Showing papers on "Structural health monitoring published in 2009"
TL;DR: In this paper, a structural health monitoring (SHM) system consisting of over 600 sensors has been designed and is being implemented by The Hong Kong Polytechnic University to GNTVT for both in-construction and in-service real-time monitoring.
Abstract: The Guangzhou New TV Tower (GNTVT), currently being constructed in Guangzhou, China, is a supertall structure with a height of 610 m. This tube-in-tube structure comprises a reinforced concrete inner tube and a steel outer tube adopting concrete-filled-tube columns. A sophisticated structural health monitoring (SHM) system consisting of over 600 sensors has been designed and is being implemented by The Hong Kong Polytechnic University to GNTVT for both in-construction and in-service real-time monitoring. This paper outlines the technology innovation in developing and implementing this SHM system, which includes (i) modular design of the SHM system, (ii) integration of the in-construction monitoring system and the in-service monitoring system, (iii) wireless-based data acquisition and Internet-based remote data transmission, (iv) design and implementation of a fiber Bragg grating sensing system,(v) structural health and condition assessment using static and dynamic monitoring data, (vi) verification of the effectiveness of vibration control devices by the SHM system, and (vii) development of an SHM benchmark problem by taking GNTVT as a test bed and using real-world measurement data. Preliminary monitoring data including those obtained during the Wenchuan earthquake and recent typhoons are also presented. Copyright © 2008 John Wiley & Sons, Ltd.
332 citations
TL;DR: Time series modeling, i.e. auto-regressive models, is used in conjunction with Mahalanobis distance-based outlier detection algorithms to identify different types of structural changes on different test structures in the context of SHM using different laboratory structures.
250 citations
TL;DR: In this paper, a conformable carbon nanotube-polyelectrolyte sensing skin fabricated via the layer-by-layer technique is proposed to monitor strain and impact damage over spatial areas.
Abstract: Impact damage, excessive loading, and corrosion have been identified as critical and long-term problems that constantly threaten the integrity and reliability of structural systems (e.g., civil infrastructures, aircrafts, and naval vessels). While a variety of sensing transducers have been proposed for structural health monitoring, most sensors only offer measurement of structural behavior at discrete structural locations. Here, a conformable carbon nanotube-polyelectrolyte sensing skin fabricated via the layer-by-layer technique is proposed to monitor strain and impact damage over spatial areas. Specifically, electrical impedance tomographical (EIT) conductivity mapping techniques are employed to offer two-dimensional damage maps from which damage location and severity can be easily and accurately quantified. This study deposits carbon nanotube-based sensing skins upon metallic structural plates with electrodes installed along the plate boundary. Based on boundary electrical measurements, EIT mapping captures both strain in the underlying substrate as well as damage (e.g., permanent deformation and cracking) introduced using an impact apparatus.
240 citations
TL;DR: A novel wireless sensor system is presented that harvests vibrations of the bridge created by passing traffic, which is converted into usable electrical energy by means of a linear electromagnetic generator.
Abstract: The task of structural health monitoring (SHM) of aging highway bridges and overpasses is important not only from the point of preventing economic losses from traffic delays and detours but also is a matter of preventing catastrophic failures and loss of human life. In recent years, wireless sensor technologies have been used extensively to develop SHM platforms for bridges. A limitation of wireless sensors is the finite life span of batteries and high cost of battery replacements, which make such systems prohibitively expensive in many cases. Energy harvesting is a solution capable to alleviate this problem. A novel wireless sensor system is presented that harvests vibrations of the bridge created by passing traffic, which is converted into usable electrical energy by means of a linear electromagnetic generator. Utilization of an electromagnetic generator allows harvesting of up to 12.5 mW of power in the resonant mode with the frequency of excitation at 3.1 Hz, in this particular design. The novelty of the system also includes tight integration of the power generator and a smart algorithm for energy conversion that switches between the low-power mode and the impedance matching mode. Finally, results of field experiments are presented in which the wireless system is operated exclusively by the harvested energy of vibration on a rural highway bridge with low traffic volume.
