Use In Structural Health Monitoring Research Articles

Free Vibration Response Studies on Bridges

Abstract Bridges are essential components of the transportation network. These are unique constructions that serve as a transportation link between regions separated by physical barrier. Because of their critical role in ensuring passenger safety, these structures require constant maintenance. When vehicle cross a bridge, the structural components vibrate, which over time may cause damage to these components. The preferred method of maintaining structural health is currently seeing a rise in the use of Structural Health Monitoring (SHM) systems on infrastructure, especially bridges. In order to overcome the drawbacks of visual inspection methods, it is crucial to continuously monitor the integrity and analyse the dynamic properties of bridges. This work provides a comprehensive analysis of bridge health monitoring, emphasizing three key elements: vibration-based structural health monitoring (SHM), vehicle-bridge interaction analysis, and machine learning integration. The work improves monitoring, maintenance, and safety protocols by expanding our knowledge of structural integrity assessment for bridges by a detailed examination of the convergence of these domains. Notably, the work uses a condensed 2D model to forecast bridge natural frequencies Understanding the dynamic behaviour and structural integrity of the vital infrastructures is greatly aided by free vibration response research on bridges. The results, insights, and new directions in the subject of bridge free vibration analysis are summarized in this review paper. The uses and drawbacks of several analytical, numerical, and experimental techniques are covered. The research on the free vibration response of bridges is also reviewed in this review paper, with an emphasis on new developments incorporating machine learning principles.

A Structural Health Monitoring System for Bond Line Flaws Detection on a Full-Scale Wingbox Section Demonstrator

In recent years, there has been a significant increase in the use of structural health monitoring (SHM) technologies as systems for monitoring the integrity of aircraft’s structures. The use of compact and embeddable sensor networks, like the ones based on fibre optics (FO), is particularly attractive from the perspective of releasing an integrated structural system with intrinsic sensing capacity. Usually, an SHM system architecture is completed by a dedicated algorithm that processes the data gathered from the sensors to elaborate on the level of damage currently suffered by the structure, with the further possibility of providing information to the relevant specialists involved with its supervision. One of the main SHM applications that is attracting major interest is related to the inspection and detection of anomalies in bonded joints, which is extremely relevant in many composite realizations. Aeronautical regulations allow the use of bonded joints on an aircraft’s primary structure but require the implementation of a means to ensure their absolute safety, such as the introduction of further mechanical links aimed at stopping the propagation of a possible flaw or the availability of Non-Destructive Inspection (NDI) systems to prove the absence of relevant damaged areas. Generally, the main typical defects occurring during the manufacturing of bonded joints include adhesive curing, kissing bonds, poor porosity, and poor surface preparation. The current NDI systems more widely used and available to detect defects are still inaccurate due to the lack of standard procedures for the creation of representative defects in a controlled manner, which would allow for the development of reliable methodologies and tools able to ensure the safety of a bonded joint, as required by safety regulations. This paper shows the results relative to the implementation of an SHM system developed by the Italian Aerospace Research Centre (CIRA) aimed at monitoring the bonding lines between spar caps and panels of a typical composite wingbox section and detecting faults in location and length. The work was performed during typical ground static tests by using a fibre optical sensing network embedded within relevant adhesive paste layers during the manufacturing process of the structure. In the reported investigation, the SHM system assumed the function of an NDI system tool. The results show that the developed SHM system has good reliability for the detection of both the position and size of damage areas that were artificially inserted within the test article during the bonding phase, showing its potential as a candidate to be used as a tool to verify the conditions of a bonded joint, as required by aviation authorities’ regulations.

