Health Monitoring Of Infrastructure Research Articles

The application of a fuzzy inference system and analytical hierarchy process based online evaluation framework to the Donghai Bridge Health Monitoring System

In this paper, a fuzzy inference system and an analytical hierarchy process-based online evaluation technique is developed to monitor the condition of the 32-km Donghai Bridge in Shanghai. The system has 478 sensors distributed along eight segments selected from the whole bridge. An online evaluation subsystem is realized, which uses raw data and extracted features or indices to give a set of hierarchically organized condition evaluations. The thresholds of each index were set to an initial value obtained from a structure damage and performance evolution analysis of the bridge. After one year of baseline monitoring, the initial threshold system was updated from the collected data. The results show that the techniques described are valid and reliable. The online method fulfills long-term infrastructure health monitoring requirements for the Donghai Bridge.

Fiber Bragg grating-based performance monitoring of a slope model subjected to seepage

In the past few years, fiber optic sensing technologies have played an increasingly important role in the health monitoring of civil infrastructures. These innovative sensing technologies have recently been successfully applied to the performance monitoring of a series of geotechnical structures. Fiber optic sensors have shown many unique advantages in comparison with conventional sensors, including immunity to electrical noise, higher precision and improved durability and embedding capabilities; fiber optic sensors are also smaller in size and lighter in weight. In order to explore the mechanism of seepage-induced slope instability, a small-scale 1 g model test of the soil slope has been performed in the laboratory. During the model’s construction, specially fabricated sensing fibers containing nine fiber Bragg grating (FBG) strain sensors connected in a series were horizontally and vertically embedded into the soil mass. The surcharge load was applied on the slope crest, and the groundwater level inside of the slope was subsequently varied using two water chambers installed besides the slope model. The fiber optic sensing data of the vertical and horizontal strains within the slope model were automatically recorded by an FBG interrogator and a computer during the test. The test results are presented and interpreted in detail. It is found that the gradually accumulated deformation of the slope model subjected to seepage can be accurately captured by the quasi-distributed FBG strain sensors. The test results also demonstrate that the slope stability is significantly affected by ground water seepage, which fits well with the results that were calculated using finite element and limit equilibrium methods. The relationship between the strain measurements and the safety factors is further analyzed, together with a discussion on the residual strains. The performance evaluation of a soil slope using fiber optic strain sensors is proved to be a potentially effective approach.

Reviews on innovations and applications in structural health monitoring for infrastructures

The developments and implementations of the structural health monitoring (SHM) system for large infrastructures have been gradually recognized by researchers, engineers and administrative authorities in the last decades. This paper summarizes an updated review on innovations and applications in SHM for infrastructures carried out by researchers at Dalian University of Technology. Invented sensors and data acquisition system are firstly briefly described. And then, some proposed theories and methods including the sensing technology, sensor placement method, signal processing and data fusion, system identification and damage detection are discussed in details. Following those, the activities on the standardization of SHM and several case applications on specific types of structure are reviewed. Finally, existing problems and promising research efforts in the field of SHM are given.

Array Signal Processing in Wireless Sensor Networks

Recent advances in Integrated Circuits (IC) and Micro Electro Mechanical Systems (MEMS) technologies have allowed the design and construction of low-cost low-power smart sensor nodes with sensing, computing, signal processing and wireless communication capabilities that can form distributed wireless sensor network systems. Wireless sensor networks have been recognized as one of the most important technologies in the 21st century, and the systems hold the promise to revolutionize the sensing paradigm and can be used to perform detection, localization, tracking, and identification of objects in a broad spectrum of military, scientific, industrial, and home applications like infrastructure health monitoring, disaster management, and battlefield surveillance [1].

Structural health monitoring of civil infrastructure using optical fiber sensing technology: a comprehensive review.

In the last two decades, a significant number of innovative sensing systems based on optical fiber sensors have been exploited in the engineering community due to their inherent distinctive advantages such as small size, light weight, immunity to electromagnetic interference (EMI) and corrosion, and embedding capability. A lot of optical fiber sensor-based monitoring systems have been developed for continuous measurement and real-time assessment of diversified engineering structures such as bridges, buildings, tunnels, pipelines, wind turbines, railway infrastructure, and geotechnical structures. The purpose of this review article is devoted to presenting a summary of the basic principles of various optical fiber sensors, innovation in sensing and computational methodologies, development of novel optical fiber sensors, and the practical application status of the optical fiber sensing technology in structural health monitoring (SHM) of civil infrastructure.

