Health Monitoring Of Infrastructure Research Articles

Design of a low cost and high performance wireless sensor network for structural health monitoring

Structural health monitoring systems often requires a large number of accelerometers, so the cost of each sensor and the architecture of the acquisition system become determining elements when evaluating the feasibility of this kind of studies. The flexibility of the monitoring system is fundamental, especially in case of existing buildings, where the use of a considerable quantity of cable could compromise the normal exercise, could affect the quality of acquired signal and finally be too expensive. For these reasons, the adoption of wireless sensor networks able to manage several accelerometers nodes is desirable. Wireless sensor networks have several critical aspects to solve: most important are the synchronism and the high determinism in data sampling required in this kind of applications, and the possible loss of data during the wireless transmission. The purpose of this work is to show and discuss the results obtained with a wireless sensors system for structural health monitoring of buildings or infrastructures placed in seismic zones. The first six prototypes of sensors have been assembled and tested, in order to state floor noise, the performances and the synchronization method.

Image analyses for video-based remote structure vibration monitoring system

Video-based vibration measurement is a cost-effective way for remote monitoring the health conditions of transportation and other civil structures, especially for situations where accessibility is restricted and does not allow installation of conventional monitoring devices. Besides, video-based system is global measurement. The technical basis of video-based remote vibration measurement system is digital image analysis. Comparison of the images allow the field of motion to be accurately delineated. Such information is important to understand the structure behaviors including the motion and strain distribution. This paper presents system and analyses to monitor the vibration velocity and displacement field. The performance is demonstrated on a testbed of model building. Three different methods (i.e., frame difference method, particle image velocimetry, and optical Flow Method) are utilized to analyze the image sequences to extract the feature of motion. The Performance is validated using accelerometer data. The results indicate that all three methods can estimate the velocity field of the model building, although the results can be affected by factors such as background noise and environmental interference. Optical flow method achieved the best performance among these three methods studied. With further refinement of system hardware and image processing software, it will be developed into a remote video based monitoring system for structural health monitoring of transportation infrastructure to assist the diagnoses of its health conditions.

Harvesting Energy from Vibration Absorber under Random Excitations

Abstract A dynamic vibration absorber is a passive device used often to reduce the vibrations in host structures. The potential of harvesting energy out of the motion of vibration absorber has shown promise under harmonic excitations. To achieve this goal, vibration absorber is supplemented with a piezoelectric stack for both vibration confinement and energy harvesting. The primary goal is to control the vibration of the host structure and the secondary goal is to harvest energy out of the dynamic vibration absorber simultaneously. The harvested energy can be used to power low-powered wireless sensor systems used for infrastructural health monitoring. In field applications the excitation frequency may not be harmonic and may have randomness. This manuscript looks into the power harvested by these devices under random excitations. Analytical studies are carried out considering stationary Gaussian white noise excitation within the frequency band of interest. The analytical results show the promise of broadband energy harvesting using the device combined with vibration reduction in the primary structure.

Experimental Study of Acoustic Noise Correlation Technique for Passive Monitoring of Rails

The work presented in this paper aims at investigating the ability of acoustic noise correlation technique for railway infrastructure health monitoring. The principle of this technique is based on impulse responses reconstruction by correlation of random noise propagated in the medium. Since wheel-rail interaction constitutes a source of such noise, correlation technique could be convenient for detection of rail defects using only passive sensors. Experiments have been carried out on a 2 m-long rail sample. Acoustic noise is generated in the sample at several positions. Direct comparison between an active emission-reception response and the estimated noise correlation function has confirmed the validity of the equivalence relation between them. The quality of the reconstruction is shown to be strongly related to the spatial distribution of the noise sources. High sensitivity of the noise-correlation functions to a local defect on the rail is also demonstrated. However, interpretation of the defect signature is more ambiguous than when using classical active responses. Application of a spatiotemporal Fourier transform on data recorded with variable sensor-defect distances has allowed overcoming this ambiguity.

