Structural Health Monitoring Technology Research Articles

A wavelet-based despiking algorithm for large data of structural health monitoring

The last two decades have witnessed a rapid increase in the applications of long-term structural health monitoring technologies to the civil structures. A wealth of field data has been collected by...

Health monitoring of reinforced concrete structures with hysteretic dampers subjected to dynamical loads by means of the acoustic emission energy

A test specimen representing a scaled reinforced concrete frame structure with hysteretic dampers was subjected to sequential seismic simulations of incremental peak acceleration on a 3 × 3 m2 shaking table. From this specimen, two beam–column connections—one exterior and one interior—were continuously monitored with wideband low-frequency acoustic emission sensors properly attached on the structure. Complementing other Non-Destructive Testing (NDT) methods, acoustic emission has proven to be a reliable technology for structural health monitoring within a predictive maintenance program. In particular, it was found that the accumulated energy of acoustic emission signals—properly filtered by means of the root mean squared and the spectral partial power of the waveforms in order to avoid the influence of secondary sources—correlated well with the plastic strain energy released by the specimen. Moreover, the use of the rise angle and average frequency of the filtered acoustic emission signals allowed for successful discernment between tensile and shear cracks in the concrete. The acoustic emission energy associated with shear cracks was found to be substantially lesser than that corresponding to tensile crack. This observation is consistent with the fact that the beams and column of the tested reinforced concrete frame were designed under modern codes aimed at preventing the brittle shear failure of members.

Synthesis of polyvinyl alcohol (PVA) infiltrated MWCNTs buckypaper for strain sensing application

Buckypaper (BP)/polymer composites are viewed as a viable option to improve the strain transfer across the buckypaper strain sensor by means of providing better interfacial bonding between the polymer and carbon nanotubes (CNTs). Multiwall carbon nanotubes (MWCNTs) BP/polyvinyl alcohol (PVA) composites were fabricated by a sequence of vacuum filtration and polymer intercalation technique. The optimized conditions for achieving a uniform and stable dispersion of MWCNTs were found to be using ethanol as a dispersion medium, 54 μm ultrasonic amplitude and 40 min sonication time. FTIR analysis and SEM spectra further confirmed the introduction of oxygenated groups (-COOH) on the surface of MWCNTs BP and the complete infiltration of PVA into the porous MWCNTs network. At MWCNTs content of 65 wt. %, the tensile strength, Young’s modulus and elongation-at-break of PVA-infiltrated MWCNTs BP achieved a maximum value of 156.28 MPa, 4.02 GPa and 5.85%, improved by 189%, 443% and 166% respectively, as compared to the MWCNTs BP. Electrical characterization performed using both two-point probe method and Hall effect measurement showed that BP/PVA composites exhibited reduced electrical conductivity. From the electromechanical characterization, the BP/PVA composites showed improved sensitivity with a gauge factor of about 1.89–2.92. The cyclic uniaxial tensile test validated the high reproducibility and hysteresis-free operation of 65-BP/PVA composite under 3 loading-unloading cycles. Characterization results confirmed that the flexible BP/PVA composite (65 wt. %) with improved mechanical and electromechanical properties is suitable for strain sensing applications in structural health monitoring and wearable technology, as an alternative choice to the fragile nature of conventional metallic strain sensors.

Structural Health Monitoring (SHM) and Determination of Surface Defects in Large Metallic Structures using Ultrasonic Guided Waves.

Ultrasonic guided wave (UGW) is one of the most commonly used technologies for non-destructive evaluation (NDE) and structural health monitoring (SHM) of structural components. Because of its excellent long-range diagnostic capability, this method is effective in detecting cracks, material loss, and fatigue-based defects in isotropic and anisotropic structures. The shape and orientation of structural defects are critical parameters during the investigation of crack propagation, assessment of damage severity, and prediction of remaining useful life (RUL) of structures. These parameters become even more important in cases where the crack intensity is associated with the safety of men, environment, and material, such as ship’s hull, aero-structures, rail tracks and subsea pipelines. This paper reviews the research literature on UGWs and their application in defect diagnosis and health monitoring of metallic structures. It has been observed that no significant research work has been convened to identify the shape and orientation of defects in plate-like structures. We also propose an experimental research work assisted by numerical simulations to investigate the response of UGWs upon interaction with cracks in different shapes and orientations. A framework for an empirical model may be considered to determine these structural flaws.

