11,870 research outputs found

    Structural health monitoring and bridge condition assessment

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    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

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    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

    Configuring and enhancing measurement systems for damage identification

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    Engineers often decide to measure structures upon signs of damage to determine its extent and its location. Measurement locations, sensor types and numbers of sensors are selected based on judgment and experience. Rational and systematic methods for evaluating structural performance can help make better decisions. This paper proposes strategies for supporting two measurement tasks related to structural health monitoring – (1) installing an initial measurement system and (2) enhancing measurement systems for subsequent measurements once data interpretation has occurred. The strategies are based on previous research into system identification using multiple models. A global optimization approach is used to design the initial measurement system. Then a greedy strategy is used to select measurement locations with maximum entropy among candidate model predictions. Two bridges are used to illustrate the proposed methodology. First, a railway truss bridge in Zangenberg, Germany, is examined. For illustration purposes, the model space is reduced by assuming only a few types of possible damage in the truss bridge. The approach is then applied to the Schwandbach bridge in Switzerland, where a broad set of damage scenarios is evaluated. For the truss bridge, the approach correctly identifies the damage that represents the behaviour of the structure. For the Schwandbach bridge, the approach is able to significantly reduce the number of candidate models. Values of candidate model parameters are also useful for planning inspection and eventual repair.Swiss National Science Foundatio

    Studies of Sensor Data Interpretation for Asset Management of the Built Environment

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    Sensing in the built environment has the potential to reduce asset management expenditure and contribute to extending useful service life. In the built environment, measurements are usually performed indirectly; effects are measured remote from their causes. Modelling approximations from many sources, such as boundary conditions, geometrical simplifications and numerical assumptions result in important systematic uncertainties that modify correlation values between measurement points. In addition, conservative behavior models that were employed - justifiably during the design stage, prior to construction - are generally inadequate when explaining measurements of real behavior. This paper summarizes the special context of sensor data interpretation for asset management in the built environment. Nearly twenty years of research results from several doctoral thesis and fourteen full-scale case studies in four countries are summarized. Originally inspired from research into model based diagnosis, work on multiple model identification evolved into a methodology for probabilistic model falsification. Throughout the research, parallel studies developed strategies for measurement system design. Recent comparisons with Bayesian model updating have shown that while traditional applications Bayesian methods are precise and accurate when all is known, they are not robust in the presence of approximate models. Finally, details of the full-scale case studies that have been used to develop model falsification are briefly described. The model-falsification strategy for data interpretation provides engineers with an easy-to-understand tool that is compatible with the context of the built environment

    Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring

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    In recent years, data mining technology has been employed to solve various Structural Health Monitoring (SHM) problems as a comprehensive strategy because of its computational capability. Optimization is one the most important functions in Data mining. In an engineering optimization problem, it is not easy to find an exact solution. In this regard, evolutionary techniques have been applied as a part of procedure of achieving the exact solution. Therefore, various metaheuristic algorithms have been developed to solve a variety of engineering optimization problems in SHM. This study presents the most applicable as well as effective evolutionary techniques used in structural damage identification. To this end, a brief overview of metaheuristic techniques is discussed in this paper. Then the most applicable optimization-based algorithms in structural damage identification are presented, i.e. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA) and Ant Colony Optimization (ACO). Some related examples are also detailed in order to indicate the efficiency of these algorithms

