Structural System Identification Research Articles

Gradient Methods for Fixed Structure Controller Synthesis and System Identification of Decomposable Systems

In this letter we are considering the problem of fixed structure controller synthesis and system identification for large scale systems. The systems considered are referred to as decomposable systems and can be seen as resulting from the interconnection of a large number of identical subsystems. Combining recent results from nonsmooth controller synthesis with the special structure of decomposable systems, we show that the computational complexity of the controller synthesis and system identification procedure scale linearly with the size of the system. Employing results from robust control, the computational complexity of the controller synthesis can be reduced further, however, at the cost of additional conservatism. Using numerical examples we demonstrate that the proposed method can reduce the conservatism compared to existing LMI methods for full order decomposable controllers.

Constrained unscented Kalman filter for parameter identification of structural systems

The unscented Kalman filter (UKF) can be used to identify model parameters of structural systems from the measurement data. However, the standard UKF may provide unreliable and nonphysical estimates, since no parameter constraints are incorporated in the identification process. This paper discusses and compares several constrained UKF (CUKF) methods for parameter identification of structural systems. The effectiveness and robustness of the methods are evaluated through numerical simulation on a Bouc–Wen hysteretic system. The results demonstrate that with properly handling of the constraints, the identification accuracy can be improved. The proposed CUKF method is further validated using experimental data collected from a full-scale reinforced concrete structure. Based on the identified model parameters, the updated models can achieve more accurate simulation responses than the initial model.

Component model synthesis using model updating with neural networks

A component model synthesis (CMS) has been explored by carrying out model updating with neural networks. Structural system identification was achieved by applying the degree of freedom (DOF)-based reduction method and the inverse perturbation method. Experimental vibration data were restored to a full finite element model to update the numerical model. The experimental data were obtained using the specific sensor location selection method. The system identification was improved using the system equivalent reduction–expansion process by carrying out proper orthogonal decomposition. The proposed CMS was applied for a tank bracket model by simplifying the bolt assembly. Top and bottom parts of the tank bracket were each constructed to have a modally equivalent model. The convolutional neural network was adopted by training the density, Young’s modulus and contact properties to improve the computational efficiency.

Underexposed Vision-Based Sensors’ Image Enhancement for Feature Identification in Close-Range Photogrammetry and Structural Health Monitoring

This paper describes an alternative structural health monitoring (SHM) framework for low-light settings or dark environments using underexposed images from vision-based sensors based on the practical implementation of image enhancement algorithms. The proposed framework was validated by two experimental works monitored by two vision systems under ambient lights without assistance from additional lightings. The first experiment monitored six artificial templates attached to a sliding bar that was displaced by a standard one-inch steel block. The effect of image enhancement in the feature identification and bundle adjustment integrated into the close-range photogrammetry were evaluated. The second validation was from a seismic shake table test of a full-scale three-story building tested at E-Defense in Japan. Overall, this study demonstrated the efficiency and robustness of the proposed image enhancement framework in (i) modifying the original image characteristics so the feature identification algorithm is capable of accurately detecting, locating and registering the existing features on the object; (ii) integrating the identified features into the automatic bundle adjustment in the close-range photogrammetry process; and (iii) assessing the measurement of identified features in static and dynamic SHM, and in structural system identification, with high accuracy.

Robust Static Structural System Identification Using Rotations

Deflections are commonly measured in the static structural system identification of structures. Comparatively less attention has been paid to the possibility of measuring rotations for structural system identification purposes, despite the many advantages of using inclinometers, such as a high resolution and being reference free. Although some work using rotations can be found in the literature, this paper, for the very first time, proposes a statistical analysis that justifies the theoretical advantage of measuring rotations. The analytical expressions for the target parameters are obtained via static structural system identification using the constrained observability method first. Combined with the inverse distribution theory, the probability density function of the estimations of the target parameters can be obtained. Comparative studies on a simply supported bridge and a frame structure demonstrate the advantage of measuring rotations regarding the unbiasedness and the extent of variation in the estimations. To achieve robust parameter estimations, four strategies to use redundant rotations are proposed and compared. Numerical verifications on a bridge structure and a high-rise building have shown promising results.

Structural system identification based on combining weighted transmissibility and wavelet transform

In this paper, an integrated method called WTs-OMA (weighted transmissibility and wavelet transform-based operational modal analysis) is proposed to identify the structural dynamic characteristics using the earthquake response of structures. The proposed method, which is a time–frequency domain method with both parametric and non-parametric characteristics, utilizes the effective and efficient capabilities of weighted transmissibility and wavelet transform to achieve a proper performance in modal parameter estimation. This method employs weighted transmissibility functions to identify natural frequencies and mode shapes and calculates the damping values using the skeletons or the single frequency signals obtained from wavelet transform of the correlation functions of seismic responses. In this research, a numerical model under El Centro earthquake excitation is used for performance validation of the proposed method; Furthermore, the Karun 3 arch dam is selected as a case study in which the dynamic characteristics of the structure are identified based on the seismic responses from the different earthquakes.