Multiple Signal Classification-Based Impact Localization in Composite Structures Using Optimized Ensemble Empirical Mode Decomposition

Multiple signal classification (MUSIC) algorithm-based structural health monitoring technology is a promising method because of its directional scanning ability and easy arrangement of the sensor array. However, in previous MUSIC-based impact location methods, the narrowband signals at a particular central frequency had to be extracted from the wideband Lamb waves induced by each impact using a wavelet transform. Additionally, the specific center frequency had to be obtained after carefully analyzing the impact signal, which is time consuming. Aiming at solving this problem, this paper presents an improved approach that combines the optimized ensemble empirical mode decomposition (EEMD) and two-dimensional multiple signal classification (2D-MUSIC) algorithm for real-time impact localization on composite structures. Firstly, the impact signal at an unknown position is obtained using a unified linear sensor array. Secondly, the fast Hilbert Huang transform (HHT) with an optimized EEMD algorithm is introduced to extract intrinsic mode functions (IMFs) from impact signals. Then, all IMFs in the whole frequency domain are directly used as the input vector of the 2D-MUSIC model separately to locate the impact source. Experimental data collected from a cross-ply glass fiber reinforced composite plate are used to validate the proposed approach. The results show that the use of optimized EEMD and 2D-MUSIC is suitable for real-time impact localization of composite structures.

Cost-Effectiveness of Structural Health Monitoring in Fuselage Maintenance of the Civil Aviation Industry †

Although structural health monitoring (SHM) technologies using sensors have dramatically been developed recently, their capability should be evaluated from the perspective of the maintenance industry. As a first step toward utilizing sensors, the objective of the paper is to investigate the possibility of using sensors for inspecting the entire fuselage during C-check. First, we reviewed various sensors for their detection range, detectable damage size, and installed weight, which revealed that the piezoelectric wafer active sensor (PWAS) is the most promising sensor for aircraft SHM. Second, we performed a case study of inspecting the fuselage of Boeing-737NG using PWAS. To maintain the same detecting capability of manual inspection in C-check, we estimated the total number of sensors required. It turned out that utilizing sensors can reduce the maintenance downtime and thus, maintenance cost. However, even with a very conservative estimate, the lifetime cost was significantly increased due to the weight of sensor systems. The cost due to the weight increase was an order of magnitude higher than the cost saved by using SHM. We found that a large number of sensors were required to detect damage at unknown locations, which was the main cause of the weight increase. We concluded that to make SHM cost-effective, it would be necessary either to improve the current sensor technologies so that a less number of sensors are used or to modify the aircraft design concept for SHM.

Effectiveness of RAPID and SSM Algorithms on Composite Scarf Repair

This work focuses on diagnostic methodologies for composite repair patch based on structural health monitoring (SHM) technology. Methodologies based on ultrasonic guided waves (GW) are developed and assessed for monitoring composite scarf repair with piezoelectric transducers. The effectiveness of the RAPID (reconstruction algorithm for probabilistic inspection of defects) algorithm was investigated for adhesively bonded composite patch repair. A composite scarf repair has been weakened by 4-point bending fatigue test and impacted after to generate a Barely Visible Damage (BVID). Both conventional RAPID technique, which requires baseline signals, and the Scaling Subtraction Method (SSM) were applied to detect damage in the bondline. The conventional method showed good performance for defect detection and localization whereas the SSM gives encouraging results for non-linear baseline-free RAPID.

Bayesian Modeling Approach for Forecast of Structural Stress Response Using Structural Health Monitoring Data

AbstractThe advancement in structural health monitoring (SHM) technology has been evolving from monitoring-based diagnosis to monitoring-based prognosis. The structural stress response derived by t...

IFC Monitor – An IFC schema extension for modeling structural health monitoring systems

The pervasive emergence of sensing technologies for structural health monitoring (SHM) and the digitalization ubiquitous in engineering (“Industry 4.0”) pose increasing demands on information modeling concepts in civil engineering. While in building information modeling (BIM) conventional building information (such as geometry, material, or cost) can precisely be described using current modeling standards, information about SHM systems, referred to as “monitoring-related information”, cannot be fully described on a well-defined, formal basis. In this paper, a BIM-based approach towards describing monitoring-related information is proposed, using the Industry Foundation Classes (IFC), an open BIM standard facilitating the interoperability of BIM models, as a formal basis. First, possibilities and constraints of describing monitoring-related information with the IFC schema are discussed. Then, information necessary to describe SHM systems is integrated into a semantic model serving as a technology-independent metamodel. Next, the IFC schema is extended to enable BIM-based descriptions of SHM systems in compliance with IFC modeling capabilities, which is referred to as “IFC Monitor” schema. The IFC Monitor schema is verified with test software used in the official IFC certification program. For validation, a prototype SHM system is formally described using the IFC Monitor schema. The validation aims at checking if the IFC Monitor schema is capable of precisely describing monitoring-related information. As will be shown in this paper, the description of the prototype SHM system meets the requirements of a well-defined IFC model as specified in the official IFC certification program. As a result, the IFC Monitor schema proposed in this study advances BIM-based descriptions of SHM systems in association with structural systems being monitored on a well-defined, formal basis.