Wireless Sensor Networks of Infrastructure Health Monitoring for High-Speed Railway

High-speed railways (HSRs) have been widely deployed all over the world in recent years and China has entered an era with both high investments and rapid expansion of HSR transport infrastructure. One of the most challenging issues is how to keep the security and safety of millions of HSR infrastructures. Meanwhile, the emerging sensing and wireless sensor network (WSN) technologies for infrastructure health monitoring (IHM) are being substituted for traditional tethered monitoring systems. This paper presents a two-layer architecture of WSN which will be appropriate for infrastructure health monitoring of HSR. The upper layer is named as tree access network and the lower layer is called star detection network. By adapting to the special characteristics of IHM network, we design a short network address and an optimized communication frame structure, which can satisfy the actual requirements and special characteristics of the IHM network. In order to implement a better transmission performance, we propose a novel transmission power based method which adopts the knowledge update mechanism to detect the optimization result. In the end, the details of address assignment and network construction are discussed, and the effectiveness of the proposed method is validated by a practical instance.

Physical topology optimization of infrastructure health monitoring sensor network for high-speed rail

With the rapid development of high-speed rail (HSR) systems, the security and safety of these huge systems are becoming the primary concerns for passengers. HSR infrastructure plays an important role in HSR systems, making the maintenance of security and safety of the HSR infrastructure especially important. Meanwhile, sensor network technologies allow the realization of real-time and all-weather monitoring of HSR infrastructure. This paper analyzes the application requirements and characteristics of infrastructure health monitoring sensor network (IHMSN) through construction of a three-layer IHMSN which is composed of end devices, repeater points, and access points. The physical topology optimization goal of IHMSN is to set the optimal number of network nodes (namely, minimum cost) as well as the best physical connections. Given types and amount of the end devices, a multiple knapsack model is established which converts the physical topology optimization problems into multiple knapsack problems. Based on the different needs of practical application, three different cases (basis case, adding devices case and weight-based case) are proposed, and the corresponding models are built. Some artificial intelligence algorithms and a traditional dynamic programming algorithm are presented to solve the problems. In addition, a general algorithmic finite state machine is proposed to describe the solving process. After comparing these algorithms in execution time, memory, and optimal results, the genetic algorithm and particle swarm optimization algorithm stand out when used to solve the basic case as well as the extended cases. The numerical results show that these proposed models and algorithms can effectively solve the physical topology optimization problem of IHMSN for HSR systems. Moreover, these methods can effectively reduce network costs and provide a theoretical basis for network communication link optimization.

Smart One-Channel Sensor Node for Ambient Vibration Test with Applications to Structural Health Monitoring of Large Civil Infrastructures

Dynamic characteristics of structures have been monitored for safe operation and efficient maintenance of large civil infrastructures. For vibration data measurement, the conventional system uses cables, which cause very expensive costs and inconvenient installation. Therefore, various wireless sensor nodes have been developed to replace the conventional wired system. However, there still remain lots of issues to be resolved such as time synchronization between sensor nodes, data loss, data security, and power supply. In this study, Smart One-Channel Sensor Node (SOSN) was developed to measure vibration data, which can practically solve the issues on installation, time synchronization, and data storage. It is designed for temporal measurement with a limited capacity to operate for several hours using embedded batteries. Laboratory tests were carried out to verify the performance of the developed SOSN compared with conventional wired system. Its practical advantages were investigated through three full-scale tests on large civil infrastructures. Three field applications revealed that SOSN is a very practical tool for short-term monitoring of large civil infrastructures with respect to traffic control, installation time and convenience, secure data gathering, and so forth.

Building structural health monitoring using dense and sparse topology wireless sensor network

Wireless sensor technology has been opened up numerous opportunities to advanced health and maintenance monitoring of civil infrastructure. Compare to the traditional tactics, it offers a better way of providing relevant information regarding the condition of building structure health at a lower price. Numerous domestic buildings, especially longer-span buildings have a low frequency response and challenging to measure using deployed numbers of sensors. The way the sensor nodes are connected plays an important role in providing the signals with required strengths. Out of many topologies, the dense and sparse topologies wireless sensor network were extensively used in sensor network applications for collecting health information. However, it is still unclear which topology is better for obtaining health information in terms of greatest components, node\'s size and degree. Theoretical and computational issues arising in the selection of the optimum topology sensor network for estimating coverage area with sensor placement in building structural monitoring are addressed. This work is an attempt to fill this gap in high-rise building structural health monitoring application. The result shows that, the sparse topology sensor network provides better performance compared with the dense topology network and would be a good choice for monitoring high-rise building structural health damage.

