Monitoring Of Civil Engineering Structures Research Articles

Multiple criteria-based sensor optimization for Structural Health Monitoring of civil engineering structures

Multiple criteria-based sensor optimization for Structural Health Monitoring of civil engineering structures

Linear time periodic system approximation based on Floquet and Fourier transformations for operational modal analysis and damage detection of wind turbine

Operational Modal Analysis (OMA) identifies modal properties of mechanical structures from vibration data collected from a few sensors under operation conditions. These methods are widely used to monitor civil engineering structures that are modeled as time-invariant systems. However, when dealing with wind turbines and rotating machines, many dedicated OMA techniques were developed to deal with the time-periodic behavior. Existing methods pre-process the data to adapt them to classical identification techniques. Yet, these methods present limitations as either requiring a high number of measured rotation periods, assuming the assumption of an isotropic rotor (i.e. undamaged), or the knowledge of the rotational speed. This work proposes to lift these difficulties by proving that the application of the classical system identification methods can produce meaningful estimates for anisotropy monitoring. This observation is based on directly approximating the time-periodic dynamic behavior of the wind turbine as a properly defined time-invariant system under periodic inputs. It results in the possibility of using classical identification methods without modification to retrieve the system matrices of the approximated time-invariant system. This approach does not require knowing the rotor speed, requiring relatively fewer measurements and it is not restricted to isotropic rotors. The identified modes can be used reliably for the monitoring of operating wind turbines and more especially for fault detection, at the expense of losing a part of the complete description of the periodic system. The resulting anisotropy monitoring approach and its capacities are illustrated in two cases. Firstly, on an academic model of a wind turbine and then on an aero-servo-elastic numerical model of a rotating 10 MW wind turbine to demonstrate the ability of the method to deal with realistic and representative data. It is shown for the latest case, from sensors located on the blades of the wind turbine, the approach is able to identify correctly the system properties (frequencies and mode shapes) and is also able to detect efficiently a fault on a blade.

On using the DFOS for damage identification in civil engineering structures

AbstractThe application of distributed fiber optic sensing (DFOS) technologies to the monitoring of civil engineering structures has been widely developed in recent years. In particular, fiber optic sensing (FOS) using the Rayleigh backscattering phenomenon of the laser frequency spectrum has been used to continuously measure strain along the fiber optic sensor with high spatial resolution. However, the practical implementation of fiber optic sensorsin the field is still a challenge. These include, but are not limited to, the bonding between fiber sensor cables and the surrounding medium, the practical way in which fiber optic sensors can be installed in concrete structures, and the analysis of crack width using the strain distribution along the fiber. Compared to classical discrete sensors, the DFOS has a great potential to detect, localize and quantify crack formation even in the early stages of microcracking. This paper discusses the potential and challenges of using distributed fiber optic sensors for crack detection in concrete structures. In this sense, different types of mechanical tests on concrete structures using different fiber optic sensors, and different bonding techniques are presented to explore the possibilities of crack detection in concrete structures and its potential for practical structural health monitoring (SHM) systems of civil structures.

Automated operational modal analysis for civil engineering structures with compressed measurements

Introducing compressive sensing (CS) to modal analysis enables modal identification approaches to process compressed measurements, which enhances the efficiency in both data transmission and analysis for long-term monitoring of civil engineering structures. Nevertheless, the existing methods normally require users’ judgment in mode selection, and thus they cannot realize automated modal analysis. To bridge this gap, this study proposes an automated CS-based operational modal analysis (OMA) method, namely Automated Sparse Decomposition (ASD). A mode selection strategy is proposed to gradually eliminate noise and false modes and thus realize automated modal identification based on compressed measurements. A search strategy with both extensive and intensive search stages is proposed to realize higher frequency search resolution with low computational costs. Additionally, an automated mode shape identification process is designed to enhance accuracy. To demonstrate the effectiveness of the proposed method, experimental data on an offshore wind turbine model and real-world monitoring data from an in-service cable-stayed bridge are analyzed. The results show that the identification accuracy of the proposed ASD is comparable to state-of-art CS-based methods. Due to its superior performance in both efficiency and accuracy, the proposed method has the potential to become a promising online modal analysis tool.

