Health Monitoring Of Civil Structures Research Articles

Development of a Wall-Climbing Drone Capable of Vertical Soft Landing Using a Tilt-Rotor Mechanism

Wall-climbing drones have many applications, including structural health monitoring of civil structures, such as bridges and high-rise buildings, cleaning of solar panels to improve power generation efficiency, and airplane visual inspections. For these applications, the drone requires a high-payload capacity, and consequently the size and weight of the drone increase. The drone also should not damage the target structures considering the purpose of its mission. Our previous versions of a wall-climbing drone could have high-impact force on the surface where the drone perches and on the platform itself because of the impact caused by a fast pose change and landing speed. In order to overcome this potential risk, a mechanism and a control algorithm for perching on a vertical surface through low-speed pose change are proposed in this paper. The drone platform is based on an X-configuration quadcopter, and a tilt-rotor mechanism is incorporated into the two axes, such that the front thrusters and the rear thrusters are paired. The vertical soft landing mechanism using the tilt-rotors is validated by the experimental tests of the prototype.

Radiation-Induced Effects on Fiber Bragg Gratings Inscribed in Highly Birefringent Photonic Crystal Fiber

The fiber Bragg gratings (FBGs) inside photonic crystal fibers (PCFs) having high fiber birefringence, such as in PCF with butterfly shape microstructure, are characterized by two Bragg peaks. The spectral distance between these two peaks is not affected by a change of temperature or by a longitudinally applied strain, but this spectral separation evolves when the sensor is subjected to a transversal strain. This makes such FBGs inscribed in highly birefringent PCFs very interesting for structural health monitoring of civil structures, as well as for operation in harsh environments such as the ones associated with the nuclear industry. In this paper, we monitor the radiation-induced Bragg wavelength shift (RI-BWS) of the punctual sensor and the radiation-induced attenuation in the fiber used for its inscription up to a dose of 1.5 MGy(SiO2). Even if we observe a RI-BWS for both Bragg peaks, the spectral distance between them only slightly evolves, of less than 10 pm. These gratings are then very promising sensors for the structural health monitoring of nuclear facilities.

An Automatic Modal Identification Procedure for the Permanent Dynamic Monitoring of the Sanctuary of Vicoforte

ABSTRACTThe interest in automatic modal parameter extraction techniques has increased significantly in recent years because of the rising demand for Continuous Structural Health Monitoring (CSHM) of civil structures and infrastructures. The wider use of CSHM is related to its capability for early damage detection and therefore to its usefulness for planning maintenance and strengthening interventions. This article presents the main steps followed in creating a robust routine to run on an online basis the first-ever Continuous Vibration-Based Structural Health Monitoring (CVB-SHM) of a large masonry oval dome, i.e., the dome of the Regina Montis Regalis Basilica, also known as Sanctuary of Vicoforte. The procedure uses an output-only stochastic subspace identification method complemented by an automatic analysis applied to identification results, which are typically provided in terms of stabilization diagrams. In this study, the fully automatic procedure also includes a hierarchical clustering algorithm to separate authentic from spurious modes.

Performance comparison of Kalman−based filters for nonlinear structural finite element model updating

Finite element (FE) model updating has emerged as a powerful technique for structural health monitoring and damage identification of civil structures. Updating mechanics-based nonlinear FE models allows for a complete and comprehensive damage diagnosis of large and complex structures, but it is computationally demanding. This paper first introduces an Iterated Extended Kalman filter (IEKF) to update mechanics-based nonlinear FE models of civil structures. Different model updating techniques using the Extended Kalman filter (EKF), Unscented Kalman Filter (UKF) and IEKF, are then compared for their performance in terms of convergence, accuracy, robustness, and computational demand. Finally, a non-recursive estimation procedure is presented and its effectiveness in reducing the computational cost, while maintaining accuracy and robustness, is demonstrated. An application example is presented based on numerically simulated response data for a three-dimensional 5-story 2-by-1 bay reinforced concrete (RC) frame building subjected to bi-directional earthquake excitation. Excellent estimation results are obtained with the EKF, UKF, and IEKF used in conjunction with the proposed non-recursive estimation approach. Because of the analytical linearization used in the EKF and IEKF, abrupt and large jumps in the estimates of the model parameters are observed with these filters, which may lead to divergence of the nonlinear FE model solution procedure. The UKF slightly outperforms the EKF and IEKF, but at a higher computational cost.