Development and field application of a multivariate statistical process control framework for health-monitoring of transportation infrastructure

We present a two-part multivariate statistical process control framework to support health-monitoring of transportation infrastructure. The first part consists of estimation of regression and ARIMA–GARCH models to explain, predict, and control for common-cause variation in the data, i.e., changes that can be attributed to usual operating conditions, including traffic loads, environmental effects, and damage when present throughout the data. The second part of the framework consists of using multivariate control charts to simultaneously analyze the standardized innovations of the aforementioned models in order to detect possible special-cause or extraordinary events, such as unique/infrequent traffic, weather, or the onset of damage. The proposed approach revolves around construction of T2 control charts as a framework to jointly monitor the evolution and contemporaneous correlation of a set of measurements. The approach provides significant practical/computational advantages over individual analysis of multiple structural properties, and addresses technical problems stemming from ignoring the relationships among them.To illustrate the framework, we analyze strain and displacement data from the monitoring system on the Hurley Bridge (Wisconsin Structure B-26-7). Data were collected from 1 April 2010 to 28 June 2012. Analysis of 7 measurement sequences collected over the 27month planning horizon revealed 6 possible special-cause events. In terms of outlier interpretation, we use Mason–Young–Tracy Decomposition to establish the contribution of (subsets of) the measurements. Also, we link the most significant special-cause events, in terms of magnitude and duration, to unusual changes in weather and traffic. To conclude, we compare the proposed approach and benchmark the empirical results with Principal Component Analysis, perhaps, the most common alternative appearing in the literature.

Iterated square root unscented Kalman filter for nonlinear states and parameters estimation: three DOF damped system

Structural health monitoring of civil engineering infrastructure involves uncertainties for damage detection, damage identification, damage classification, sensor optimization, safety, durability, reliability, serviceability, performance based engineering, life-cycle performance, risk management, decision making and so on. For incorporating such uncertainties, several filtering techniques accounting for stochasticity can be implemented utilizing collected data from the structures. In this paper, an iterated square root unscented Kalman filter method is proposed for the estimation of the nonlinear state variables of nonlinear structural systems. Various conventional and state-of-the-art state estimation methods are compared for the estimation performance, namely the unscented Kalman filter (UKF), the square-root unscented Kalman filter (SRUKF), the iterated unscented Kalman filter (IUKF) and the iterated square root unscented Kalman filter (ISRUKF), in two comparative studies. In the first study, UKF, IUKF, SRUKF and ISRUKF methods are utilized at a simple non-linear second order LTI system with the aim to predict a two state variables, and to estimate two model parameters. In the second study, UKF, IUKF, SRUKF and ISRUKF techniques are utilized to a complex three degree of freedom spring-mass-dashpot system to predict the displacements and the velocities state variables. They are also used to estimate the model’s hysteretic parameters. Furthermore, the effect of practical challenges (e.g., measurement noise, number of states and parameters to be estimated) on the performances of UKF, IUKF, SRUKF and ISRUKF were investigated. The results of both comparative studies reveal that the ISRUKF method provides a better estimation accuracy than the IUKF method; while both methods provide improved accuracy over the UKF and SRUKF methods. The benefit of the ISRUKF method lies in its ability to provide accuracy related advantages over other estimation methods since it re-linearizes the measurement equation by iterating an approximate maximum a posteriori (MAP) estimate around the updated state, instead of relying on the predicted state. The results of the comparative studies show also that, for all the techniques, estimating more model parameters affects the estimation accuracy as well as the convergence of the estimated states and parameters. The ISRUKF, however, still provides advantages over other methods in terms of the estimation accuracy and convergence.