Integrated Sensor Fusion Device with an Optimized Mathematical Model to Monitor Civil Engineering Structures

Integrated Sensor Fusion Device with an Optimized Mathematical Model to Monitor Civil Engineering Structures

Study on Performance monitoring system of civil engineering structure

Civil engineering structure as a basic part of the urban construction development, directly affects the social residents quality of life, if not in view of the concrete problems to formulate the effective solution, then it will limit our social economy steady development. In the face of the increasing probability of civil engineering structure safety accidents, how to base on the performance monitoring system of civil engineering structure components has become the main issue discussed by domestic and foreign researchers. In this paper, based on the understanding of the status quo of civil engineering structural performance monitoring research, according to the basic structure of structural performance monitoring system, in-depth discussion of practical application of damage identification methods, in order to provide effective reference for civil engineering structure monitoring and problem identification in the new era.

Distributed Optical Fiber Sensors for Monitoring of Civil Engineering Structures.

Distributed Fiber Optics Sensing (DFOS) is a mature technology, with known, tested, verified, and even certified performance of various interrogators and measurement methods, which include Distributed Temperature Sensing (DTS), Distributed Temperature-Strain Sensing (DTSS), and Distributed Acoustic Sensing (DAS). This paper reviews recent progress in two critical areas of DFOS implementation in large scale civil engineering structures. First is the substantial improvement in sensing accuracy achieved by replacing Brillouin scattering-based measurements with its Rayleigh counterpart. The second is progress in acquisition speed and robustness, as now engineers can observe parameters of interest in real-time, and make informed, operational decisions regarding quality and safety. This received a high valuation from field engineers when used during the construction stage of the project. Furthermore, this change in the use of DFOS in civil engineering greatly increases the practical possibility of installing FO cables permanently. The same FO cables can be later used for long-term monitoring, during maintenance periods throughout the structure’s lifetime. To illustrate these two advances, we present a comparison between Brillouin and Rayleigh scattering measurements, and their accuracy, and highlight the importance of temperature and strain separation. We also present several important applications in large scale civil engineering infrastructure projects.

Water Level Retrieval Using a posteriori Residual of GNSS Pseudorange and Carrier-Phase Observations

In global navigation satellite system reflectometry (GNSS-R) applications, water level variations could be determined by analyzing one of three types of data: signal-to-noise ratios (SNRs), combinations of carrier-phase observations and combinations of pseudorange and carrier-phase observations. In this study, a new GNSS-R method based on the analysis of a posteriori residual of undifferenced and uncombined (UC) Precise Point Positioning (PPP) and ionosphere free (IF) PPP was proposed. Double peaks were found in the Lomb-Scargle periodogram (LSP) estimates of dual-frequency carrier-phase residuals generated with UC PPP, the pseudorange and carrier-phase residuals generated with IF PPP and the detrended S2 time series. To solve this problem, we present a strict quality control strategy to screen the correct retrievals. Two experimental datasets from a dam and a bridge-deformation monitoring system, respectively, were used to evaluate the performance of this new method in water-level retrieval. The results show that the water level retrievals from the pseudorange and carrier-phase residuals generated by the UC PPP and IF PPP positioning method show a good agreement with the in-situ references, with correlation coefficient exceeding 0.95 and 0.97 in the two experiments, which is comparable to the SNR method. The retrievals showed a root mean square error (RMSE) of 6-7cm in the reservoir experiment and 13-16cm in the Ganjiang river experiment. Meanwhile, the inter-frequency bias for about 0.2m was found in the Xilongchi reservoir water level retrieval experiment, however, it is not the case for the Ganjiang River water level retrieval experiment. The new method enables the simultaneous determination of displacements and water-level variation and is thus applicable for in-situ displacement monitoring of civil engineering structures, such as dam, bridge etc.

Investigation of proposed integrated control strategies based on performance and positioning of MR dampers on shaking table

Most of the studies in the field of structural control are focused on maximizing the performance of control devices without taking into consideration the complex dynamic response of the structure, computational efficiency, performance flexibility, and feedback reliability. Also, the location algorithm of energy dissipation should be concatenated with the control strategy, for effective structural control of multi-story structures. Moreover, in recent years, non-contact measurements using digital image correlation techniques have been adopted and implemented for the monitoring of civil engineering structures. However, its application is restricted to reinforced cement concrete members, involving a lower frequency spectrum, small displacements, and a lower image capture rate. In this study two response-based-adaptive control strategies based on inter-story drift and acceleration response reduction objectives respectively have been proposed. The control strategies are then integrated with the device location algorithm to establish the optimum configuration/location of magnetorheological dampers in addition to the design parameters of the controls system. The performance of proposed strategies is numerically compared with the benchmark Genetic algorithm using the Clipped optimal controller. The corresponding results indicated that proposed control strategies performed better for high-intensity ground motion and satisfactorily for low-intensity records. Next, the shake table results validated the performance of response-based-adaptive control strategies with device allocation algorithm in alleviating the peak and RMS response of the structure. The response of the structure was also attenuated and distributed among the modes of the structure, as indicated by the fast Fourier transform response of the structure.

