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

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

Structural Health Monitoring Strategies Using Traditional Sensors and Computer Vision

The vibration-based condition assessment of structures is the predominant method in structural health monitoring. The condition assessment of structures can be determined through the response of structures (i.e., peak displacement and acceleration), or through change characterization (i.e., system and damage identification). This dissertation presents three improved strategies for structural health monitoring using traditional sensors and computer vision. One strategy uses data fusion of acceleration and strain to estimate the displacement of building structures subjected to nonstationary wind load. In particular, this study presents two methods (data fusion A and B) that can accurately estimate both components of the displacement–the pseudo-static and the dynamic components. The two methods are validated numerically using a 20-story structure and experimentally using a small-scale 6-story structure. The second strategy is based on a computer vision method for system identification using consumer-level cameras and small structural motions. The Kanade-Lucas-Tomasi (KLT) and the Phase-Based Motion Processing (PBMP) methods are adopted in the proposed method. The method is validated experimentally using two small-scale steel structures: a 6-story building and a single-span truss bridge. The third strategy relies on the use of computer vision in damage identification by means of the Damage Locating Vector (DLV) method. This study also investigated the impact of using aliased modes in damage identification. The small-scale truss bridge was used for numerical and experimental evaluations of computer vision in system and damage identifications

Experimental and numerical analyses on the behavior of civil and marine steel structures

Bolts and welds in combination occur most commonly during the construction phase of a building when the design load changes, when there are unforeseen difficulties in make-up or matching of bolt holes, or in retrofit of existing structures. Due to the different load-displacement behavior of the bolts and welds, the behavior of the combination connections may change in different conditions. Pretensioned high-strength bolts and longitudinal fillet welds in combination has been studied both experimentally and numerically. Slip-dependent surface frictional and ductile fracture models have been incorporated in the numerical analysis to address the strain compatibility between bolts and welds. Effects of critical variables, e.g., bolt pattern, faying surface, weld size, weld location, and weld/bolt strength ratio have simulated and discussed. Similarly, the fatigue behavior of stiffened panels under variable amplitude loading has been investigated. These panels are commonly found in steel structures, such as naval vessels and bridges subjected to random variable loading. An experimental fatigue test was conducted for the stiffened box girder under variable amplitude loading. An XFEM-based fatigue crack prediction approach was proposed based on the crack closure concept. Prediction were made using the proposed approach considering the loading history effect as well as stiffener effect, and residual stresses due to welding

Investigation of Computer Vision Concepts and Methods for Structural Health Monitoring and Identification Applications

This study presents a comprehensive investigation of methods and technologies for developing a computer vision-based framework for Structural Health Monitoring (SHM) and Structural Identification (St-Id) for civil infrastructure systems, with particular emphasis on various types of bridges. SHM is implemented on various structures over the last two decades, yet, there are some issues such as considerable cost, field implementation time and excessive labor needs for the instrumentation of sensors, cable wiring work and possible interruptions during implementation. These issues make it only viable when major investments for SHM are warranted for decision making. For other cases, there needs to be a practical and effective solution, which computer-vision based framework can be a viable alternative. Computer vision based SHM has been explored over the last decade. Unlike most of the vision-based structural identification studies and practices, which focus either on structural input (vehicle location) estimation or on structural output (structural displacement and strain responses) estimation, the proposed framework combines the vision-based structural input and the structural output from non-contact sensors to overcome the limitations given above. First, this study develops a series of computer vision-based displacement measurement methods for structural response (structural output) monitoring which can be applied to different infrastructures such as grandstands, stadiums, towers, footbridges, small/medium span concrete bridges, railway bridges, and long span bridges, and under different loading cases such as human crowd, pedestrians, wind, vehicle, etc. Structural behavior, modal properties, load carrying capacities, structural serviceability and performance are investigated using vision-based methods and validated by comparing with conventional SHM approaches. In this study, some of the most famous landmark structures such as long span bridges are utilized as case studies. This study also investigated the serviceability status of structures by using computer vision-based methods. Subsequently, issues and considerations for computer vision-based measurement in field application are discussed and recommendations are provided for better results. This study also proposes a robust vision-based method for displacement measurement using spatio-temporal context learning and Taylor approximation to overcome the difficulties of vision-based monitoring under adverse environmental factors such as fog and illumination change. In addition, it is shown that the external load distribution on structures (structural input) can be estimated by using visual tracking, and afterward load rating of a bridge can be determined by using the load distribution factors extracted from computer vision-based methods. By combining the structural input and output results, the unit influence line (UIL) of structures are extracted during daily traffic just using cameras from which the external loads can be estimated by using just cameras and extracted UIL. Finally, the condition assessment at global structural level can be achieved using the structural input and output, both obtained from computer vision approaches, would give a normalized response irrespective of the type and/or load configurations of the vehicles or human loads

Non-Contact Evaluation Methods for Infrastructure Condition Assessment

The United States infrastructure, e.g. roads and bridges, are in a critical condition. Inspection, monitoring, and maintenance of these infrastructure in the traditional manner can be expensive, dangerous, time-consuming, and tied to human judgment (the inspector). Non-contact methods can help overcoming these challenges. In this dissertation two aspects of non-contact methods are explored: inspections using unmanned aerial systems (UASs), and conditions assessment using image processing and machine learning techniques. This presents a set of investigations to determine a guideline for remote autonomous bridge inspections

3D Information Technologies in Cultural Heritage Preservation and Popularisation

This Special Issue of the journal Applied Sciences presents recent advances and developments in the use of digital 3D technologies to protect and preserve cultural heritage. While most of the articles focus on aspects of 3D scanning, modeling, and presenting in VR of cultural heritage objects from buildings to small artifacts and clothing, part of the issue is devoted to 3D sound utilization in the cultural heritage field

O self-branding da geração Z no TikTok

O TikTok, enquanto plataforma digital recente e popular entre a Geração Z, tem um grande poder de autopromoção. Foram muitos os jovens que mundialmente, e de uma forma inesperada, ascenderam à fama através da plataforma tornando-se microcelebridades, fenómeno que também ocorreu em Portugal. Através desta investigação, pretende-se compreender em que medida é o TikTok uma ferramenta eficiente para fins de self-branding e autopromoção para os influenciadores portugueses na plataforma. As estratégias utilizadas pelas microcelebridades, assim como as tipologias de conteúdos produzidos, e ainda as vantagens e desvantagens resultantes das especificidades do TikTok foram estudadas. Para tal, recorreu-se a entrevistas semiestruturadas a microcelebridades portuguesas do TikTok (18-24 anos) pertencentes à Geração Z. Os resultados obtidos comprovam a eficiência do TikTok no que respeita à profissionalização de uma carreira no mundo digital, resultante da interface do TikTok, do conteúdo produzido que se revela humorístico, e ainda, da forte comunidade construída através da plataforma.TikTok, as a recent and popular digital platform among Gen Z, has great selfpromotion power. Many young people worldwide, and in an unexpected way, rose to fame through the platform, becoming microcelebrities, a phenomenon that also occurred in Portugal. Through this research, it is intended to understand to what extent TikTok is an efficient tool for self-branding and self-promotion purposes for portuguese influencers on the platform. The strategies used by microcelebrities, as well as the typologies of content produced and the advantages and disadvantages resulting from the specificities of TikTok, will be studied. To this end, semi-structured interviews were used with Portuguese microcelebrities from Portuguese TikTok (18-24 years old) belonging to Generation Z. The results obtained prove the efficiency of TikTok in terms of professionalizing a career in the digital world, resulting from the interface of TikTok, the content produced which is humorous and typical, and also, the strong community that is built through the platform