Damage Detection for Civil Structural Health Monitoring Application - A Case Study of the Steel Grid Bridge Structural Model

Damage Detection for Civil Structural Health Monitoring Application - A Case Study of the Steel Grid Bridge Structural Model

A Novel Parallel Auto-Encoder Framework for Multi-Scale Data in Civil Structural Health Monitoring

In this paper, damage detection/identification for a seven-storey steel structure is investigated via using the vibration signals and deep learning techniques. Vibration characteristics, such as natural frequencies and mode shapes are captured and utilized as input for a deep learning network while the output vector represents the structural damage associated with locations. The deep auto-encoder with sparsity constraint is used for effective feature extraction for different types of signals and another deep auto-encoder is used to learn the relationship of different signals for final regression. The existing SAF model in a recent research study for the same problem processed all signals in one serial auto-encoder model. That kind of models have the following difficulties: (1) the natural frequencies and mode shapes are in different magnitude scales and it is not logical to normalize them in the same scale in building the models with training samples; (2) some frequencies and mode shapes may not be related to each other and it is not fair to use them for dimension reduction together. To tackle the above-mentioned problems for the multi-scale dataset in SHM, a novel parallel auto-encoder framework (Para-AF) is proposed in this paper. It processes the frequency signals and mode shapes separately for feature selection via dimension reduction and then combine these features together in relationship learning for regression. Furthermore, we introduce sparsity constraint in model reduction stage for performance improvement. Two experiments are conducted on performance evaluation and our results show the significant advantages of the proposed model in comparison with the existing approaches.

PCA-based filtering of temperature effect on impedance monitoring in prestressed tendon anchorage

For the long-term structural health monitoring of civil structures, the effect of ambient temperature variation has been regarded as one of the critical issues. In this study, a principal component analysis (PCA)-based algorithm is proposed to filter out temperature effects on electromechanical impedance (EMI) monitoring of prestressed tendon anchorages. Firstly, the EMI monitoring via a piezoelectric interface device is described for prestress-loss detection in the tendon anchorage system. Secondly, the PCA-based temperature filtering algorithm tailored to the EMI monitoring of the prestressed tendon anchorage is outlined. The proposed algorithm utilizes the damage-sensitive features obtained from sub-ranges of the EMI data to establish the PCA-based filter model. Finally, the feasibility of the PCA-based algorithm is experimentally evaluated by distinguishing temperature changes from prestress-loss events in a prestressed concrete girder. The accuracy of the prestress-loss detection results is discussed with respect to the EMI features before and after the temperature filtering.

Development of a Wireless Unified-Maintenance System for the Structural Health Monitoring of Civil Structures.