A Time-Domain Structural Damage Detection Method Based on Improved Multiparticle Swarm Coevolution Optimization Algorithm

Optimization techniques have been applied to structural health monitoring and damage detection of civil infrastructures for two decades. The standard particle swarm optimization (PSO) is easy to fall into the local optimum and such deficiency also exists in the multiparticle swarm coevolution optimization (MPSCO). This paper presents an improved MPSCO algorithm (IMPSCO) firstly and then integrates it with Newmark’s algorithm to localize and quantify the structural damage by using the damage threshold proposed. To validate the proposed method, a numerical simulation and an experimental study of a seven-story steel frame were employed finally, and a comparison was made between the proposed method and the genetic algorithm (GA). The results show threefold: (1) the proposed method not only is capable of localization and quantification of damage, but also has good noise-tolerance; (2) the damage location can be accurately detected using the damage threshold proposed in this paper; and (3) compared with the GA, the IMPSCO algorithm is more efficient and accurate for damage detection problems in general. This implies that the proposed method is applicable and effective in the community of damage detection and structural health monitoring.

Effects of Concrete on Propagation Characteristics of Guided Wave in Steel Bar Embedded in Concrete

Techniques based on ultrasonic guided waves (UGWs) play important roles in the structural health monitoring (SHM) of large-scale civil infrastructures. In this paper, dispersion equations of longitudinal wave propagation in reinforced concrete member are investigated for the purpose of monitoring steels embedded in concrete. For a steel bar embedded in concrete, not the velocity but the attenuation dispersion curves will be affected by the concrete. The effects of steel-to-concrete shear modulus ratio, density ratio, and Poisson’s ratio on propagation characteristics of guided wave in steel bar embedded in concrete were studied by the analysis of the real and imaginary parts of the wave number. The attenuation characteristics of guided waves of steel bar in different conditions including different bar concrete constraint and different diameter of steel bar are also analyzed. Studies of the influence of concrete on propagation characteristics of guided wave in steel bars embedded in concrete will increase the accuracy in judging the structure integrity and promote the level of defect detection for the steel bars embedded in concrete.

Health Monitoring of Civil Infrastructure and Materials

Despite the generally long life span of concrete structures, they cannot be considered maintenance-free. Several incidents of catastrophic failures remind the engineering world that monitoring of structures is imperative nowadays both for prevention of loss of life and property and also for effective infrastructure management based on a usually finite budget. Due to the variety of structural sizes, shapes, and uses, as well the possible vulnerabilities of the different types of structures, different tools must be used, many times complementary, in order to lead to reliable assessment results. Indeed, the reliability of in situ implementation is a key issue for any health monitoring technique along with other characteristics such as testing speed and cost-effectiveness. Several nondestructive testing (NDT) techniques have already been established, showing their suitability in certain aspects of material and structural characterization and behavior monitoring. As an example, the use of ultrasonic pulse velocity can be mentioned, the correlation of which with the quality of concrete is well documented. Other techniques based on elastic waves such as impact and impulse response, as well as the radar method, have proven their suitability in distinguishing delaminations and inhomogeneities. The assessment based on the above-mentioned methods as well as a number of others, like acoustic emission, radiography, vibration modal analysis, and slightly destructive surface strength methods in conjunction with visual inspection, provides a valuable platform for decision making concerning the maintenance, based on more robust engineering criteria than solely the experience of the engineer. However, it is a common impression that most NDT techniques have not at all reached their full potential especially with regard to in situ implementation. Therefore, the current special issue intends to examine all possible tools for economic and timely infrastructure condition assessment, with emphasis on reliability and connection of the monitoring results with the proper maintenance action that should be taken. The challenges are even higher since new and innovative materials are being increasingly used. These materials include high performance concrete, textile reinforced concrete, and nanomodified and recycled materials, which offer better capabilities for sustainable structures; however their assessment through the same techniques used for conventional concrete should not be taken for granted. In this special issue, the latest advances in different topics of civil structural health monitoring (SHM) are highlighted. More specifically, recent findings in elastic wave methods (acoustic emission, impact-echo, and ultrasonics) are reported. Vibration methodologies which are the most suitable for global bridge SHM are discussed, while the use of radar is reviewed and applied. Advancements in automated robotic inspection and online monitoring of bridge components are also discussed. The issue includes studies on corrosion detection, reinforcing and self-sensing elements for SHM. Algorithms incorporating pattern recognition clustering for the characterization of the location and degree of damage could not but be a strong part of the issue, while monitoring of self-healing and textile-reinforced materials is also discussed. The dual importance of monitoring of old structures is stressed out: their structural safety as well as their cultural heritage significance. The methodologies are successfully tested in several case studies as reported herein. We believe that the present special issue reflects on the recent advances in SHM and NDT for civil structures and materials complemented with insightful findings of various assessment techniques. We wish to thank all the authors for submitting their work in the issue and their patience during the review process. Dimitrios G. Aggelis Ninel Alver Hwa Kian Chai