Optimum wavelet selection for nonparametric analysis toward structural health monitoring for processing big data from sensor network: A comparative study

A critical problem encountered in structural health monitoring of civil engineering structures, and other structures such as mechanical or aircraft structures, is how to convincingly analyze the nonstationary data that is coming online, how to reduce the high-dimensional features, and how to extract informative features associated with damage to infer structural conditions. Wavelet transform among other techniques has proven to be an effective technique for processing and analyzing nonstationary data due to its unique characteristics. However, the biggest challenge frequently encountered in assuring the effectiveness of wavelet transform in analyzing massive nonstationary data from civil engineering structures, and in structural health diagnosis, is how to select the right wavelet. The question of which wavelet function is appropriate for processing and analyzing the nonstationary data in civil engineering structures has not been clearly addressed, and no clear guidelines or rules have been reported in the literature to show how the right wavelet is chosen. Therefore, this study aims to address an important question in this regard by proposing a new framework for choosing a proper wavelet that can be customized for massive nonstationary data analysis, disturbances separation, and extraction of informative features associated with damage. The proposed method takes into account data type, data and wavelet characteristics, similarity, sharing information, and data recovery accuracy. The novelty of this study lies in integrating multi-criteria which are associated directly with features that correlated well with change in structures due to damage, including common criteria such as energy, entropy, linear correlation index, and variance. Also, it introduces and considers new proposed measures, such as wavelet-based nonlinear correlation such as cosh spectral distance and mutual information, wavelet-based energy fluctuation, measures-based recovery accuracy, such as sensitive feature extraction, noise reduction, and others to evaluate various base wavelets’ function capabilities for appropriate decomposition and reconstruction of structural dynamic responses. The proposed method is verified by experimental and simulated data. The results revealed that the proposed method has a satisfactory performance for base wavelet selection and the small order of Daubechies and Symlet provide the best results, especially order 3. The idea behind our proposed framework can be applied to other structural applications.

Review of electromagnetic waves-based distance measurement technologies for remote monitoring of civil engineering structures

Measuring distance is essential for health monitoring and condition assessment of civil engineering structures. This paper reviews recent advances in remote sensing technologies for measuring distance based on electromagnetic waves. Specifically, four families of technologies are reviewed, which are the Global Navigation Satellite Systems, microwave radars, laser-based methods, and vision-based methods. The reviewed content covers the measurement principles, signal processing methods, state-of-the-art applications, and key performance metrics. The investigated performance includes the measurement accuracy, sampling frequency, operating distance, robustness to the environment, and compatibility with autonomous platforms. Existing inconsistent viewpoints concerning the performance are discussed. Based on the features of different technologies, a decision tree is presented to facilitate selection of appropriate methods for intended applications. This research is expected to promote development and applications of remote sensing technologies for facilitating condition assessment of engineering structures.

Analysis of the performance of closely spaced low-cost multi-GNSS receivers

The recent advances of low-cost GNSS receivers have broadened their application field not only in positioning and navigation, but also in deformation monitoring of civil engineering structures and geohazards. Even though some consumer-grade low-cost GNSS receivers can achieve cm-level accuracy, their lower performance compared to the dual-frequency high-end GNSS receivers restricts its systematic application of GNSS technology in monitoring projects. In this study, the noise level and performance of the low-cost GNSS receivers are assessed against geodetic receivers in terms of precision and availability when subjected to different measurements conditions, such as antenna grade, satellite constellation, and base station (antenna-receiver), based on zero- and short-baseline measurements. Furthermore, a new method is developed where a dual low-cost GNSS rover-system is formed by deploying two closely spaced low-cost GNSS receivers (30 cm apart), aiming to model their common error (multipath, satellite constellation, etc.) and reduce their noise level. The analysis of the zero- and short-baseline measurements reveals the potential improvement of the precision of the low-cost receiver by using multi-GNSS measurements and the importance of using a GNSS base station with geodetic antenna. However, development of a methodology which is based on adopting the sidereal filtering and the common mode error technique for the two closely spaced low-cost GNSS receivers may lead to precision of mm-level. The proposed methodology may broaden the application of low-cost GNSS receivers in monitoring networks and mainly for slowly developed deformations.

