series of small-scale fatigue tests were performed under the designed loading protocols with various stress intensity ratios. Test results under the realistic fatigue crack conditions demonstrated the proposed crack monitoring algorithm can generate robust CGIs which are positively correlated with crack lengths and independent from loading conditions

Thin-Film Sensor for Fatigue Crack Sensing and Monitoring in Steel Bridges under Varying Crack Propagation Rates and Random Traffic Loads

Fatigue cracks are critical structural concerns for steel highway bridges, and fatigue initiation and propagation activity continues undetected between physical bridge inspections. Monitoring fatigue crack activity between physical inspections can provide far greater reliability in structural performance and can be used to prevent excessive damage and repair costs. In this paper, a thin-film strain sensor, called a soft elastomeric capacitor (SEC) sensor, is evaluated for sensing and monitoring fatigue cracks in steel bridges. The SEC is a flexible and mechanically robust strain sensor, capable of monitoring strain over large structural surfaces. By deploying multiple SECs in the form of dense sensor arrays, it is possible to detect fatigue cracks over large regions of a structural member such as a bridge girder. Previous studies have verified the SEC’s capability to monitor fatigue cracks under idealized harmonic load cycles with a constant crack propagation rate. Here, an investigation is performed under more complex and realistic situations to translate the SEC technology from laboratory testing to field applications—specifically, as cracking propagates under (1) a decreasing crack propagation rate, and (2) random traffic load cycles with stochastic peak-to-peak amplitudes and periods. An experimental program was developed which included an efficient data collection strategy, new loading protocols, and crack-sensing algorithms. The experimental results showed an increasing trend of the fatigue damage feature, crack growth index (CGI), under crack initiation and propagation, despite decreasing crack propagation rates or random traffic load cycles. In addition, the results also showed that the SEC did not produce false-positive results when cracks stopped growing. The findings of this study significantly enhance the SEC’s fatigue sensing and monitoring capability under more realistic loading conditions, which is a critical step toward field applications of this technology

Capacitance-based wireless strain sensor development

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

Hands-on STEM education during the pandemic: A fully-engaged, remote learning approach using Lego Mindstorms EV3

Hands-on learning experience is a critical component for science, technology, engineering, and mathematics (STEM) education, particularly for laboratory-based engineering courses. Previously, the engineering science program at Coastal Carolina University (CCU) established a course curriculum for ENGR 201 Engineering Problem Solving using Lego Mindstorms EV3, a programmable robotics tool kit based on Lego building blocks. In this course, students learned the topics of programming, mechanical gearing system, sensors, and robotics through the Lego Mindstorms EV3, a programmable robotics tool kit based on Lego building blocks. In this presentation, we will introduce our efforts in redesigning the course curriculum to transfer the course to a remote environment during the COVID-19 pandemic. In addition, we will share our instruction experience and student\u27s feedback, discuss the challenges we face, and illustrate potential improvements. We hope our presentation could provide useful reference resources for the peers in the STEM higher education in South Carolina