    Wireless Cyber-Physical Simulator and Case Studies on Structural Control

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    Abstract: Wireless Structural Control (WSC) systems can play a crucial role in protecting civil infrastructure in the event of earthquakes and other natural disasters. Such systems represent an exemplary class of cyber-physical systems that perform close-loop control using wireless sensor networks. Existing WSC research usually employs wireless sensors installed on small lab structures, which cannot capture realistic delays and data loss in wireless sensor networks deployed on large civil structures. The lack of realistic tools that capture both the cyber (wireless) and physical (structural) aspects of WSC systems has been a hurdle for cyber-physical systems research for civil infrastructure. This advances the state of the art through the following contributions. First, we developed the Wireless Cyber-Physical Simulator (WCPS), an integrated environment that combines realistic simulations of both wireless sensor networks and structures. WCPS integrates Simulink and TOSSIM, a state-of-the-art sensor network simulator featuring a realistic wireless model seeded by signal traces. Second, we performed two realistic case studies each combining a structural model with wireless traces collected from real-world environments. The building study combines a benchmark building model and wireless traces collected from a multi-story building. The bridge study combines the structural model of the Cape Girardea

    Optimal Number and Location of Sensors for Structural Damage Detection using the Theory of Geometrical Viewpoint and Parameter Subset Selection Method

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    The recorded responses at predefined sensor placements are used as input to solve an inverse structural damage detection problem. The error rate that noise causes from the recorded responses of the sensors is a significant issue in damage detection methods. Therefore, an optimal number and location of sensors is a goal to achieve the lowest error rate in structural damage detection. To overcome this problem, an algorithm (GVPSS) based on a Geometrical Viewpoint (GV) of optimal sensor placement and Parameter Subset Selection (PSS) method is proposed. The goal of the GVPSS algorithm is to minimize the effect of noise on damage detection problem. Therefore, the fitness function based on error in damage detection is minimized by GVPSS. In this method, the degrees of freedom are arranged to place sensors using a fitness function based on GV theory. Then, the optimal number and location of sensors are found on these arranged the degrees of freedom using the objective function. The efficiency of the proposed method is studied in a 52-bar dome structure under static and dynamic loadings. In the examples, damages are detected in two states: 1) using responses recorded at all DOFs, 2) using responses recorded at the optimal number and location of sensors obtained by GVPSS. The results showed that the damage detection error in state 2 is approximately equal to the error in state 1. Therefore, the GVPSS have the high performance to find the optimal number and location of sensors for structural damage detection

    Experimental sensitivity analysis of sensor placement based on virtual springs and damage quantification in CFRP composite

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    This paper suggests a method for vibration sensor placement in Carbon Fibre Reinforced Polymer (CFRP) composite structures in small structure applications where the measuring instrument weight can affect the vibrational characteristics. Considering the actual weight of the beam and the actual weight of the vibrational sensor and connecting cables. We performed a set of structural vibration experiments in various sensor positions and used the experimental results as a reference through the inverse problems technique. And Finite Element Analysis (FEA) for numerical modelling, in which the sensors are modelled as an additional mass on the beam and the virtual springs are modelled with variable rigidity. We employ the Teaching-Learning-Based Optimization Algorithm (TLBO) to identify the optimal sensor placement location. The results indicate that this application can explain the effect of sensor placement. In a second application, we consider the problem of the cracked beam and the prediction of damage severity and crack depth with the help of a formulation for crack location. Results of this Application show that the proposed approach can serve in solving both problems.

    Experimental sensitivity analysis of sensor placement based on virtual springs and damage quantification in CFRP composite

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    This paper suggests a method for vibration sensor placement in Carbon Fibre Reinforced Polymer (CFRP) composite structures in small structure applications where the measuring instrument weight can affect the vibrational characteristics. Considering the actual weight of the beam and the actual weight of the vibrational sensor and connecting cables. We performed a set of structural vibration experiments in various sensor positions and used the experimental results as a reference through the inverse problems technique. And Finite Element Analysis (FEA) for numerical modelling, in which the sensors are modelled as an additional mass on the beam and the virtual springs are modelled with variable rigidity. We employ the Teaching-Learning-Based Optimization Algorithm (TLBO) to identify the optimal sensor placement location. The results indicate that this application can explain the effect of sensor placement. In a second application, we consider the problem of the cracked beam and the prediction of damage severity and crack depth with the help of a formulation for crack location. Results of this Application show that the proposed approach can serve in solving both problems.
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