Performance analysis of low-cost GNSS stations for structural health monitoring of civil engineering structures

Global Navigation Satellite Systems (GNSS) have been used in various monitoring applications for the past two decades, as one of the very few options to provide absolute positions in a global reference frame. However, high performance GNSS stations are expensive, and sometimes may be impractical because of their size, power consumption or software requirements. Thus, the use of low-cost GNSS stations for structural health monitoring (SHM) has gained increasing attention. This paper presents a detailed experimental assessment of multiple combinations of GNSS receivers and antennas, and highlights an optimal cost-efficient solution for monitoring applications. Several sets of processing parameters and constraints are also evaluated using open source RTKLib software. The performance of the proposed solution is evaluated through two experimental dynamic scenarios, proving its ability to track quick displacements down to 4 mm and oscillations of 1 cm with a frequency up to 0.25 Hz with a 1 Hz receiver. Finally, a two-week dataset acquired from on a network of low-cost GNSS stations deployed on a suspended bridge is used to validate on-site performance. Results show good agreement between GNSS time series, traditional displacement sensors, and numerical simulations made using an operational mechanical model of the bridge, highlighting the potential of such low-cost solutions for structural health monitoring applications.

Structural health monitoring methods, dispersion of fibers, micro and macro structural properties, sensing, and mechanical properties of self‐sensing concrete—A review

AbstractSelf‐sensing composites are now becoming extremely attractive for the applications in civil engineering structures in order to enhance the safety and service life. In the new generation carbon nanofibers (CNFs) and nanotubes (CNTs) have shown good electrical, mechanical, and thermal parameters which inspired in the development of innovative nanocomposites with great and multifunctional properties. Among the numerous advances in civil engineering, the introduction of such carbon fibers to cement based materials produces cementitious materials as a smart or self‐sensing. Such smart composites reveal a noticeable change in their electrical conductivity with applied stress/strain and this special characteristic makes them helpful for monitoring of structures. However, there are still issues with CNFs and CNTs dispersion in case of cementitious composites, which impede the effective transfer of nanomaterials properties into the composites. Present paper presents a critical review on the dispersion of CNFs and CNTs within cementitious matrices, mechanical and electrical properties for the health monitoring of civil engineering structures.

Preface: Recent Research Advances on Structural Health Monitoring of Civil Engineering Structures

Australian Network of Structural Health Monitoring (ANSHM) is the leading professional association in the field of Structural Health Monitoring in Australia. Every year ANSHM organizes an annual workshop for members and practicing engineers to exchange their knowledge and practical developments in Structural Health Monitoring (SHM). The annual workshop series have been organized in around Australia either as a standalone workshop or in partnership with other scientific events including international conferences, and several special issues have been published based on these activities and recent research progresses. On 10–12 December 2018, ANSHM embraced its 10th annual workshop hosted by the University of Wollongong. Inspired by high-quality presentations and recent research advances on SHM techniques and applications at the workshop, a special issue was proposed for International Journal of Structural Stability and Dynamics. The 10th ANSHM workshop speakers were invited to submit the enhanced and extended versions of their papers to this special issue. After rigorous peer review and revision processes, 17 full papers and one technical note were accepted for inclusion in this special issue. ANSHM was established in 2009 and the preparation of this special issue also serves as one of the activities celebrating its 10th Anniversary.

Quasi-static macrostrain-based structural damage detection with distributed long-gauge fiber Bragg grating sensing

The distributed long-gauge fiber Bragg grating sensing technology has been studied and developed in recent years for structural health monitoring of civil engineering structures. Also, the corresponding damage identification method is one of the research hotspots and still needs to be enhanced. In this article, a novel damage detection method based on the distributed long-gauge fiber Bragg grating sensing technique is proposed to detect and localize damages. The method is based on the advanced complete ensemble empirical mode decomposition adaptive noise algorithm. Measured macrostrain responses from the long-gauge fiber Bragg grating sensors are decomposed into intrinsic mode functions, and the quasi-static macrostrains are extrapolated and extracted. A damage indicator is therefore proposed and built based on the quasi-static macrostrain time history. The effectiveness of the proposed damage detection approach was validated by numerical simulations of a cantilever beam. The robustness of the method was further verified by considering the noise pollution contained within the measured macrostrain. Experiments with a practical cantilever steel beam with different damage scenarios were also conducted and studied. Results proved that the proposed method could not only detect but also locate the damages accurately, and therefore has the promising potential for structural damage detection in civil engineering.