202 citations
01 Mar 2009
TL;DR: The intent is to provide the reader with an introduction to feature extraction and statistical modelling for feature classification in the context of SHM through the application of the Los Alamos National Laboratory’s statistical pattern recognition paradigm for structural health monitoring (SHM).
Abstract: The real-world structures are subjected to operational and environmental condition changes that impose difficulties in detecting and identifying structural damage. The aim of this report is to detect damage with the presence of such operational and environmental condition changes through the application of the Los Alamos National Laboratory’s statistical pattern recognition paradigm for structural health monitoring (SHM). The test structure is a laboratory three-story building, and the damage is simulated through nonlinear effects introduced by a bumper mechanism that simulates a repetitive impact-type nonlinearity. The report reviews and illustrates various statistical principles that have had wide application in many engineering fields. The intent is to provide the reader with an introduction to feature extraction and statistical modelling for feature classification in the context of SHM. In this process, the strengths and limitations of some actual statistical techniques used to detect damage in the structures are discussed. In the hierarchical structure of damage detection, this report is only concerned with the first step of the damage detection strategy, which is the evaluation of the existence of damage in the structure. The data from this study and a detailed description of the test structure are available for download at: http://institute.lanl.gov/ei/software-and-data/.
201 citations
TL;DR: A wind and structural health monitoring system (WASHMS) has been installed in the Tsing Ma Bridge and operated by the Hong Kong Highways Department since 1997 as mentioned in this paper, which is devised to carry out the monitoring of environmental status, traffic loads, bridge features and bridge responses.
Abstract: The Tsing Ma Bridge in Hong Kong is a long suspension bridge. A wind and structural health monitoring system (WASHMS) has been installed in the bridge and operated by the Hong Kong Highways Department since 1997. The WASHMS is devised to carry out the monitoring of environmental status, traffic loads, bridge features and bridge responses. The environmental status includes temperature environment monitored by temperature sensors, whereas the bridge responses contain displacement responses recorded by displacement transducers, level sensing stations, and global positioning systems (GPS). Bridge displacement responses are, however, induced by a combination of four major types of loadings due to wind, temperature, highway, and railway. This investigation focuses on the temperature environment and the predominating temperature effect on the Tsing Ma Bridge. The main features and the pertinent monitoring system of the Tsing Ma Bridge are first introduced. The data collected from the four types of sensors are pre-processed. The statistics of ambient air temperature, effective temperature and displacement response of the bridge are then figured out based on the measurement data. The statistical relationship between the effective temperature and the displacement of the bridge is finally established. These results are useful for monitoring temperature effects on the Tsing Ma Bridge. Copyright © 2009 John Wiley & Sons, Ltd.
178 citations
TL;DR: A sparse-array structural health monitoring (SHM) system based on guided waves was applied to the door of a commercial shipping container, and comparison of signals transmitted between different transducer pairs before and after damage was used to give an initial indication of defect detectability.
Abstract: A sparse-array structural health monitoring (SHM) system based on guided waves was applied to the door of a commercial shipping container. The door comprised a corrugated steel panel approximately 2.4 m by 2.4 m surrounded by a box beam frame and testing was performed in a nonlaboratory environment. A 3-D finite element (FE) model of the corrugations was used to predict transmission coefficients for the A0 and S0 modes across the corrugations as a function of incidence angle. The S0 mode transmission across the corrugations was substantially stronger, and this mode was used in the main test series. A sparse array with 9 transducers was attached to the structure, and signals from the undamaged structure were recorded at periodic intervals over a 3-week period, and the resulting signal database was used for temperature compensation of subsequent signals. Defects in the form of holes whose diameter was increased incrementally from 1 to 10 mm were introduced at 2 different points of the structure, and signals were taken for each condition. Direct analysis of subtracted signals allowed understanding of the defect detection capability of the system. Comparison of signals transmitted between different transducer pairs before and after damage was used to give an initial indication of defect detectability. Signals from all combinations of transducers were then used in imaging algorithms, and good localization of holes with a 5-mm diameter or above was possible within the sparse array, which covered half of the area of the structure.