Bayesian operational modal analysis of Jiangyin Yangtze River Bridge

Abstract Vibration testing of long span bridges is becoming a commissioning requirement, yet such exercises represent the extreme of experimental capability, with challenges for instrumentation (due to frequency range, resolution and km-order separation of sensor) and system identification (because of the extreme low frequencies). The challenge with instrumentation for modal analysis is managing synchronous data acquisition from sensors distributed widely apart inside and outside the structure. The ideal solution is precisely synchronised autonomous recorders that do not need cables, GPS or wireless communication. The challenge with system identification is to maximise the reliability of modal parameters through experimental design and subsequently to identify the parameters in terms of mean values and standard errors. The challenge is particularly severe for modes with low frequency and damping typical of long span bridges. One solution is to apply ‘third generation’ operational modal analysis procedures using Bayesian approaches in both the planning and analysis stages. The paper presents an exercise on the Jiangyin Yangtze River Bridge, a suspension bridge with a 1385 m main span. The exercise comprised planning of a test campaign to optimise the reliability of operational modal analysis, the deployment of a set of independent data acquisition units synchronised using precision oven controlled crystal oscillators and the subsequent identification of a set of modal parameters in terms of mean and variance errors. Although the bridge has had structural health monitoring technology installed since it was completed, this was the first full modal survey, aimed at identifying important features of the modal behaviour rather than providing fine resolution of mode shapes through the whole structure. Therefore, measurements were made in only the (south) tower, while torsional behaviour was identified by a single measurement using a pair of recorders across the carriageway. The modal survey revealed a first lateral symmetric mode with natural frequency 0.0536 Hz with standard error ±3.6% and damping ratio 4.4% with standard error ±88%. First vertical mode is antisymmetric with frequency 0.11 Hz ± 1.2% and damping ratio 4.9% ± 41%. A significant and novel element of the exercise was planning of the measurement setups and their necessary duration linked to prior estimation of the precision of the frequency and damping estimates. The second novelty is the use of the multi-sensor precision synchronised acquisition without external time reference on a structure of this scale. The challenges of ambient vibration testing and modal identification in a complex environment are addressed leveraging on advances in practical implementation and scientific understanding of the problem.

Image Registration-Based Bolt Loosening Detection of Steel Joints.

Self-loosening of bolts caused by repetitive loads and vibrations is one of the common defects that can weaken the structural integrity of bolted steel joints in civil structures. Many existing approaches for detecting loosening bolts are based on physical sensors and, hence, require extensive sensor deployment, which limit their abilities to cost-effectively detect loosened bolts in a large number of steel joints. Recently, computer vision-based structural health monitoring (SHM) technologies have demonstrated great potential for damage detection due to the benefits of being low cost, easy to deploy, and contactless. In this study, we propose a vision-based non-contact bolt loosening detection method that uses a consumer-grade digital camera. Two images of the monitored steel joint are first collected during different inspection periods and then aligned through two image registration processes. If the bolt experiences rotation between inspections, it will introduce differential features in the registration errors, serving as a good indicator for bolt loosening detection. The performance and robustness of this approach have been validated through a series of experimental investigations using three laboratory setups including a gusset plate on a cross frame, a column flange, and a girder web. The bolt loosening detection results are presented for easy interpretation such that informed decisions can be made about the detected loosened bolts.

Frequency Selective Surface for Structural Health Monitoring

Structural health monitoring (SHM) technologies have attained attention to monitor civil structures. SHM sensor systems have been used in various civil structures such as bridges, buildings, tunnels and so on. However the previous sensor for SHM is wired and encounter with problem to cover large areas. Therefore, wireless sensor was introduced for SHM to reduce network connecting problem. Wireless sensors for Structural Health monitoring are new technology and have many advantages to overcome the drawback of conventional and wired sensor. This project proposed passive wireless SHM sensor using frequency selective surface (FSS) as an alternative to conventional sensors. The electromagnetic wave characteristic of FSS will change by geometrical changes of FSS due to mechanical strain or structural failure. The changes feature is used as a sensing function without any connecting wires. Two type of design which are circular ring and square loop along with the transmission and reflection characteristics of SHM using FSS were discussed in this project. A simulation process has shown that incident angle characteristics can be use as a data for SHM application.