A PZT-Based Smart Anchor Washer for Monitoring Prestressing Force Based on the Wavelet Packet Analysis Method

Prestressed steel strands in prestressed structures offset or reduce the tensile stress caused by external loads, making them the primary load-bearing components. Great concerns have been raised about prestress monitoring due to the growing use of structural health monitoring (SHM). Piezoceramic (PZT) active sensing methods are commonly used in this field. However, there appears to be a problem of “energy saturation” in the utilization of piezoceramic active sensing methods. In this study, a smart anchor washer with semi-cylinders was developed to alleviate the saturation problem. An intelligent monitoring system is formed by combining the upper and lower annular cylinders with two piezoelectric patches. The piezoelectric patch on the upper annular cylinder is used as an actuator to emit signals through the contact interface of the smart anchor washer, which are then received by the piezoelectric patch on the lower annular cylinder. Based on wavelet packet decomposition, we investigate the correlation between the energy of the received signal and the applied tension force. Finally, a prestressing force index is developed for monitoring prestressing force using Shannon entropy. It is found that the index decreases with the increase in tension. The proposed design and index are also sensitive to early monitoring of prestressing force and can be used to monitor the entire prestressing process of steel strands.

A Bayesian network-based framework for SHM data fusion supporting bridge management

Guidelines for Risk Classification and Management, Safety Assessment and Monitoring of Bridges, issued in 2020 by the Italian Ministry of Sustainable Mobility, promote the use of Structural Health Monitoring (SHM) systems for risk assessment of bridges. Prestressed or ordinary reinforced concrete bridges are designed to maintain their functionality over a long period of time. However, during their service life, such structures may be exposed to ever- increasing traffic volumes, extreme weather conditions and/or marine environments and so on. Therefore, constant maintenance and timely interventions are crucial to ensure the functionality of the transportation network. The current management process is mainly based on visual inspections, while the aggregation of information coming from multiple sources is still a major challenge. In this context, the main objective of the present work is to develop a general Bayesian framework capable of processing the different sources of information based on a Bayesian network (BN), a probabilistic graphical model that represents a set of variables and their conditional dependencies. A BN is a useful tool for the management of bridges since it allows the prediction of damage states and failure conditions based on the knowledge by visual inspections and SHM. The combined use of SHM and Bayesian approaches can reduce the overall risk of failure increasing the efficiency of the infrastructure system. The procedure is exemplified and validated with reference to a simply supported post-tensioned case study bridge.

Directional Multi-Frequency Guided Waves Communications Using Discrete Frequency-Steerable Acoustic Transducers.

A novel directional transducer based on Guided Waves (GW) is introduced in this paper, designed for use in structural health monitoring (SHM) and acoustic data communication applications, i.e., systems in which the elastic medium serves as a transmission channel and information is conveyed through the medium via elastic waves. Such systems can overcome difficulties associated with traditional communication methods like wire-based or radio frequency (RF), which can be complex and have limitations in harsh environments or hard-to-reach places. However, the development of these techniques is hampered by GW dispersive and multi-modal propagation and by multi-path interference. The shortcomings can be effectively addressed by employing Frequency Steerable Acoustic Transducers (FSATs), which leverage their inherent directional capabilities. This can be achieved through the exploitation of a frequency-dependent spatial filtering effect, yielding to a direct correlation between the frequency content of the transmitted or received signals and the direction of propagation. The proposed transducer is designed to actuate or sense the A0 Lamb wave propagating in three orientations using varying frequencies, and has three channels with distinct frequencies for each direction, ranging from 50 kHz to 450 kHz. The transducer performance was verified through Finite Element (FE) simulations, accompanied by experimental testing using a Scanning Laser Doppler Vibrometer (SLDV). The unique frequency-steering capability of FSATs is combined with the On-Off Keying (OOK) modulation scheme to achieve frequency directivity in hardware, similar to ongoing research in 5G communications. The MIMO capabilities of the transducer were finally tested over a thin aluminum plate, showing excellent agreement with the FE simulation results.