A disaster preventive structural health monitoring (SHM) system needs to be equipped with the following abilities: First, it should be able to simultaneously measure diverse types of data (e.g., displacement, velocity, acceleration, strain, load, temperature, humidity, etc.) for accurate diagnosis. Second, it also requires standalone power supply to guarantee its immediate response in crisis (e.g., sudden interruption of normal AC power in disaster situations). Furthermore, it should be capable of prompt processing and realtime wireless communication of a huge amount of data. Therefore, this study is aimed at developing a wireless unified-maintenance system (WUMS) that would satisfy all the requirements for a disaster preventive SHM system of civil structures. The WUMS is designed to measure diverse types of structural responses in realtime based on wireless communication, allowing users to selectively use WiFi RF band and finally working in standalone mode by means of the field-programmable gate array (FPGA) technology. To verify its performance, the following tests were performed: (i) A test to see how far communication is possible in open field, (ii) a test on a shaker to see how accurate responses are, (iii) a modal test on a bridge to see how exactly characteristic real-time dynamic responses are of structures. The test results proved that the WUMS was able to secure stable communication far up to nearly 800 m away by acquiring wireless responses in realtime accurately, when compared to the displacement and acceleration responses which were acquired through wired communication. The analysis of dynamic characteristics also showed that the wireless acceleration responses in real-time represented satisfactorily the dynamic properties of structures. Therefore, the WUMS is proved valid as a SHM, and its outstanding performance is also proven.

Smart reinforcement steel bars with low-cost MEMS sensors for the structural health monitoring of RC structures

Structural health monitoring (SHM) of civil structures and infrastructures is increasingly drawing the attention of stakeholders and of the research community because of the emerging need in the optimal maintenance and management of the existing assets. The monitoring of reinforced concrete (RC) structures through measuring local strains is a valuable procedure but only a limited number of alternative technologies are currently available, mainly including electric strain gages and fibre optic sensors. The difficulty of their installation, the high cost of their sensing and/or interrogating equipment and their uncertain long-term reliability push for alternative low-cost and distributed systems, easier to install and use. This paper presents a new concept of an instrumented reinforcement steel bar, achieving strain-sensing capabilities through the incorporation of an ordinary low-cost embedded Micro Electro-Mechanical Systems (MEMS) sensor. According to this concept, a sealed cavity is made in the bar, filled with a fluid and hosting the MEMS sensor; the temperature and pressure readings collected by the MEMS sensor are used to evaluate the cavity volume variations and are thus correlated with the bar axial deformation. The proposed smart system shows significant advantages over current strain-measurement devices, proving cheap, durable, easy to install, robust to mechanical shocks and chemical aggression. The theoretical framework of the new concept and the results of a preliminary mechanical test campaign are here presented and discussed.

Predicting the variability of natural frequencies and its causes by Second-Order Blind Identification

Structural aging, degradation phenomena, and damage due to hazardous events are common causes of failure in civil structures and infrastructures. The increasing need of extending the structure lifespan for sustainability and economic reasons motivated the rapid development of remote, fully automated structural health monitoring systems. Different approaches have been developed for damage detection based on the incoming data. Modal-based damage detection is probably one of the most popular procedures for structural health monitoring of civil structures, also thanks to the development of robust automated operational modal analysis algorithms in the last decade. However, the sensitivity of modal parameter estimates and the associated damage features to environmental and operational factors represents a significant drawback to the extensive application of this technology. Thus, effective damage detection cannot skip the preliminary compensation of the effect of those variables on modal properties. Different approaches to compensate the environmental influence on modal property estimates are reported in the literature. In this article, the use of Second-Order Blind Identification is proposed. It is applied to a number of case studies in order to validate its effectiveness in the presence of one or more environmental or operational variables. Results demonstrate that it can model the variability of natural frequency estimates in operational conditions and, above all, it can give a fundamental insight in determining the causes of such variability.

Multi-axis dynamic displacement measurement based on a strain shunt structure

Transient gap or crack width monitoring is essential for structural health monitoring and failure analysis of large civil structures. In this paper, an innovative multi-axis displacement sensor, which utilises metal foil strain gauges on a strain shunt structure, has been proposed. This displacement sensor has the advantages inherited from metal foil strain sensing, such as low cost, high precision, fast dynamic response and low power consumption, and can also measure displacement in two axes independently. The working principles and sensitivity are derived theoretically from key geometrical parameters of the shunt structure, and the linear response of strain values to the given displacement of two translational axes has been demonstrated experimentally. Furthermore, modal response, stress concentration, optimal gauges installation positions and bending deformation due to moment of the third axis are studied numerically.