Mechanical impedance based embedded piezoelectric transducer for reinforced concrete structural impact damage detection: A comparative study

Electromechanical admittance (inverse of impedance) based nondestructive evaluation method has been extensively applied to damage detection and health monitoring of engineering structures. This paper proposed an innovative method of using the effective structural mechanical impedance (ESMI) to detect reinforced concrete (RC) structural damages. ESMI based method was first formulated based on a generic two-dimensional model for embedded piezoelectric (PZT) transducer interacting with a host structure. And structural mechanical impedance (SMI) based method using a typical one-dimensional PZT-structure interaction model was also briefly introduced. Validation of the proposed method was investigated in an experiment of testing a RC beam structure with different levels of impact damages, in which raw admittance signatures measured from three embedded PZT transducers were used for extracting ESMI and SMI signatures. Sensitivity of ESMI, SMI and admittance signatures was then qualitatively compared in detecting RC beam structural damages. Additionally, these three signatures based damage quantification was also conducted using two-types of statistical root mean square deviation (RMSD) indices. Results demonstrated that the ESMI signature exhibits well performance in detecting the RC beam structural impact damage. The proposed method provides a promising alternative for precise prediction of RC structural damage especially when the PZT transducers embedded inside structures.

Use of Savitzky-Golay Filter for Performances Improvement of SHM Systems Based on Neural Networks and Distributed PZT Sensors.

A considerable amount of research has focused on monitoring structural damage using Structural Health Monitoring (SHM) technologies, which has had recent advances. However, it is important to note the challenges and unresolved problems that disqualify currently developed monitoring systems. One of the frontline SHM technologies, the Electromechanical Impedance (EMI) technique, has shown its potential to overcome remaining problems and challenges. Unfortunately, the recently developed neural network algorithms have not shown significant improvements in the accuracy of rate and the required processing time. In order to fill this gap in advanced neural networks used with EMI techniques, this paper proposes an enhanced and reliable strategy for improving the structural damage detection via: (1) Savitzky–Golay (SG) filter, using both first and second derivatives; (2) Probabilistic Neural Network (PNN); and, (3) Simplified Fuzzy ARTMAP Network (SFAN). Those three methods were employed to analyze the EMI data experimentally obtained from an aluminum plate containing three attached PZT (Lead Zirconate Titanate) patches. In this present study, the damage scenarios were simulated by attaching a small metallic nut at three different positions in the aluminum plate. We found that the proposed method achieves a hit rate of more than 83%, which is significantly higher than current state-of-the-art approaches. Furthermore, this approach results in an improvement of 93% when considering the best case scenario.

Spatially continuous fully distributed microwave-photonic and coaxial cable sensors for structural health monitoring

This report summarises our recent research progresses in developing new distributed sensing technologies for structural health monitoring (SHM) based on the new microwave-photonic and coaxial cable platforms. The two new platforms have the combined advantages of high measurement resolution, large strain capability, long span of coverage, easy-to-install/embed, spatial continuity (no dark zones), and flexible gauge lengths. The new distributed sensing technology may bring a revolutionary tool to address a number of insufficiently-studied areas in SHM, such as distributed monitoring of cracks and corrosion, post seismic monitoring to ensure mission-critical services in disaster responses and emergency evacuations, study of the progressive structural failures/collapses under extreme loads, and high performance smart structures.

Advances in Smart Technologies for Structural Health Monitoring of Cable-stayed Bridges

This study deals with the general problem of developing smart technologies for vibration and impedance-based structural health monitoring (SHM) of cable-stayed bridges. The following approaches are implemented to achieve the objective. Firstly, vibration- and impedance-based SHM methods suitable for cable-stayed bridge are briefly outlined. Secondly, smart sensors are designed for vibration- and impedance-based SHM. Thirdly, the practicality of the smart sensor system is evaluated on a real cable-stayed bridge, Hwamyung Bridge in Korea. The system's performance is experimentally analyzed under various cable forces and weather conditions. Finally, the experimental modal parameters are identified by numerical modal analyses of the target bridge. Also, its structural parameters are estimated from the vibration-based structural identification using experimental modal parameters.