A BIM-Based Model for Structural Health Monitoring of the Central Body of the Monserrate Palace: A First Approach

The preservation and safeguarding of built cultural heritage is a permanent concern for institutions. These structures were generally poorly prepared for movement triggered by natural disasters, a situation further complicated in the case of earthquakes, as each building has a unique structural dynamic linked to its geometry, materials, method of construction and environmental conditions. The use of structural health monitoring (SHM) systems integrating monitoring techniques as well as inspection and structural analyses has gained great relevance in the appearance of low-cost IoT (Internet of Things) sensors on the market. In this paper, an IoT BIM-based solution is presented for real-time monitoring using low-cost sensors in the scope of building SHM systems. The case study takes place at the central body of the Palace of Monserrate, one of the most distinguished elements of the Cultural Landscape of Sintra. An H-BIM model was created in Autodesk Revit® software (version 2022 and 2023) based on a point cloud, and used as the basis for the numerical model developed in 3MURI. A MeM low-cost sensor was installed on the third floor of the central tower of the Monserrate Palace in Sintra, and the data gathered were recorded in the H-BIM model. The capacity to acquire real-time information on a structure’s vibration, both during normal operation and after an extraordinary occurrence, could allow the application of more effective maintenance and repair practices, resulting in lower operating costs and allowing for the best management of built cultural heritage.

An ultrasonic guided waves based prognostic approach for predictive maintenance: Experimental study cases

This paper concerns the use of ultrasonic guided waves (UGW) in predictive maintenance (PM). Generally, PM is based on machine or process parameters such as current, voltage, temperature and pressure. However, research studies on the use of structural health monitoring (SHM) techniques in PM are seldom (to the best knowledge of the authors). Hence, the main purpose of the present study is to investigate the feasibility of using UGW, in order to reinforce the PM of a given installation. An approach that combines diagnostic and prognostic models is developed. The predictive model is hybrid and based on Empirical Mode Decomposition (EMD) and Autoregressive Integrated Moving Average (ARIMA). The built model is applied on data collected from two kinds of structures, namely flexible and rigid risers. Residual useful life (RUL) of each structure is estimated and compared with its simulated actual value. The paper provides a fruitful discussion about the applicability of UGW for prognostic as this is an important step in SHM and there is still a lack of research related to this topic.

A novel index for vibration-based damage detection technique in laminated composite plates under forced vibrations: experimental study

In this study, vibration modal analysis has been used to experimentally investigate the detection of fiber breakage (FB) and delamination in laminated composite plates. The modal analysis was performed on two arrangement styles of carbon fiber reinforced polymer (CFRP) laminated plates under forced vibrations. The main contribution in this study is that a new index named Improved Curvature Damage Factor (ICDF) was introduced to detect and localize damages in composite structures. It is a developed version of an earlier index based on the change in mode shape curvature (MSC) of dynamic response. To include the effect of more than one mode shape Cumulative Improved Damage Factor (CICDF) was proposed. The ICDF index was validated with a number of earlier studies that investigated the damage detection in different structures in order to approve the efficiency of this index. A scanning laser vibrometer was also used to experimentally examine the modal-based and forced responses for intact and damaged plates. Various experimental tests were used to verify the advantages of the proposed methodology and its use in structural health monitoring. In addition, the analysis of experimental results showed that the mutation caused by FB is almost double that of the value of delamination peaking.

Smartphone Application for Structural Health Monitoring of Bridges.

The broad availability and low cost of smartphones have justified their use for structural health monitoring (SHM) of bridges. This paper presents a smartphone application called App4SHM, as a customized SHM process for damage detection. App4SHM interrogates the phone's internal accelerometer to measure accelerations, estimates the natural frequencies, and compares them with a reference data set through a machine learning algorithm properly trained to detect damage in almost real time. The application is tested on data sets from a laboratory beam structure and two twin post-tensioned concrete bridges. The results show that App4SHM retrieves the natural frequencies with reliable precision and performs accurate damage detection, promising to be a low-cost solution for long-term SHM. It can also be used in the context of scheduled bridge inspections or to assess bridges' condition after catastrophic events.

Strain-Field Modifications in the Surroundings of Impact Damage of Carbon/Epoxy Laminate.