155 citations
TL;DR: In this article, a feature-based approach is taken whereby differential features between a signal and a baseline are defined that are sensitive to damage but less sensitive to surface wetting, and multiple features obtained from a spatially distributed sensor array are combined via a voting strategy.
Abstract: Strongly reverberating diffuse-like ultrasonic waves can interrogate large areas of complex structures that do not support more easily interpreted guided waves. However, sensitivity to environmental changes such as temperature and surface wetting can degrade the performance of a structural health monitoring system using these types of waves. Surface wetting is investigated here with a simplified experiment where controlled amounts of water are applied to the surface of a specimen in conjunction with incrementally introduced artificial damage. A feature-based approach is taken whereby differential features between a signal and a baseline are defined that are sensitive to damage but less sensitive to surface wetting, and multiple features obtained from a spatially distributed sensor array are combined via a voting strategy. In addition, the features considered are insensitive to moderate temperature changes, which are unavoidable even in the laboratory. Experimental results show a probability of detection greater than 90% when detecting damage in the presence of modest surface wetting while maintaining a false alarm rate under 5%.
147 citations
TL;DR: A survey and evaluation of relevant studies that appear promising and practical for this purpose is presented in this paper, where several image processing techniques, including enhancement, noise removal, registratio, etc., are evaluated.
Abstract: Automatic health monitoring and maintenance of civil infrastructure systems is a challenging area of research. Nondestructive evaluation techniques, such as digital image processing, are innovative approaches for structural health monitoring. Current structure inspection standards require an inspector to travel to the structure site and visually assess the structure conditions. A less time consuming and inexpensive alternative to current monitoring methods is to use a robotic system that could inspect structures more frequently. Among several possible techniques is the use of optical instrumentation (e.g. digital cameras) that relies on image processing. The feasibility of using image processing techniques to detect deterioration in structures has been acknowledged by leading experts in the field. A survey and evaluation of relevant studies that appear promising and practical for this purpose is presented in this study. Several image processing techniques, including enhancement, noise removal, registratio...
147 citations
TL;DR: To verify the efficiency and accuracy of the new methodology, practical tests were carried out, and the results were compared with those obtained using a conventional impedance analyzer.
Abstract: Recent research has shown that electromechanical (E/M) impedance-based techniques are an efficient strategy for damage sensing in the context of structural health monitoring (SHM). The basic principle is to excite the lead zirconate titanate (PZT) sensors attached to the structure and to measure the corresponding electrical impedance. A variation in the electrical impedance indicates a variation in the mechanical impedance and, consequently, structural damage. To measure the electrical impedance, most universities and research centers use expensive, bulky, heavy, and high-powered impedance analyzers, which limit new developments and practical applications. To overcome these issues, this paper presents an efficient and inexpensive methodology for electrical impedance measurement. To verify the efficiency and accuracy of the new methodology, practical tests were carried out, and the results were compared with those obtained using a conventional impedance analyzer.
145 citations
TL;DR: In this paper, the authors evaluate the performance of these techniques for practical civil structural health monitoring (SHM) by using displacement modes from accelerometers and long-gage distributed strain measurements.
Abstract: Accurate measurement and monitoring of vibration characteristics is critical for proper detection of the location and severity of damage. However, experimentally measured modal properties are inevitably corrupted by measurement noise and errors, which could render most vibration-based structural damage identification algorithms unreliable for civil structural health monitoring (SHM). This article, through computer simulation and experimental investigation of a simply supported beam, comparatively evaluates the performance of these techniques for practical civil SHM by using displacement modes from accelerometers and long-gage distributed strain measurements. Most of all the techniques proved unreliable for damage identification using noisy measurements from accelerometers, while successful with distributed strain measurements. The findings reveal that long-gage distributed strain measurements are much more efficient choice over the traditional measurement techniques for reliable civil SHM. It may therefor...