Bayesian optimal estimation for output-only nonlinear system and damage identification of civil structures

This paper presents a new framework for output-only nonlinear system and damage identification of civil structures. This framework is based on nonlinear finite element (FE) model updating in the time-domain, using only the sparsely measured structural response to unmeasured or partially measured earthquake excitation. The proposed framework provides a computationally feasible approach for structural health monitoring and damage identification of civil structures when accurate measurement of the input seismic excitations is challenging (e.g., buildings with significant foundation rocking and bridges with piers in deep water) or the measured seismic excitations are erroneous and/or distorted by significant measurement error (e.g., malfunctioning sensors). Grounded on Bayesian inference, the proposed framework estimates the unknown FE model parameters and the ground acceleration time histories simultaneously, using the sparse measured dynamic response of the structure. Two approaches are presented in this study to solve the joint structural system parameter and input identification problem: (a) a sequential maximum likelihood estimation approach, which reduces to a sequential nonlinear constrained optimization method, and (b) a sequential maximum a posteriori estimation approach, which reduces to a sequential iterative extended Kalman filtering method. Both approaches require the computation of FE response sensitivities with respect to the unknown FE model parameters and the values of base acceleration at each time step. The FE response sensitivities are computed efficiently using the direct differentiation method. The two proposed approaches are validated using the seismic response of a 5-story reinforced concrete building structure, numerically simulated using a state-of-the-art mechanics-based nonlinear structural FE modeling technique. The simulated absolute acceleration response time histories of 3 floors and the relative (to the base) roof displacement response time histories of the building to a bidirectional horizontal seismic excitation are polluted with artificial measurement noise. The noisy responses of the structure are then used to estimate the unknown FE model parameters characterizing the nonlinear material constitutive laws of the concrete and reinforcing steel and the (assumed) unknown time history of the ground acceleration in the longitudinal direction of the building. The same nonlinear FE model of the structure is used to simulate the structural response and to estimate the dynamic input and system parameters. Thus, modeling uncertainty is not considered in this paper. Although the validation study demonstrates the estimation accuracy of both approaches, the sequential maximum a posteriori estimation approach is shown to be significantly more efficient computationally than the sequential maximum likelihood estimation approach.

Recent Advances in Intelligent-Based Structural Health Monitoring of Civil Structures

This survey paper deals with the structural health monitoring systems on the basis of methodologies involving intelligent techniques. The intelligent techniques are the most popular tools for damage identification in terms of high accuracy, reliable nature and the involvement of low cost. In this critical survey, a thorough analysis of various intelligent techniques is carried out considering the cases involved in civil structures. The importance and utilization of various intelligent tools to be mention as the concept of fuzzy logic, the technique of genetic algorithm, the methodology of neural network techniques, as well as the approaches of hybrid methods for the monitoring of the structural health of civil structures are illustrated in a sequential manner.

JCSHM: invaluable resource for practice of civil structural health monitoring

JCSHM: invaluable resource for practice of civil structural health monitoring

Damage identification using modal strains identified from operational fiber-optic Bragg grating data

Vibration-based structural health monitoring of civil structures relies on the repeated identification of dynamic structural characteristics of the structure from output-only vibration data. Natural frequencies and displacement mode shapes are the most commonly employed dynamic characteristics; yet their sensitivity to local damage of moderate severity is rather low with respect to their sensitivity to other factors such as temperature, necessitating data normalization. Strain mode shapes offer a higher sensitivity to local damage, but their accurate identification in a dense grid is challenging given the very small dynamic strain levels that are encountered under ambient excitation. In this article, a method is presented for tackling this challenge. It consists of three stages. First, fiber-optic Bragg grating strain sensors are attached to the structure and interrogated with a tunable laser performing a wavelength sweep. In this way, the measured strain amplitudes have the required accuracy but synchronization errors are introduced between the different Bragg sensors. Second, a modal analysis is performed on the dynamic strain data using an accurate parametric system identification technique. This is followed by a synchronization step which compensates for the delays introduced by the wavelength sweep. Finally, the synchronized strain mode shapes are employed as damage-sensitive features, either directly or via a newly proposed quantity, the top-to-bottom strain ratio. The method is validated by progressive damage testing of a complex, prestressed concrete “roof” beam, reinforced with steel fibers. It is observed that the proposed method can identify both the presence and the location of the damage in a relatively early stage.