The relationship between deformation and stress is crucial for any elasto-plastic body. This paper deals with the experimental identification of the basic parameters of the composite laminate model in relation to the finite element model. Standardized tensile, impact, and post-impact tests on a carbon fiber-reinforced epoxy laminate were used. The method by which the elasticity and failure parameters were obtained from the initial components is described. In the article, the modes of initiation and complete failure of samples in tensile tests, which are compared with the simulation, are presented. Furthermore, the article deals with the issue of the generation and detection of damage by low-speed impact, which can be caused by contact with moving objects, due to improper handling or maintenance. The results of impact analysis simulations are shown in the context of strain-field distribution changes obtained with the help of digital image correlation. The results showed high agreement between the calculations and the experiments. Based on this agreement, simulations of impact damage for various energies were performed. These simulations were used to determine the approximate sizes of the affected zones in relation to the impact energy. The results are finally discussed in the context of the possible use of structural health monitoring based on strain modifications.

Vibration-based Damage Localization and Quantification Framework of Large-Scale Truss Structures

The use of structural health monitoring (SHM) systems on a regular basis is critical to achieve early damage detection, avoid unpredicted failures, and perform cost-effective maintenance planning. The main objective of this work is to present a model-based Damage Detection Framework for truss structural systems that uses output-only vibration measurements. Model-based methods provide much more comprehensive information about the condition of the monitored system than the data-driven and also allow the prediction of the location and level of damage. The measured vibration response of a healthy structural system under operational vibrations is employed to tune a parameterized FE model using state-of-the-art FE model updating techniques to obtain an optimal numerical model of the structural system. Based on the optimal FE model, a set of damaged FE models is generated for selected damage scenarios. A damage approximation approach that represents local damage with uniform stiffness reduction is also presented. In the Damage Detection Framework, the vibration data records for both the “healthy” and the “damaged” structure and the results from multiple numerical analysis on the “healthy” and the “damaged” FE models are used. The transmittance functions for the “healthy” and “damaged” states of the structure and the FE models are derived to calculate the damage indicators. Using these indicators, potentially damaged structural members are identified, grouped, and compared to finally locate the specific damaged member. The proposed framework provides both accurate damage localization and damage quantification using a limited number of sensors for unknown input excitation. Herein, the case study used is a laboratory steel truss bridge.

Use of structural health monitoring to extend the service life of the Diefenbaker Bridge

Use of structural health monitoring to extend the service life of the Diefenbaker Bridge

Bayesian probabilistic assessment of occupant comfort of high-rise structures based on structural health monitoring data

Comfort performance of high-rise structures during strong winds is significant to habitants. Despite the significance, procedures for evaluating occupant comfort in serviceability limit states have not been as well developed as those for strength-based design of high-rise structures. One of the difficulties arises from uncertainties associated with the parameters in occupant comfort assessment, which pertain to the acceleration response magnitude and its relationship to human reaction to the motion. The comfort assessment is in general conducted by examining whether the wind-induced acceleration response satisfies some occupant comfort criteria. Such a deterministic approach, however, fails to account for uncertainty inherent in the wind-induced acceleration response as it is affected by the wind field of stochastic nature and uncertainty about the aerodynamic loads and the structure’s dynamic behavior. In view of this, a Bayesian probabilistic approach is proposed in this study to evaluate the occupant comfort of high-rise structures. First, a Bayesian regression model is formulated for characterizing wind-induced acceleration responses of a structure by use of structural health monitoring (SHM) data acquired during strong winds, thereby enabling to account for the uncertainty contained in the monitored acceleration responses and quantify the uncertainty in modeling and prediction. Based on the predicted acceleration distribution and reliability theory, a safety index is then elicited to perform probabilistic assessment of occupant comfort in wind-induced motion of the structure. In the case study, field monitoring data acquired from a supertall structure of 600 m high during six tropical cyclones are used to illustrate the proposed approach, including the evaluation of occupant comfort of the structure under extreme wind speeds.

Modal analysis of cracked beam with a piezoelectric layer

Piezoelectric material was employed first as sensor/actuator for structural control and then it has got an effective use for structural health monitoring and repairing damaged structures. In this report, modal analysis of cracked beam with piezoelectric layer is carried out to investigate effect of crack and piezoelectric layer thickness on natural frequencies of the structure and output charge generated in the piezoelectric layer by vibration modes. Governing equations of the coupled structure are established using the double beam model and two-spring (translational and rotational) representation of crack and solved to obtain the modal parameters including the output charge associated with natural modes acknowledged as modal piezoelectric charge (MPC). Numerical examples have been examined for validation and illustration of the developed theory.