TL;DR: In this article, the use of cementitious composites as their own sensor platform capable of measuring mechanical behavior under loading is explored, where the piezoresistivity of the composites is quantified so as to establish the material as a multifunctional system capable of self-sensing.
Abstract: Cement-based composites (for example, concrete) are brittle materials that crack when loaded in tension. Current strategies for crack detection are primarily based upon visual inspection by an inspector; such approaches are labor-intensive and expensive. Direly needed are sensors that can be included within a structural health monitoring (SHM) system for automated quantification of crack damage. This study explores the use of cementitious materials as their own sensor platform capable of measuring mechanical behavior under loading. Fundamentally, this self-sensing functionality will be based upon electro-mechanical properties. First, the piezoresistivity of cementitious composites is quantified so as to establish the material as a multifunctional system capable of self-sensing. Second, electrical impedance tomography (EIT) is proposed for measuring internal strain fields using only electrical measurements taken along the boundary of the structural element. An inherent advantage of EIT is that it is a distributed sensing approach offering measurement of strain fields across 2D or 3D. Furthermore, the approach is well suited for imaging cracks which appear as conductivity reductions in EIT-derived conductivity maps. Finally, to validate the accuracy of the EIT technique, it is applied to fiber reinforced cementitious composite elements loaded by axial tension-compression cycles and 3-point bending.
TL;DR: In this article, the feasibility of the EMI sensing technique for the online strength gain monitoring of early-age concrete is investigated and the experimental study is conducted on the piezoelectric patch instrumented concrete specimen.
TL;DR: 6 damage detection methods based on vibration monitoring could detect damage for all the damage scenarios; however, their performance was notably affected when noise was introduced to the vibration parameters.
Abstract: The important advances achieved in modal identification, sensors, and structural monitoring of bridges have motivated the bridge engineering community to develop damage detection methods based on vibration monitoring. Some of these methods have already been demonstrated under certain conditions in bridges with deliberate damage. However, performance of these methods for damage detection in bridges has not been fully proven and more research must be done in this area. In this paper, 6 damage detection methods based on vibration monitoring are evaluated with 2 case studies. First, the dynamic simulation and modal parameters of a cracked composite bridge are obtained. Here, the damage detection methods are evaluated under different crack depth, extension of the damage, and noise level. Second, damage is identified in a reinforced concrete bridge. This bridge was deliberately damaged in 2 phases. In this example, damage detection methods, which do not require comparison between different structural conditions, were applied. In the first case study, evaluated damage detection methods could detect damage for all the damage scenarios; however, their performance was notably affected when noise was introduced to the vibration parameters. In the second case study, the evaluated methods could successfully localize the damage induced to the bridge.
TL;DR: In this article, the authors present the advances in distributed polymer optical fiber (POF) strain sensors technology with respect to application in structural health monitoring in textiles for health monitoring of geotechnical structures.
Abstract: Polymer optical fiber (POF) sensors have the unique ability to measure high strain distributed along the fiber up to 40% using the optical time-domain reflectometry (OTDR) technique. Both, standard PMMA fibers and perfluorinated (PF) graded-index (GI) POF are introduced and evaluated in potential use and applicability. Further, distributed length change measurement based on cross-correlation analysis of the characteristic fiber signature of PF POF is introduced. We conclude the advances in distributed POF strain sensors technology with respect to application in structural health monitoring. Special focus is on the sensor integration into technical textiles for health monitoring of geotechnical structures and masonry structures. Measurement results of sensor-equipped textiles in different model tests are presented, displacement of soil and cracks in retrofitted masonry structures are detected and evaluated.
Book•
01 Aug 2009
TL;DR: This research has resulted in the first autonomous, full-scale implementation of a wireless smart sensor network for structural health monitoring, providing for the first time the enhanced computation and communication resources that support demanding sensor network applications such as SHM of civil infrastructure.