A machine‐learning approach to load‐monitoring based on guided waves

AbstractThe research field of structural health monitoring (SHM) describes the way to assess the structural integrity. The research objectives are broadly diversified, which are all addressing the improvement of SHM technologies. It is important to optimise the capability of sensor networks by applying intelligent signal processing models. These intelligent systems predict the health state based on acquired data in a real‐time environment. Piezoelectric sensor networks and guided‐wave based analyses are combined with machine learning to create an efficient load‐monitoring routine. This load‐monitoring approach is chosen to track any changes in the environmental conditions, which causes most of the problems in reference signal based damage detection. Therefore, the proposed load‐monitoring method can be used to compensate the influence of external changes on the acquired signals and improve the performance and robustness of future SHM systems. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Integration of scheduled structural health monitoring with airline maintenance program based on risk analysis

The aerospace industry is striving to reduce the aircraft operating costs while maintaining required safety level. Emerging technologies such as the structural health monitoring to reduce long-term maintenance cost and increase aircraft availability are promoted by the manufacturers. To successfully integrate the structural health monitoring technology into the current maintenance process of modern commercial aviation, a clear definition of the structural-health-monitoring-based maintenance operational concept and the system level requirements is required. This article proposed a structural health monitoring operational concept and the associated maintenance cost modeling and risk assessment methods for the implementation of the structural health monitoring in commercial aviation industry. The developed methodology provides a tool to determine the optimal scheduled structural health monitoring inspection interval and repair decision thresholds for approved scheduled structural health monitoring task. A simulated case study is carried out to demonstrate the structural health monitoring operational concept and how an optimal maintenance strategy can be determined using the proposed methodology. Preliminary results show that the integration of the structural health monitoring into the existing maintenance process can reduce the maintenance cost compared to that of the current practice using the traditional Non-Destructive Evaluation (NDE) techniques while maintaining the risk below an acceptable level.

Towards structural health monitoring of space vehicles

PurposeThe purpose of this paper is to report the results of a recent project of complex tests on the survival of structural health monitoring (SHM) technology with piezo wafer active sensors (PWAS) and electromechanical impedance spectroscopy (EMIS) at simulating the concomitant action of harsh conditions of outer space: extreme temperatures, radiations, vacuum.Design/methodology/approachThe tests were conducted on PWAS, consists in adhesive and aluminium discs as structural specimens, with PWAS bonded on them. The substantiating of PWAS-EMIS-based SHM technique consists the fact that real part of the PWAS electromechanical impedance spectrum follows with fidelity the resonance behaviour of the structure vibrating under the PWAS excitation. This EMIS signature is very sensitive to any structural changes and, on this basis, can be monitored the onset and progress of structural damages such as fatigue, cracks, corrosion, etc.FindingsThe conclusion of the tests is that the cumulative impact of severe conditions of temperature, radiation and vacuum has not generated decommissioning of sensors or adhesive, which would have meant the compromise of the methodology. A second important outcome is linked to the capability of this methodology to distinguish between the damages of mechanical origin and the false ones, caused by environmental conditions, which are, basically, harmless.Originality/valueThe question of transfer of PWAS-EMIS-based SHM technology to space vehicles and applications received, as a novelty, a first and encouraging response.

Middleware and communication technologies for structural health monitoring of critical infrastructures: A survey

Critical Infrastructure Protection (CIP) has become a priority for every country around the world with the aim of reducing vulnerabilities and improving protection of Critical Infrastructures (CI) against terrorist attacks or natural disasters, among other threats. As part of CIP, Structural Health Monitoring (SHM) is defined as the process of gathering basic information that allows detecting, locating and quantifying vulnerabilities early on (fatigue cracking, degradation of boundary conditions, etc.) thereby improving, the resilience of the CI. Recent advances in electronics, wireless communication and software are expected to open the door to a new era of densely connected devices sharing information worldwide, known as the Internet of Things (IoT), in which Wireless Sensor Networks (WSNs) play an important role. The combined use of IoT/WSNs together with industrial sensors in SHM provide an ad-hoc, inexpensive and easy way of deploying a monitoring system, where data can be shared among different entities. SHM requirements are challenging and diverse and therefore several different technologies may be used in the same deployment. At the same time the use of a middleware can substantially simplify and speed up the development of applications for SHM. Taking into account the challenges of SHM systems, this paper provides a review of the most novel and relevant wireless technologies and a state-of-the-art middleware for WSNs focusing on SHM specific requirements.