17,817 research outputs found
Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology
INE/AUTC 10.0
Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm
Offshore Wind has become the most profitable renewable energy source due to the remarkable development it has experienced in Europe over the last decade. In this paper, a review of Structural Health Monitoring Systems (SHMS) for offshore wind turbines (OWT) has been carried out considering the topic as a Statistical Pattern Recognition problem. Therefore, each one of the stages of this paradigm has been reviewed focusing on OWT application. These stages are: Operational Evaluation; Data Acquisition, Normalization and Cleansing; Feature Extraction and Information Condensation; and Statistical Model Development. It is expected that optimizing each stage, SHMS can contribute to the development of efficient Condition-Based Maintenance Strategies. Optimizing this strategy will help reduce labor costs of OWTs׳ inspection, avoid unnecessary maintenance, identify design weaknesses before failure, improve the availability of power production while preventing wind turbines׳ overloading, therefore, maximizing the investments׳ return. In the forthcoming years, a growing interest in SHM technologies for OWT is expected, enhancing the potential of offshore wind farm deployments further offshore. Increasing efficiency in operational management will contribute towards achieving UK׳s 2020 and 2050 targets, through ultimately reducing the Levelised Cost of Energy (LCOE)
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GPS monitoring of a steel box girder viaduct
Structural performance monitoring of bridges has increased as major infrastructure ages and is required to sustain loads that are significantly greater than those predicted during design. Structural stiffness and/or mass distribution can change over the lifespan of a bridge structure. Resulting changes in profile or resonant frequency provide key indicators of change, and may identify structural defects. Field tests using GPS for monitoring relatively small deformations were carried out on a steel box girder viaduct bridge in the UK. The configuration consisted of five GPS receivers located at key locations on the viaduct and two reference GPS receivers. GPS data was collected at either 10 Hz or 20 Hz and post-processed using proprietary software, along with appropriate filtering and spectral analysis. Three main frequencies were clearly detected by the GPS in the vertical component. A previously reported frequency of approximately 0.56 Hz was identified along with two other frequencies. The peak vertical deflections lie in the range of ± 50 mm, while lateral and longitudinal deflections of much smaller magnitude - in the order of a few mm - are also measured. The use of GPS leads to readily obtained and useful engineering data for continued monitoring of structures
Structural health monitoring and bridge condition assessment
Thesis (Ph.D.) University of Alaska Fairbanks, 2016This research is mainly in the field of structural identification and model calibration, optimal sensor placement, and structural health monitoring application for large-scale structures. The ultimate goal of this study is to identify the structure behavior and evaluate the health condition by using structural health monitoring system. To achieve this goal, this research firstly established two fiber optic structural health monitoring systems for a two-span truss bridge and a five-span steel girder bridge. Secondly, this research examined the empirical mode decomposition (EMD) method’s application by using the portable accelerometer system for a long steel girder bridge, and identified the accelerometer number requirements for comprehensively record bridge modal frequencies and damping. Thirdly, it developed a multi-direction model updating method which can update the bridge model by using static and dynamic measurement. Finally, this research studied the optimal static strain sensor placement and established a new method for model parameter identification and damage detection.Chapter 1: Introduction -- Chapter 2: Structural Health Monitoring of the Klehini River Bridge -- Chapter 3: Ambient Loading and Modal Parameters for the Chulitna River Bridge -- Chapter 4: Multi-direction Bridge Model Updating using Static and Dynamic Measurement -- Chapter 5: Optimal Static Strain Sensor Placement for Bridge Model Parameter Identification by using Numerical Optimization Method -- Chapter 6: Conclusions and Future Work
Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications
The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version
Optical fibre sensors - applications and potential
Fibre optic sensors have progressed considerably during the past few years and are now establishing their potential as very real contenders in the environmental, structural monitoring and industrial sensing areas. This paper will explore some examples of these emerging applications and analyse the benefits which optical fibre technology offers within these measurement sectors. We shall then continue to explore emerging prospects which offer new opportunities for future research and exploitation
Damage Tolerant Active Contro l: Concept and State of the Art
Damage tolerant active control is a new research area relating to fault tolerant control design applied to mechanical structures. It encompasses several techniques already used to design controllers and to detect and to diagnose faults, as well to monitor structural integrity. Brief reviews of the common intersections of these areas are presented, with the purpose to clarify its relations and also to justify the new controller design paradigm. Some examples help to better understand the role of the new area
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