Abstract: The demands of aging infrastructure require effective methods for structural monitoring and maintenance. Wireless smart sensors provide an attractive means for structural health monitoring (SHM) through the utilization of onboard computation to achieve distributed data management. Such an approach is scalable to the large number of sensor nodes required for high-fidelity modal analysis and damage detection. While much of the technology associated with smart sensors has been available for nearly a decade, there have been limited numbers of full-scale implementations due to the lack of critical hardware and software elements. This research develops a flexible smart sensor framework for full-scale, autonomous SHM that integrates the necessary software and hardware while addressing key implementation requirements. The Imote2 smart sensor platform is employed herein, providing for the first time the enhanced computation and communication resources that support demanding sensor network applications such as SHM of civil infrastructure. A multimetric Imote2 sensor board with onboard signal processing specifically designed for SHM applications has been designed and validated. Flexible network management software combines a sleep/wake cycle for enhanced power efficiency with threshold detection for triggering network wide operations such as synchronized sensing or decentralized modal analysis. A cable-stayed bridge in South Korea serves as one of the test beds for this effort, both informing and driving system development. This research has resulted in the first autonomous, full-scale implementation of a wireless smart sensor network for structural health monitoring.
TL;DR: In this article, the authors developed a baseline-free damage diagnosis technique based on time reversal theory and improved piezoelectric actuators/sensors array arrangement to separate the scattered signals from the sensing signals.
Abstract: The Lamb wave-based structural damage detection technique is widely viewed as one of the most encouraging tools for structural health monitoring. However, the propagation of Lamb waves is complicated. Damage-scattering components in the sensing signals may easily be mixed with the waves that come from the source and those reflected from boundaries. Traditionally, damage scattering signals are obtained by comparing test data with a baseline data. However, such a process is affected easily by environmental and operational variability. Major emphases of the research in this article are on developing a baseline-free damage diagnosis technique based on time reversal theory. First, the propagation of the Lamb wave and its time reversal process are discussed. Second, time window functions and improved piezoelectric actuators/sensors array arrangement are developed to separate the scattered signals from the sensing signals. A time reversal imaging method is also adopted to detect and show the damages. This method...
TL;DR: In this article, an impedance-based structural health monitoring (SHM) technique considering temperature effects is presented, which minimizes the effect of the temperature variation on the impedance measurements, a previously proposed temperature compensation technique based on the cross-correlation between the reference-impedance data and a concurrent impedance data is revisited.
Abstract: This study presents an impedance-based structural health monitoring (SHM) technique considering temperature effects. The temperature variation results in significant impedance variations, particularly a frequency shift in the impedance, which may lead to erroneous diagnostic results of real structures, such as civil, mechanical, and aerospace structures. In order to minimize the effect of the temperature variation on the impedance measurements, a previously proposed temperature compensation technique based on the cross-correlation between the reference-impedance data and a concurrent impedance data is revisited. In this study, cross-correlation coefficient (CC) after an effective frequency shift (EFS), which is defined as the frequency shift causing two impedance data to have the maximum correlation, is utilized. To promote a practical use of the proposed SHM strategy, an automated continuous monitoring framework using MATLAB® is developed and incorporated with the current hardware system. Validation of t...
TL;DR: An integrated multi-channel PZT array scanning system (ISS) is developed for the purpose of SHM and an integrated software system which is based on the LabVIEW software platform is developed to manage the hardware, and perform signal processing and damage estimation.
Abstract: Piezoelectric sensor (PZT) based structural health monitoring (SHM) methods can efficiently estimate the health condition of aircraft structures. To monitor large-scale structures, dense PZT arrays are usually needed. How to scan different PZT actuator–sensor channels in the PZT array to achieve a real time and stable SHM task is an important issue in the application of these methods. In this paper, an integrated multi-channel PZT array scanning system (ISS) is developed for the purpose of SHM. A type of gain programmable charge amplifier and a low crosstalk scanning module are discussed. An integrated software system which is based on the LabVIEW software platform is developed to manage the hardware, and perform signal processing and damage estimation. To validate the functions of this system, an evaluation is performed on a carbon fiber composite wing box of an unmanned aerial vehicle (UAV). The application results show the promising performance of this system.
