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Structural Health Monitoring (structural + health_monitoring)
Selected AbstractsStructural Health Monitoring via Measured Ritz Vectors Utilizing Artificial Neural NetworksCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 4 2006Heung-Fai Lam Unlike most other pattern recognition methods, an artificial neural network (ANN) technique is employed as a tool for systematically identifying the damage pattern corresponding to an observed feature. An important aspect of using an ANN is its design but this is usually skipped in the literature on ANN-based SHM. The design of an ANN has significant effects on both the training and performance of the ANN. As the multi-layer perceptron ANN model is adopted in this work, ANN design refers to the selection of the number of hidden layers and the number of neurons in each hidden layer. A design method based on a Bayesian probabilistic approach for model selection is proposed. The combination of the pattern recognition method and the Bayesian ANN design method forms a practical SHM methodology. A truss model is employed to demonstrate the proposed methodology. [source] Mobile Agent Computing Paradigm for Building a Flexible Structural Health Monitoring Sensor NetworkCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 7 2010Bo Chen While sensor network approach is a feasible solution for structural health monitoring, the design of wireless sensor networks presents a number of challenges, such as adaptability and the limited communication bandwidth. To address these challenges, we explore the mobile agent approach to enhance the flexibility and reduce raw data transmission in wireless structural health monitoring sensor networks. An integrated wireless sensor network consisting of a mobile agent-based network middleware and distributed high computational power sensor nodes is developed. These embedded computer-based high computational power sensor nodes include Linux operating system, integrate with open source numerical libraries, and connect to multimodality sensors to support both active and passive sensing. The mobile agent middleware is built on a mobile agent system called Mobile-C. The mobile agent middleware allows a sensor network moving computational programs to the data source. With mobile agent middleware, a sensor network is able to adopt newly developed diagnosis algorithms and make adjustment in response to operational or task changes. The presented mobile agent approach has been validated for structural damage diagnosis using a scaled steel bridge. [source] Health Monitoring of Rehabilitated Concrete Bridges Using Distributed Optical Fiber SensingCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 6 2006Wei Zhang As newly developed techniques, distributed optical fiber sensing (DOFS) have gradually played a prominent role in structural health monitoring for the last decade. This article focuses on the employment of two types of DOFS, namely fiber Bragg grating (FBG) and Brillouin optical time domain reflectometry (BOTDR), into an integrated HMS for rehabilitated RC girder bridges by means of a series of static and dynamic loading tests to a simply supported RC T-beam strengthened by externally post-tensioned aramid fiber reinforced polymer (AFRP) tendons. Before the loading tests, a calibration test for FBG and another one for BOTDR were implemented to, respectively, obtain good linearity for both of them. Monitoring data were collected in real time during the process of external strengthening, static loading, and dynamic loading, respectively, all of which well identified the relevant structural state. The beam was finally vibrated for 2 million cycles and then loaded monotonously to failure. Based on the bending strength of externally prestressed members, ultimate values for the test specimen were numerically computed via a newly developed simplified model, which satisfactorily predicted the ultimate structural state of the beam. And then the alert values were adopted to compare with the monitoring results for safety alarm. The investigation results show a great deal of applicability for the integrated SHM by using both DOFS in rehabilitated concrete bridges strengthened by external prestressing. [source] Nonparametric Identification of a Building Structure from Experimental Data Using Wavelet Neural NetworkCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 5 2003Shih-Lin Hung By combining wavelet decomposition and artificial neural networks (ANN), wavelet neural networks (WNN) are used for solving chaotic signal processing. The basic operations and training method of wavelet neural networks are briefly introduced, since these networks can approximate universal functions. The feasibility of structural behavior modeling and the possibility of structural health monitoring using wavelet neural networks are investigated. The practical application of a wavelet neural network to the structural dynamic modeling of a building frame in shaking tests is considered in an example. Structural acceleration responses under various levels of the strength of the Kobe earthquake were used to train and then test the WNNs. The results reveal that the WNNs not only identify the structural dynamic model, but also can be applied to monitor the health condition