Analysis of common-cause and special-cause variation in the deterioration of transportation infrastructure: A field application of statistical process control for structural health monitoring

We present a statistical process control framework to support structural health monitoring of transportation infrastructure. We contribute an integrated, generally-applicable (to various types of structural response data) statistical approach that links the literatures on statistical performance modeling and on structural health monitoring. The framework consists of two parts: The first, estimation of statistical models to explain, predict, and control for common-cause variation in the data, i.e., changes, including serial dependence, that can be attributed to usual operating conditions. The ensuing standardized innovation series are analyzed in the second part of the framework, which consists of using Shewhart and Memory Control Charts to detect special-cause or unusual events.We apply the framework to analyze strain and displacement data from the monitoring system on the Hurley Bridge (Wisconsin Structure B-26-7). Data were collected from April 1, 2010 to June 29, 2011. Our analysis reveals that, after controlling for seasonal effects, linear trends are significant components of the response measurements. Persistent displacement may be an indication of deterioration of the bridge supports. Trends in the strain data may indicate changes in the material properties, i.e., fatigue, sensor calibration, or traffic loading. The results also show that autocorrelation and conditional heteroscedasticity are significant sources of common-cause variation. Use of the control charts detected 43 possible special-cause events, with approximately 50% displaying persisting effects, and 25% lasting longer than one week. Analysis of traffic data shows that unusually heavy loading is a possible cause of the longest special-cause event, which lasted 11days.

Mechanism of a Hair Cell Bioinspired Sensor with Ultrasensitivity to Weak and Low Frequency Vibration Signals

Although significant progresses have been made in sensor technology, it is still a challenging task to develop ultrasensitive sensors to monitor very weak and low frequency vibration signal for early warning of natural disasters and efficient structural health monitoring of infrastructures. It has been reported from previous experiments that some fishes have acute sensitivities to extremely low frequency linear acceleration due to the otolith organs of the inner ear. In this paper, based on the experimental results and qualitative mechanism of the infrasound sensitivity of some fishes conducted by other researchers, a bioinspired gating spring model with negative stiffness is established to simulate the mechanical-electricity transduction of the hair cell in fish ear. Then, numerical analyses of the mechanical model subject to static and dynamic loading are conducted, respectively. It is shown that the gating model has adaptive amplification capability to weak and low frequency excitation compared with the corresponding linear model. This mechanism can be used for the design of bioinspired ultrasensitive sensors for monitoring weak and low frequency vibration signal.

Marker-Based Framework for Structural Health Monitoring of Civil Infrastructure

As no civil infrastructure can escape aging and deterioration, health monitoring can prevent and report serious structural damage. With the rapid evolution of computer vision algorithms, optical-based systems become an increasingly feasible option for automatic monitoring. This paper proposes a cheap and flexible standalone system based on marker tracking to report deflection of structural elements of civil infrastructure. A single marker is placed on tracked objects, which allows unambiguous identification of objects and accurate movement tracking. Accuracy of the system is discussed by presenting a theoretical analysis of the translation error. Additionally, as a proof of concept we extend our work with a low-cost laboratory test implementation.

Stress-Induced Magnetic Signals for Structural Health Monitoring of Civil Infrastructures

In this research, the correlations between the magnetic signals and the mechanical behavior of a ferromagnetic steel structure under tensile loads were investigated. The failure mode of the tested steel joint was the buckling of the annular plate. It was observed that during the loading process the piezomagnetic response demonstrated a different behavior compared to the mechanical response. The greater variation of the load-B field curve shows that piezomagnetic signal is a more sensitive indicator of structural failure than the mechanical response. The piezomagnetic effect may be used as a promising method to monitor the failure process of civil infrastructures.