Self-sensing cement-based sensor with carbon nanotube: Fabrication and properties – A review

Structural health monitoring is currently becoming more and more vital as it detects damage and warns users early. Since metallic sensors are less reliable and incompatible with concrete, their use in structural health monitoring is limited. This leads to the development of a nanomaterial embedded cement-based sensor. Cement composite is converted to a cement-based sensor due to its piezoresistive property. Thus, the paper examines the use of carbon nanotubes (CNT) in the development of cement-based sensors. The properties and optimum dosage of the CNT are also investigated to prepare the cement-based sensor. The manufacturing process determines the cement-based sensor properties, which is carefully examined and a manufacturing process is also proposed. The review explored that the sensor shows greater elongation capacity, compressive and flexural strength due to the CNT reinforcing capability, CNT properties, and stable interface connection with O–H bond formation. The cement-based conductive properties increase with an increase in CNT content due to contact conduction and tunnelling conduction. The analysis shows that the CNT disperses and distributes favourably in the composite through the acid treatment and sonication process, resulting in improved mechanical, conductive and piezoresistive properties. The cement composite exhibits stable piezoresistive properties with hybrid embedment due to pore filling capability, contact point increase, fiber spacing shortening and tunnelling effect. The reason for property enhancement was validated with the help of SEM investigations. This review gives a detailed insight into the material properties, sensor fabrication, mechanical, conductive and piezoresistive properties of the developed cement-based sensor.

Strain monitoring based bridge reliability assessment using parametric Bayesian mixture model

Bridge condition assessment by use of structural health monitoring (SHM) data has been recognized as a promising approach towards the condition-based preventive maintenance. In-service bridges are normally subjected to multiple types of loads such as highway traffic, railway traffic, wind and thermal effect, resulting in heterogeneous and multimodal data structure of strain/stress responses. This study aims to develop an SHM-based bridge reliability assessment procedure in terms of parametric Bayesian mixture modelling. The parametric mixture model admits representation of multimodal structural responses, while the Bayesian paradigm enables both aleatory and epistemic uncertainties to be accounted for in modelling. By defining appropriate priors for the mixture parameters that are viewed as random variables to interpret the model uncertainty, an analytical form of the full conditional posteriors is derived. A Markov chain Monte Carlo (MCMC) algorithm in conjunction with Bayes factor is explored to determine the optimal model order and estimate the joint posterior of the mixture parameters. In full compliance with the Bayesian framework, a conditional reliability index is elicited with the parametric Bayesian mixture model by using the first-order reliability method. The estimated value of the reliability index, which serves as a quantitative measure of health condition for the in-service bridge, can be successively updated with the accumulation of monitoring data. The proposed method is exemplified by using one-year strain monitoring data acquired from the instrumented Tsing Ma Suspension Bridge, in which the evolution of the estimated reliability index is obtained.

A Bayesian approach for condition assessment and damage alarm of bridge expansion joints using long-term structural health monitoring data

Premature failure of bridge expansion joints has been increasingly observed in recent years, and nowadays it becomes a major concern of bridge owners. A better understanding of their performance in service is highly desired. Deterministic linear regression models between bridge temperature and expansion joint displacement have widely been adopted to characterize the in-service performance of bridge expansion joints. When such a regression pattern is elicited using real-time monitoring data, the deterministic models fail to account for uncertainty inherent in the monitoring data and interpret the model error. In this study, a probabilistic approach for characterization of the regression pattern between bridge temperature and expansion joint displacement by use of Structural Health Monitoring (SHM) data and for SHM-based condition assessment and damage alarm of bridge expansion joints is developed in the Bayesian context. The proposed approach enables to account for the uncertainty contained in the monitoring data and quantify the model error and the prediction uncertainty. By combining the Bayesian regression model and reliability theory, an anomaly index is formulated to evaluate the health condition of the expansion joint when newly collected monitoring data are available and to provide damage alarm once the probability of damage exceeds a certain threshold. In the case study, real-world monitoring data acquired from a cable-stayed bridge are used to illustrate the proposed approach, including examining the appropriateness of the design values of expansion joint displacements under extreme temperatures in serviceability limit state.