of a building structure under strong external excitation. [source] Piezoelectric wafer active sensors for in situ ultrasonic-guided wave SHMFATIGUE & FRACTURE OF ENGINEERING MATERIALS AND STRUCTURES, Issue 8 2008L. YU ABSTRACT In situ structural health monitoring aims to perform on-demand interrogation of the structure to determine the presence of service-induced damage and defects using non-destructive evaluation ultrasonic wave methods. Recently emerged piezoelectric wafer active sensors (PWAS) have the potential to significantly improve damage detection and health monitoring. PWAS are low-profile transducers that can be permanently attached onto the structure or inserted in between composite laminates, and can perform structural damage detection in thin-wall structures using guided wave methods (Lamb, Rayleigh, SH, etc.). This paper describes the analytical and experimental work of using PWAS-guided waves for in situ structural damage detection on thin-wall structures. We begin with reviewing the guided wave theory in plate structures and PWAS principles. The mechanisms of Lamb wave excitation and detection using PWAS is presented. Subsequently, we address in turn the use of PWAS to generate Lamb waves for damage (cracks and corrosion) detection in metallic structures. Pulse-echo, pitch-catch, phased array and time reversal methods are illustrated demonstrating that PWAS Lamb-waves techniques are suitable for damage detection and structural health monitoring. The last part of the paper treats analytically and experimentally PWAS excitation and tuning in composite materials. The research results presented in this paper show that in situ SHM methodologies using PWAS transducers hold the promise for more efficient, effective and timely damage detection in thin-wall structures. [source] Fabrication and Electromechanical Characterization of a Piezoelectric Structural Fiber for Multifunctional CompositesADVANCED FUNCTIONAL MATERIALS, Issue 4 2009Yirong Lin Abstract The use of piezoceramic materials for structural sensing and actuation is a fairly well developed practice that has found use in a wide variety of applications. However, just as advanced composites offer numerous benefits over traditional engineering materials for structural design, actuators that utilize the active properties of piezoelectric fibers can improve upon many of the limitations encountered when using monolithic piezoceramic devices. Several new piezoelectric fiber composites have been developed; however, almost all studies have implemented these devices such that they are surface-bonded patches used for sensing or actuation. This paper will introduce a novel active piezoelectric structural fiber that can be laid up in a composite material to perform sensing and actuation, in addition to providing load bearing functionality. The sensing and actuation aspects of this multifunctional material will allow composites to be designed with numerous embedded functions, including structural health monitoring, power generation, vibration sensing and control, damping, and shape control through anisotropic actuation. This effort has developed a set of manufacturing techniques to fabricate the multifunctional fiber using a SiC fiber core and a BaTiO3 piezoelectric shell. The electromechanical coupling of the fiber is characterized using an atomic force microscope for various aspect ratios and is compared to predictions made using finite element modeling in ABAQUS. The results show good agreement between the finite element analysis model and indicate that the fibers could have coupling values as high as 68% of the active constituent used. [source] Quantitative structural damage detection using high-frequency piezoelectric signatures via the reverberation matrix methodINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, Issue 5 2007W. Yan Abstract High-frequency structural analysis so far has been a major issue in dynamic analysis, for which many conventional methods such as finite element method and transfer matrix method are unable to perform well. Since the electromechanical impedance technique for structural health monitoring (SHM) operates at very high frequencies, the reverberation matrix method (RMM), which was just developed a few years ago, is employed to study dynamics of the monitored structures, which are bonded with piezoelectric lead zirconate titanate (PZT) patches. A piecewisely homogeneous Euler,Bernoulli beam model is introduced to approximate the non-homogeneous beam and only one-dimensional axial vibration of PZT wafers is considered. The imperfect interfacial bonding between PZT patches and the host beam is investigated based on a shear lag model. Using a hybrid technique combining electromechanical impedance method and RMM, an analytical expression of impedance (or admittance) related to the response of the coupled model of PZT patch-bonding layer-host beam system is derived for SHM. The proposed method is examined by comparing with other theoretical methods as well as by means of a test on an intelligent system using a steel beam with two symmetrically installed PZT wafers. It could be further applied to predicting the dynamics of monitored Timoshenko beams, continuous beams, and framed structures as well. Copyright © 2006 John Wiley & Sons, Ltd. [source] | |||||||||||||