Dynamic Survey of the Musmeci Bridge by Joint Application of Ground-Based Microwave Radar Interferometry and Ambient Noise Standard Spectral Ratio Techniques

This letter aims at analyzing the potentialities of the ground-based microwave radar interferometry technique for the dynamic characterization of civil infrastructures. This technique has been applied to estimate the fundamental dynamic parameters of the reinforced concrete Musmeci bridge in Basilicata region (southern Italy). The results have been validated by the comparison with the ones obtained applying consolidated techniques using data from accelerometers and tromometers. The good agreement obtained could suggest the joint application of such techniques as a new technological approach to set up a non-invasive and non-destructive evaluation procedure for structural health monitoring of infrastructures.

Develoment of high-sensitivity wireless strain sensor for structural health monitoring

Due to their cost-effectiveness and ease of installation, wireless smart sensors (WSS) have received considerable recent attention for structural health monitoring of civil infrastructure. Though various wireless smart sensor networks (WSSN) have been successfully implemented for full-scale structural health monitoring (SHM) applications, monitoring of low-level ambient strain still remains a challenging problem for WSS due to A/D converter (ADC) resolution, inherent circuit noise, and the need for automatic operation. In this paper, the design and validation of high-precision strain sensor board for the Imote2 WSS platform and its application to SHM of a cable-stayed bridge are presented. By accurate and automated balancing of the Wheatstone bridge, signal amplification of up to 2507-times can be obtained, while keeping signal mean close to the center of the ADC span, which allows utilization of the full span of the ADC. For better applicability to SHM for real-world structures, temperature compensation and shunt calibration are also implemented. Moreover, the sensor board has been designed to accommodate a friction-type magnet strain sensor, in addition to traditional foil-type strain gages, facilitating fast and easy deployment. The wireless strain sensor board performance is verified through both laboratory-scale tests and deployment on a full-scale cable-stayed bridge.

Sensing Methodologies and Sensor Networks for Health Monitoring of Civil Infrastructures

Large and complex civil infrastructures are especially susceptible to random vibrations, whether they are from high ground accelerations, strong wind forces, or abnormal loads such as explosions. Thus, it is imperative that the robust and continuous health monitoring system needs to be designed and implemented so as to meet the demanding goals of increasing structural safety and reliability, while reducing its operating and maintenance costs. The efficiency of a structural health monitoring system (SHMS) relies on the sensitivity of the acquired data to structural changes that may be obtained by an extended sensor network on structures as shown in “Methodology developments in sensor placement for health monitoring of civil infrastructures” and [1]. Recent advances in sensing technologies have enabled the use of large numbers of sensors for health monitoring of civil infrastructures. The sensors utilized in an SHMS are required to monitor not only the structural status including the stress, displacement, and acceleration but also those influential environmental parameters, such as the wind speed, temperature, and quality of its foundation. Since a large sensor network needs to be involved in an SHMS, how to deploy it in a complicated, spatially extended structure so that the data acquired from those locations will result in the best identification of structural characteristics will be a challenging task. On the other hand, a high cost of data acquisition systems (sensors and their supporting instruments) and accessibility limitation constrain in many cases the wide distribution of a large sensor networks on a structure. Especially, many civil structures have to be tested under operational conditions, in which the sensors are not easily amenable to be removed or changed [2, 3]. It is, therefore, of increasing interests to seek rational placement and design a method of sensor networks that contain as much information as possible about the health state of a structure. This special issue aims to aggregate the latest research efforts contributing to theoretical, methodological, and technological advances in the integration of various aspects of SHMS applications within a broad range of disciplines. Some research topics towards this target include intelligent and efficient algorithms in the design of sensor networks; evaluation criteria for different sensor networks placement methods; inherent relationship between different sensor networks placement methods; uncertainty, sensitivity, reliability, and redundancy in sensor networks; application of large scale sensors for civil infrastructures and others related. A total of 26 peer-reviewed papers have been presented in this special issue. These include identifying the optimal number and location of sensors, as well as the robustness, which aims at maintaining the stability of the sensor network when some sensors malfunction, and so forth. Without a doubt, the papers reflect the state-of-the-art researches and developments of this subject. The Guest Editor would like to express his sincere appreciation to all the authors who have contributed their work to this special issue.