Deep residual network framework for structural health monitoring

Convolutional neural networks have been widely employed for structural health monitoring and damage identification. The convolutional neural network is currently considered as the state-of-the-art method for structural damage identification due to its capabilities of efficient and robust feature learning in a hierarchical manner. It is a tendency to develop a convolutional neural network with a deeper architecture to gain a better performance. However, when the depth of the network increases to a certain level, the performance will degrade due to the gradient vanishing issue. Residual neural networks can avoid the problem of vanishing gradients by utilizing skip connections, which allows the information flowing to the next layer through identity mappings. In this article, a deep residual network framework is proposed for structural health monitoring of civil engineering structures. This framework is composed of purely residual blocks which operate as feature extractors and a fully connected layer as a regressor. It learns the damage-related features from the vibration characteristics such as mode shapes and maps them into the damage index labels, for example, stiffness reductions of structures. To evaluate the efficacy and robustness of the proposed framework, an intensive evaluation is conducted with both numerical and experimental studies. The comparison between the proposed approach and the state-of-the-art models, including a sparse autoencoder neural network, a shallow convolutional neural network and a convolutional neural network with the same structure but without skip connections, is conducted. In the numerical studies, a 7-storey steel frame is investigated. Four scenarios with considering measurement noise and finite element modelling errors in the data sets are studied. The proposed framework consistently outperforms the state-of-the-art models in all the scenarios, especially for the most challenging scenario, which includes both measurement noise and uncertainties. Experimental studies on a prestressed concrete bridge in the laboratory are conducted. The proposed framework demonstrates consistent damage prediction results on this beam with the state-of-the-art models.

Testing Walking-Induced Vibration of Floors Using Smartphones Recordings

Smartphone technology is rapidly evolving, adding sensors of growing accuracy and precision. Structural engineers are among customers who indirectly benefit from such technological advances. This paper tests whether accelerometers installed in new generations of smartphones can reach the accuracy of professional accelerometers created for vibration monitoring of civil engineering structures, and how they can be useful. The paper describes an experimental study designed to measure walking-induced vibrations of a slender prefabricated prestressed concrete slab. Both traditional, high-accuracy, accelerometers and those integrated into commercial smartphones were used for experimental data collection. Direct comparison of the recordings yielded two key findings: the accuracy of smartphone accelerometers largely depends on the specific smartphone model, and nevertheless is satisfactory for preliminary modal testing at the very least. Furthermore, the smartphone measured accelerations of the lower back were used successfully to indirectly measure pedestrian walking loads.

Health Monitoring of Civil Infrastructures by Subspace System Identification Method: An Overview

Structural health monitoring (SHM) is the main contributor of the future’s smart city to deal with the need for safety, lower maintenance costs, and reliable condition assessment of structures. Among the algorithms used for SHM to identify the system parameters of structures, subspace system identification (SSI) is a reliable method in the time-domain that takes advantages of using extended observability matrices. Considerable numbers of studies have specifically concentrated on practical applications of SSI in recent years. To the best of author’s knowledge, no study has been undertaken to review and investigate the application of SSI in the monitoring of civil engineering structures. This paper aims to review studies that have used the SSI algorithm for the damage identification and modal analysis of structures. The fundamental focus is on data-driven and covariance-driven SSI algorithms. In this review, we consider the subspace algorithm to resolve the problem of a real-world application for SHM. With regard to performance, a comparison between SSI and other methods is provided in order to investigate its advantages and disadvantages. The applied methods of SHM in civil engineering structures are categorized into three classes, from simple one-dimensional (1D) to very complex structures, and the detectability of the SSI for different damage scenarios are reported. Finally, the available software incorporating SSI as their system identification technique are investigated.

Bridge condition monitoring using fixed moving principal component analysis

This paper proposes a data-driven damage detection method based on fixed moving principal component analysis (FMPCA) to analyze structural dynamic responses and monitor the bridge operational condition and the damage occurrence. The damage indices based on principal components (PCs) and eigenvalues can be calculated continuously by applying a fixed moving window. The length of the moving window is determined by using a new criterion based on the convergent spectrum of cumulative contribution ratio. Numerical simulations and experimental tests in the laboratory on beam bridge models subjected to stochastic loads are conducted to investigate the accuracy and effectiveness of the proposed approach. Both simulation and experimental results indicate that using the FMPCA can well analyze the dynamic vibration data to detect damage or abnormal vibration behavior during the operational condition. It can be used to accurately monitor the time instant of damage occurrence, which is very important in long-term monitoring of civil engineering structures. The proposed method is successfully applied to analyze the data recorded during an incident that a real large-scale suspension bridge was slightly scraped by the mast of a sand ship, which further verifies the effectiveness and feasibility of this method in engineering applications. The results also indicate that the bridge was not damaged after the incident but presented a short time abnormal vibration behavior owing to the impact of the ship mast.