TL;DR: In this article, a large-scale wireless sensor network is deployed for ambient vibration testing of a single-span integral abutment bridge to derive in-service modal parameters.
TL;DR: In this paper, an approach to use strain data from a multi-girder, composite steel bridge for long-term Structural Health Monitoring (SHM) is presented.
Abstract: This paper presents an approach to use strain data from a multi-girder, composite steel bridge for long-term Structural Health Monitoring (SHM). The bridge being studied is part of a research proje...
TL;DR: In this article, a smart aggregate-based approach is proposed for the structural health monitoring of a concrete shear wall structure, where piezoceramic-based smart aggregates were distributed in predetermined locations prior to the casting of the concrete structure to form an active-sensing system for the health monitoring purpose.
Abstract: In this paper, a smart aggregate-based approach is proposed for the structural health monitoring of a concrete shear wall structure. The piezoceramic-based smart aggregates were distributed in predetermined locations prior to the casting of the concrete structure to form an active-sensing system for the health monitoring purpose. To evaluate the damage in different areas, the concrete shear wall was sectioned into sub-domains and a wavelet-packet-based damage index matrix is proposed to evaluate the health status in these sections. A cyclic loading procedure was applied to gradually fail the concrete shear wall and the proposed structural health monitoring approach was used to perform structural health monitoring during this loading procedure. The experimental results have shown that the proposed smart aggregate-based approach effectively evaluated the damage status in different areas and detected the precautionary point to predict the structural failure. The proposed approach has the potential to be applied to the structural health monitoring of large-scale concrete shear wall structures.
TL;DR: This article will provide a concise review of the applications of plastic optical fibre sensors for monitoring the integrity of engineering structures in the context of SHM.
Abstract: While a number of literature reviews have been published in recent times on the applications of optical fibre sensors in smart structures research, these have mainly focused on the use of conventional glass-based fibres. The availability of inexpensive, rugged, and large-core plastic-based optical fibres has resulted in growing interest amongst researchers in their use as low-cost sensors in a variety of areas including chemical sensing, biomedicine, and the measurement of a range of physical parameters. The sensing principles used in plastic optical fibres are often similar to those developed in glass-based fibres, but the advantages associated with plastic fibres render them attractive as an alternative to conventional glass fibres, and their ability to detect and measure physical parameters such as strain, stress, load, temperature, displacement, and pressure makes them suitable for structural health monitoring (SHM) applications. Increasingly their applications as sensors in the field of structural engineering are being studied and reported in literature. This article will provide a concise review of the applications of plastic optical fibre sensors for monitoring the integrity of engineering structures in the context of SHM.
TL;DR: In this paper, nonlinear principal component analysis based on unsupervised support vector machine is introduced and incorporated with a discrete-time prediction model and a hypothesis test for data normalization.
TL;DR: Based on the long-term monitored strain data induced by heavy vehicle traffic on an existing bridge, this paper presented an efficient approach to assess the bridge system performance through a series-parallel system model consisting of bridge component reliabilities.
Abstract: Based on the long-term monitored strain data induced by heavy vehicle traffic on an existing bridge, this paper presents an efficient approach to assessing the bridge system performance through a series-parallel system model consisting of bridge component reliabilities. The correlations among the bridge component safety margins are obtained by using actual traffic and strain data from structural health monitoring SHM. The prediction of bridge system reliability in the future is dependent on the performance functions of components. Sensitivity studies with respect to system modeling, correlations, extreme value probability distributions, measurement errors, and number of observations are carried out. A case study of the proposed approach is provided on an existing highway bridge in Wisconsin, which was monitored in 2004 by the Advanced Technology for Large Structural Systems Center, a National Engineering Research Center at Lehigh University, Bethlehem, Pa, USA. This study provides a solid basis for integrating SHM data into practical assessment of bridge system performance. DOI: 10.1061/ASCEST.1943-541X.0000014 CE Database subject headings: Bridges; Structural reliability; Structural safety; Monitoring; Assessments.