Self-Sensing Hybrid Composite Rod with Braided Reinforcement for Structural Health Monitoring

Enhancing the performance and lightness of different structures has already been achieved by the employment of fibre reinforced composite materials. Nowadays, a new challenging perspective is being given to these materials by the inclusion of non-metallic conductive components. This emerging technology will lead to multifunctional composites with possible applications in structural health monitoring and traffic monitoring. The aim is to avoid corrosion problems from metallic components, as well as to eliminate the need of expensive equipments used for the health monitoring of large infrastructures. In the present research, the strain-sensing capability of a core-reinforced hybrid carbon fibre/glass fibre braided composite has been investigated in order to develop continuous monitoring system. The characterization of sensing behaviour was performed with the help of an instrumental set-up capable of measuring the change in electrical resistance with mechanical stresses applied to the samples. The effect of core composition (carbon fibre/glass fibre weight ratio) on the strain sensitivity of the braided composites has been studied in order to find out the optimum composition for best sensing capability. Among the three compositions studied (23/77, 47/53 and 100/0), composites with lowest amount of carbon fibre showed the best strain sensitivity with gauge factors up to 23.4 at very low flexural strain (0.55%). Attempts have also been made in this research to develop a piezoresistive matrix for the braided composites in order to further enhance their strain sensitivity. For this purpose, the strain sensing capability of an unsaturated polyester matrix dispersed with chopped carbon fibres (1mm and 3 mm lengths) at various weight % (0.5, 0.75 and 1.25%) was evaluated in order to find out their optimum length and concentration. It was observed that chopped fibres with different lengths showed similar strain sensitivity, which however, improves with the decrease in their concentrations.

Full-scale experimental validation of decentralized damage identification using wireless smart sensors

Wireless smart sensor networks (WSSN) facilitate a new paradigm for structural health monitoring (SHM) of civil infrastructure. Conventionally, SHM systems employing wired sensors and centralized data acquisition have been used to characterize the state of a structure; however, widespread implementation has been limited due to high costs and difficulties in installation. WSSN offer a unique opportunity to overcome such difficulties. Recent developments have realized low-cost, smart sensors with on-board computation and wireless communication capabilities, making deployment of a dense array of sensors on large civil structures both economical and feasible. Wireless smart sensors (WSS) have shown their tremendous potential for SHM in recent full-scale bridge monitoring examples. However, structural damage identification using on-board computation capability in a WSSN, a primary objective of SHM, has yet to reach its full potential. This paper presents full-scale validation of a damage identification strategy using a decentralized network of Imote2 nodes on a historic steel truss bridge. A total of 24 WSS nodes with 144 sensor channels are deployed on the bridge to validate the developed damage identification software. The performance of this decentralized damage identification strategy is demonstrated on the WSSN by comparing its results with those from the traditional centralized approach, as well as visual inspection.

Methodology Developments in Sensor Placement for Health Monitoring of Civil Infrastructures

Optimal sensor placement (OSP) technique plays a key role in the structural health monitoring (SHM) of large-scale civil infrastructures. This paper outlines an overview of current research and development in the field of OSP problems in a perspective of both researchers and engineers. The paper begins with a definition of the model of sensor placement and provides the basic issues covering relevant methodologies. The primary evaluation criteria and main sensor placement methods are then discussed in details. Following that, the linkage between several influential sensor placement methods is described. Finally, existing problems and promising research efforts in the OSP problem of civil SHM are discussed.

Carbon Fibre/Silicon Composite Sensors for Concrete Structural Health Monitoring in Compression

Structural health monitoring of concrete infrastructures has attracted enormous attention due to the brittle nature of concrete. In this research, we report the carbon fibre/silicon rubber composite sensors and their excellent monitoring in concrete’s compression. It is shown that the electric resistance change synchronously with the mechanical deformation of concrete during the compression process. With the increase of carbon fibre fraction, the sensitivity decreases and the numerical equations to predict the structural change are also obtained. The experimental results reported here highlight the potential application of CF/silicon composites as an effective, real-time structural monitoring sensor with low-cost and long life.

Using vibration phase space topology changes for structural damage detection

Ideally, structural health monitoring of civil infrastructure consists of determining, by measured parameters, the location and severity of damage in the structure. Many structural vibration parameters have been used to identify and quantify damage. Using parameters based on structural vibration phase space features for damage detection is a new field in structural health monitoring. In this article, a new parameter based on topology changes of the phase space of vibration signals is proposed to identify structural damage, and an index named changes of phase space topology derived from vibration time history is used to locate the damage. A circular arch structure is used to demonstrate the method. Both numerical simulation and experimental tests of dynamic responses of a scaled arch structure to impact loads are carried out. The obtained structural response data are used to detect structural damage. Both the experimental and numerical results indicate that this method can successfully locate damage. It also demonstrated that this proposed method is more sensitive to damage but less sensitive to noise than modal-based parameters. The proposed damage index can be a good candidate in an online structural health monitoring system, as it depends on global vibration measurements but is more sensitive to structural damage than other global vibration-based parameters such as vibration frequencies and mode shapes.