Large-Scale Flexible Membrane for Automatic Strain Monitoring of Transportation Infrastructure

Structural Health Monitoring (SHM) of transportation infrastructures is a complex task, typically conducted by visual inspection due to the technical and economical constrains of existing health monitoring technologies. It results that health monitoring is highly dependent on scheduling and on the judgment of the inspectors, which can be costly and ineffective. Thus, it is fundamental to automate the SHM process to allow timely inspection, maintenance, and management of transportation infrastructure. The authors propose a flexible membrane that can be deployed over large surfaces, at low cost, for automatic and continuous monitoring of strains. The membrane, termed sensing skin, is constituted by several patches for surface discretization. Each patch is a soft capacitor, where a measured change in capacitance has a linear correspondence with changes in strain, which allows simple data processing. This paper discusses recent advances in the sensing skin, and demonstrates its promise at smart monitoring of transportation infrastructure. The novel sensor is currently installed on a highway bridge in Ames, Iowa. Preliminary results show the sensor is still functioning as designed. Direct benefits of the proposed technology to transportation infrastructure include 1) improved cost-effectiveness; 2) increased life-span; 3) enhanced resiliency; and 4) higher confidence of the public.

Detection of steel fatigue cracks with strain sensing sheets based on large area electronics.

Reliable early-stage damage detection requires continuous monitoring over large areas of structure, and with sensors of high spatial resolution. Technologies based on Large Area Electronics (LAE) can enable direct sensing and can be scaled to the level required for Structural Health Monitoring (SHM) of civil structures and infrastructure. Sensing sheets based on LAE contain dense arrangements of thin-film strain sensors, associated electronics and various control circuits deposited and integrated on a flexible polyimide substrate that can cover large areas of structures. This paper presents the development stage of a prototype strain sensing sheet based on LAE for crack detection and localization. Two types of sensing-sheet arrangements with size 6 × 6 inch (152 × 152 mm) were designed and manufactured, one with a very dense arrangement of sensors and the other with a less dense arrangement of sensors. The sensing sheets were bonded to steel plates, which had a notch on the boundary, so the fatigue cracks could be generated under cyclic loading. The sensors within the sensing sheet that were close to the notch tip successfully detected the initialization of fatigue crack and localized the damage on the plate. The sensors that were away from the crack successfully detected the propagation of fatigue cracks based on the time history of the measured strain. The results of the tests have validated the general principles of the proposed sensing sheets for crack detection and identified advantages and challenges of the two tested designs.

State-of-the-art in structural health monitoring of large and complex civil infrastructures

Researchers and administrative authorities have long recognized the significance of implementing long-term structural health monitoring (SHM) systems for civil infrastructures, especially large-span space structures or other complex infrastructures, in order to secure structural safety and issue warnings regarding the structural damage prior to costly repair or even collapse. This paper starts with a critical review of SHM objectives and standardization for the SHM in civil infrastructures. At the same time as an example, the authors present their experiences through the case study of SHM applications for large and complex civil infrastructures through a custom-made polytypic and synchronous data acquisition device that was utilized in the SHM of the large-span gymnasium in Dalian. The paper concludes with a discussion of state-of-the-art in the SHM for civil infrastructures, highlighting areas where further work is needed.

Performance assessment of indigenously developed FBG strain sensors under short-term and long-term loadings

Fibre Bragg Grating (FBG) sensors are the most recent type of fibre optic sensors, which are gaining importance in the field of structural health monitoring of civil infrastructure. These are proven to be more versatile due to their distinct advantages such as high sensitivity, immunity to electromagnetic interference, ease of multiplexing and remote sensing. These are suitable for the short-term and long-term monitoring of the structures. Multiple Fibre Bragg Grating (FBG) sensors can be surface mounted and/or embedded in structures to measure strain, temperature, cracks and vibrations. Use of FBG sensors for real time health monitoring of various civil engineering structures is well-established in western world since last decade, whereas in the Indian context this technology is still in a nascent stage. In this paper, performance assessment of indigenously developed FBG sensors for the application of health monitoring of civil engineering structures by conducting short-term and long-term studies is presented. Brief details of the laboratory studies carried out on indigenously developed FBG sensors are covered in this paper. From the laboratory studies it is found that, these FBG sensors are suitable in health monitoring of civil engineering structures.