01 Jan 2009
TL;DR: This Chapter 13 presents a review of damage detection and structural health monitoring of mechanical systems from changes in the measurement of both linear and non-linear vibrations.
Abstract: This Chapter 13 presents a review of damage detection and structural health monitoring of mechanical systems from changes in the measurement of both linear and non-linear vibrations.
15 Sep 2009
TL;DR: In this paper, the spectral properties of optical fiber Bragg gratings and their response to applied strain and temperature fields are derived for structural health monitoring applications, and common approaches to invert the spectral response of an optical fiber grating subjected to a complex strain field are also reviewed.
Abstract: This article reviews the principles and application of optical fiber Bragg gratings (FBGs) as strain and temperature sensors for structural health monitoring applications. FBG sensors have many advantages for strain sensing in such applications including their small size, the potential to multiplex hundreds of sensors with a single ingress/egress fiber, their immunity to electromagnetic interference, and their corrosion resistance. The spectral properties of FBGs and their response to applied strain and temperature fields are derived. Multiplexing and interrogation strategies for large sensor networks are presented. Special focus is placed on the response of FBGs to multiaxis strain fields and rapidly varying strain fields such as near the location of structural damage. Common approaches to invert the spectral response of an FBG subjected to a complex strain field are also reviewed. Finally, this article reviews common fabrication techniques for FBGs and their relative impacts on the strength of these sensors.
Keywords:
optical fiber sensor;
fiber Bragg grating;
long period grating;
wavelength division multiplexing;
multiaxis sensor
TL;DR: This work proposes modeling structural vibration sensor output data using nonlinear time-series models and demonstrates the improved performance of these models over currently used linear models.
Abstract: The use of statistical methods for anomaly detection has become of interest to researchers in many subject areas. Structural health monitoring in particular has benefited from the versatility of statistical damage-detection techniques. We propose modeling structural vibration sensor output data using nonlinear time-series models. We demonstrate the improved performance of these models over currently used linear models. Whereas existing methods typically use a single sensor's output for damage detection, we create a combined sensor analysis to maximize the efficiency of damage detection. From this combined analysis we may also identify the individual sensors that are most influenced by structural damage.
TL;DR: In this article, a novel fiber Bragg grating (FBG) sensor system for measurement of strain and temperature is proposed, which is based on time-division multiplexing (TDM).
TL;DR: In this article, a bridge health monitoring system is presented based on vibration measurements collected from a network of acceleration sensors, combining information from the sensor network with the theoretical information built into a finite element model for simulating bridge behavior, are incorporated into the system in order to monitor structural condition, track structural changes and identify the location, type and extent of damage.
Abstract: A bridge health monitoring system is presented based on vibration measurements collected from a network of acceleration sensors. Sophisticated structural identification methods, combining information from the sensor network with the theoretical information built into a finite element model for simulating bridge behavior, are incorporated into the system in order to monitor structural condition, track structural changes and identify the location, type and extent of damage. This work starts with a brief overview of the modal and model identification algorithms and software incorporated into the monitoring system and then presents details on a Bayesian inference framework for the identification of the location and the severity of damage using measured modal characteristics. The methodology for damage detection combines the information contained in a set of measurement modal data with the information provided by a family of competitive, parameterized, finite element model classes simulating plausible damage scenarios in the structure. The effectiveness of the damage detection algorithm is demonstrated and validated using simulated modal data from an instrumented R/C bridge of the Egnatia Odos motorway, as well as using experimental vibration data from a laboratory small-scaled bridge section.