Numerical simulation of the propagation of Lamb waves and their interaction with defects in C-FRP laminates for non-destructive testing

The constantly growing demand for cost-intensive lightweight structures is accompanied by an increased demand for reliable non-destructive testing (NDT) methods. Previous investigations have shown that Lamb Waves (LW) are very well suited for the use in Structural Health Monitoring (SHM) due to their relatively undamped propagation over long distances. Therefore the numerical modelling of a non-contact and automated system for non-destructive testing of C-FRP laminates was investigated in the present study. The method is based on the propagation of LW, which are excited by an actuator-driven piezo transducer and interact with defects in the structure. In contrast to classical Structural Health Monitoring, a scanning inspection system is used instead of a sensor permanently integrated into the structure to detect the measurement signals. For the representation of the wave propagation a three-dimensional simulation model with the commercial FEM software PZFlex was modelled. The wave interactions in defective C-FRP coupons are investigated and visualized through different output formats like A-, B- and C-Scans. Additionally the model allows the determination of the dispersion characteristics of the LW in the examined laminate. The validation of the numerical results was successfully carried out at different levels with the help of experimental investigations.

A Low-Cost Phase-OTDR System for Structural Health Monitoring: Design and Instrumentation

The design, development, and testing of a low-cost phase optical time-domain reflectometry (Phase-OTDR) system, intended for use in structural health monitoring (SHM) applications, are presented. Phase-OTDR is a technology that is growing and evolving at an impressive rate. Systems based on this principle are becoming very sensitive and elaborate and can perform very accurate condition monitoring, but at the same time, they are critically alignment-dependent and prohibitively costly to be considered as viable options in real field applications. Certain Phase-OTDR systems have been applied in real field studies, but these examples are mostly a proof-of-concept. The system presented here is the result of a compromise between performance and cost, using commercial components, specifically combined and tuned for SHM applications. The design and implementation of all the electronic and optoelectronic steps are presented, and the operation of the system is demonstrated, achieving a spatial resolution of ~6 m over 5 km. This work provides useful engineering guidelines for the low-cost implementation of Phase-OTDR systems. It is anticipated that the affordable development of such interrogation systems will promote their use in a wide range of SHM applications with moderate monitoring requirements and will assist the penetration of Phase-OTDR technology in the industry.

High-temperature strain monitoring of stainless steel using fiber optics embedded in ultrasonically consolidated nickel layers∗∗Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish

Fiber optic sensors have long been considered for use in structural health monitoring of components because of their small size, high accuracy, and their ability to perform spatially distributed strain measurements when embedded within a component or on its surface. The typical method for transferring strain from the component to the fiber is to use an epoxy, which may not survive extended exposure to high temperatures, thus necessitating a more elaborate technique to embed the fibers. This work represents a first step towards using additive manufacturing techniques to embed fiber optic sensors on stainless steel components for high-temperature strain monitoring, including quantification of the differential thermal strains that develop between the fiber and the steel substrate. Copper-coated optical fibers were embedded in nickel layers on top of a stainless steel substrate using an ultrasonic additive manufacturing technique. The embedded fibers showed minimal signal attenuation and clear compressive strain after embedding. Heating the embedded fibers in steps to temperatures of 300 °C–400 °C resulted in measured strains with values between the expected thermal strain in stainless steel and nickel. Finite element simulations confirm the measured strain values and show that the thermal strain depends on the thickness of the nickel layers deposited on top of the stainless steel substrate. While the fibers failed before reaching temperatures of 500 °C, it is suspected that these failures occurred due to a combination of (1) the lack of strain relief, (2) the high-temperature oxidation of the fiber’s copper coating, and (3) improper sizing of the machined channel in which the fiber is placed prior to embedding. If proper coating selection and sizing of the channel can prevent the failures observed in this work, the next step would be to monitor strain during mechanical loading at high temperatures.