Distributed Sensor Networks for Health Monitoring of Civil Infrastructures

Distributed Sensor Networks for Health Monitoring of Civil Infrastructures

On the advances of automatic modal identification for SHM

Structural health monitoring of civil infrastructures has great practical importance for engineers, owners and stakeholders. Numerous researches have been carried out using long-term monitoring, for instance the Rio-Niterói Bridge in Brazil, the former Z24 Bridge in Switzerland, the Millau Bridge in France, among others. In fact, some structures are monitored 24/7 in order to supply dynamic measurements that can be used for the identification of structural problems such as the presence of cracks, excessive vibration, damage or even to perform a quite extensive structural evaluation concerning its reliability and life cycle. The outputs of such an analysis, commonly entitled modal identification, are the so-called modal parameters, i.e. natural frequencies, damping ratios and mode shapes. Therefore, the development and validation of tools for the automatic identification of modal parameters based on the structural responses during normal operation is fundamental, as the success of subsequent damage detection algorithms depends on the accuracy of the modal parameters estimates. The proposed methodology uses the data driven stochastic subspace identification method (SSI-DATA), which is then complemented by a novel procedure developed for the automatic analysis of the stabilization diagrams provided by the SSI-DATA method. The efficiency of the proposed approach is attested via experimental investigations on a simply supported beam tested in laboratory and on a motorway bridge.

An integrated approach for structural health monitoring using an in-house built fiber optic system and non-parametric data analysis

Multivariate statistics based damage detection algorithms employed in conjunction with novel sensing technologies are attracting more attention for long term Structural Health Monitoring of civil infrastructure. In this study, two practical data driven methods are investigated utilizing strain data captured from a 4-span bridge model by Fiber Bragg Grating (FBG) sensors as part of a bridge health monitoring study. The most common and critical bridge damage scenarios were simulated on the representative bridge model equipped with FBG sensors. A high speed FBG interrogator system is developed by the authors to collect the strain responses under moving vehicle loads using FBG sensors. Two data driven methods, Moving Principal Component Analysis (MPCA) and Moving Cross Correlation Analysis (MCCA), are coded and implemented to handle and process the large amount of data. The efficiency of the SHM system with FBG sensors, MPCA and MCCA methods for detecting and localizing damage is explored with several experiments. Based on the findings presented in this paper, the MPCA and MCCA coupled with FBG sensors can be deemed to deliver promising results to detect both local and global damage implemented on the bridge structure.

Health Monitoring and Control of Civil Infrastructures using Wireless Smart Sensors

This paper investigates health monitoring and control (HMC) of civil infrastructures using wireless smart sensor networks. Two important requirements are (i) energy efficiency, and (ii) effective control to prevent damage of civil infrastructures. Fuzzy control scheme is incorporated in the system model to provide capability for addressing problems of imprecision and uncertainty that can be associated with values of dynamic response. Moreover, operation of wireless smart sensor networks is characterised as discounted semi-Markov decision process (SMDP) consisting of four states, namely: sleep, sense, store, and transmit. The objective is to choose policy that offers optimal energy-efficient transmission of vibration-based dynamic response, and subsequently activate appropriate control. Relevant mathematical representations are formulated for this model and its efficacy tested via numerical analysis,which is implemented in MATLAB software. It is shown by the results obtained that this HMC model can provide effective control and increase energy efficiency.

Health Monitoring and Control of Civil Infrastructures using Wireless Smart Sensors

Health Monitoring and Control of Civil Infrastructures using Wireless Smart Sensors

Dynamic Characterization of a Soft Elastomeric Capacitor for Structural Health Monitoring

AbstractStructural health monitoring of civil infrastructures is a difficult task, often impeded by the geometrical size of the monitored systems. Recent advances in conducting polymers enabled the fabrication of flexible sensors capable of covering large areas, a possible solution to the monitoring challenge of mesoscale systems. The authors have previously proposed a novel sensor consisting of a soft elastomeric capacitor (SEC) acting as a strain gauge. Arranged in a network configuration, the SECs have the potential to cover very large surfaces. In this paper, understanding of the proposed sensor is furthered by evaluating its performance at vibration-based monitoring of large-scale structures. The dynamic behavior of the SEC is characterized by subjecting the sensor to a frequency sweep, and detecting vibration modes of a full-scale steel beam. Results show that the sensor can be used to detect fundamental modes and dynamic input. Also, a network of SECs is used for output-only modal identification of...