Design and analysis of a novel SAW sensor for civil structural health monitoring

ABSTRACTThis paper presents a novel surface acoustic wave sensor for civil structural health monitoring. The sensing chip packaged in a shell is composed of two SAW delay line dice. One is utilized to sense the pressure induced by a gauge, and the other is used to sense the surrounding temperature for temperature compensation. Each SAW delay line die consists of an inter-digital transducer and two open grating reflectors. The SAW devices are analyzed by using coupling of modes model. Then, the devices in different parameters are fabricated on a 128°Y-X cut LiNbO3 substrate by lift-off technique. Finally, a network analyzer is utilized to test the devices. Large signal-to-noise ratio, low loss and clear sharp peaks of the reflection coefficients S11 in frequency and time domain can be observed. The experimental results matched well with the theoretical analysis by the finite element software COMSOL. The IDT finger pairs number, electrodes number of per reflector and acoustic aperture effect the reflection coefficient S11 is discussed in detail.

Structural Health Monitoring (SHM) of Civil Structures

As newer and more reliable ways of construction were developed, civilization began to spread out further and retain functional infrastructure for longer periods of time.[...]

A Study on the Data Compression Technology-Based Intelligent Data Acquisition (IDAQ) System for Structural Health Monitoring of Civil Structures.

In this paper, a data compression technology-based intelligent data acquisition (IDAQ) system was developed for structural health monitoring of civil structures, and its validity was tested using random signals (El-Centro seismic waveform). The IDAQ system was structured to include a high-performance CPU with large dynamic memory for multi-input and output in a radio frequency (RF) manner. In addition, the embedded software technology (EST) has been applied to it to implement diverse logics needed in the process of acquiring, processing and transmitting data. In order to utilize IDAQ system for the structural health monitoring of civil structures, this study developed an artificial filter bank by which structural dynamic responses (acceleration) were efficiently acquired, and also optimized it on the random El-Centro seismic waveform. All techniques developed in this study have been embedded to our system. The data compression technology-based IDAQ system was proven valid in acquiring valid signals in a compressed size.

Application assessments of concrete piezoelectric smart module in civil engineering

Traditional structural dynamic analysis and Structural Health Monitoring (SHM) of large scale concrete civil structures rely on manufactured embedding transducers to obtain structural dynamic properties. However, the embedding of manufactured transducers is very expensive and low efficiency for sign...

Distributed Humidity Sensing in PMMA Optical Fibers at 500 nm and 650 nm Wavelengths.

Distributed measurement of humidity is a sought-after capability for various fields of application, especially in the civil engineering and structural health monitoring sectors. This article presents a method for distributed humidity sensing along polymethyl methacrylate (PMMA) polymer optical fibers (POFs) by analyzing wavelength-dependent Rayleigh backscattering and attenuation characteristics at 500 nm and 650 nm wavelengths. Spatially resolved humidity sensing is obtained from backscatter traces of a dual-wavelength optical time domain reflectometer (OTDR). Backscatter dependence, attenuation dependence as well as the fiber length change are characterized as functions of relative humidity. Cross-sensitivity effects are discussed and quantified. The evaluation of the humidity-dependent backscatter effects at the two wavelength measurements allows for distributed and unambiguous measurement of relative humidity. The technique can be readily employed with low-cost standard polymer optical fibers and commercial OTDR devices.