Iterative reference-driven S-transform time-varying parameter identification for bridges under moving vehicle

Traditional bridge health monitoring is both expensive and time-consuming because of the need for installing a large quantity of sensors on the bridge. The indirect monitoring method as a feasible alternative has become popular over the last decade, as useful information can be gathered from moving vehicles installed with sensors by considering vehicle-bridge interaction. This paper describes an indirect time-varying modal parameter identification method for bridges under a moving test vehicle. A novel reference-driven S-transform is proposed for the parametric modal identification of time-varying structural systems with non-stationary characteristics based on a multisensory arrangement, comprising a fixed reference sensor installed on the bridge and another movable sensor installed on a moving test vehicle. By the phase-preservation property of S-transform and the multisensory time-frequency analysis, the proposed iterative reference-driven S-transform can be used to extract the time-varying modal parameters of the vehicle-bridge system when the vehicle moves on the bridge. Based on the coherence between measurements from the sensors mounted on the bridge and vehicle, one can track the time-varying characteristics by combining the spatial and time-frequency information obtained by the multisensory array. The efficiency of the proposed reference-driven S-transform method is validated by numerical simulations and laboratory experiments using a simply supported beam with a moving vehicle. The effects of the vehicle property, driving speed, road roughness and measurement noise are discussed. The numerical and experimental results have demonstrated that the proposed method is useful for time-varying parameter extraction with a multisensory system. This will be useful to structural condition monitoring of bridges.

Analytical observability method for the structural system identification of wide-flange box girder bridges with the effect of shear lag

In the field of bridge structural system identification (SSI), Bernoulli beam element models are often used to solve inverse problems. More rarely, Timoshenko beam element models can be calibrated. However, these models account for only the general shear effects, whereas other shear effects, such as shear lag, are not accounted for. However, as wide-flange box girder bridges become prevalent due to design demands, neglecting shear lag may introduce significant errors in predicting the mechanical parameters of the system and, hence, reduce the accuracy. In this paper, for the first time, the shear lag's effect on the vertical deflections is taken into account in an inverse problem. A parametric SSI, the observability method (OM), is updated to address the identification of thin-walled box girder bridges with wide flanges, including the shear lag effects. The structural analytical equations are used to connect the spatial shear effects with the beam-unidimensional-element model to be applied in the inverse analysis, which is referred to as the analytical observability method (AOM-SSI). This updated version of the OM is established and validated to identify the mechanical parameters of a structural test. The identified mechanical parameters show much better agreement with the expected results than those obtained with previous methods.

An enhanced indirect video-based measurement procedure for dynamic structural system identification applications

A video-based indirect sensing procedure for dynamic identification purposes is presented. To overcome major limitations of video-based methods in real on-site measurements, a novel three step pre-modification, magnification, post-modification process is developed. This process includes revision of the initial input video record in order to delete disturbing objects, utilizing a magnification method to filter the frequency content of the monitored motion and using a revision step for elimination of noises generated during magnification process. Finally, a set of digital signal and image processing analyses are performed on the modified video using virtual visual sensor technology. Based on the results of this research, motion signals of the monitored object are detected. The proposed approach has been used for identification of dynamic characteristics of two historic masonry minarets in Istanbul. Results shows that the proposed procedure is able to assess the dynamic characteristics of the monitored structure with a high-level of accuracy.

Identification and uncertainty quantification of structural flexibility for reliability analysis

In recent years, research on the effect of uncertainty on the results of structural system identification in order to effectively enhance structural safety has been rarely conducted. This paper studies identification and uncertainty quantification of structural flexibility from ambient vibration measurements to develop an efficient reliability analysis. The structural deflection with a confidence interval can be predicted under static loads via the uncertainty quantification on structural flexibility, and can then be used for the reliability analysis of the deflection serviceability limit state. First, the evaluation method for the uncertainty bounds of the modal parameters by a first-order perturbation analysis is reviewed. Then, the perturbation analysis method is extended to structural flexibility identification with considering the uncertainty in the identified modal parameters and the uncertainty in the reduced mass matrix. In addition, the structural deflection and its confidence interval under static load can be simultaneously derived with the identified structural flexibility. Eventually, by combining fuzzy criteria with the confidence intervals of the deflection prediction under the ultimate design load, the reliability index of the deflection serviceability limit state can be formulated. Both numerical and experimental examples were conducted to verify the efficiency of the proposed method. This study provides a scheme to directly link the structural identification with the structural safety evaluation.

A novel DNN tracking algorithm for structural system identification

In the field of structural health monitoring (SHM), cameras record videos and tracking methods can be applied to calculate the structural displacement. Commercial and unmanned aerial vehicle (UAV) cameras are promising non-contact sensors owning to their high availability and easy installation. However, effective tracking methods need to be developed. In this study, we firstly propose an end-to-end vision measuring framework with a novel deep neural network (DNN) tracker, named Siamese Single Decoder Network (SiamSDN). The system requires no target installation and uses cellphone cameras. For SiamSDN, the position and scale of bounding box are formulated through statistical parameter estimation. Unlike generative trackers, SiamSDN does not require manually extracted features or pre-defined motion areas. The tracking object is solely identified in the first frame. A shaking table test of a five-storey structure is carried out to demonstrate the efficiency. Besides, a UAV is used to simulate the field test. To minimize the error caused by the vibrations of UAV, digital video stabilization (DVS) is proposed to eliminate the drifts. Videos taken by both the commercial and UAV cameras are analyzed to calculate the displacements. Comparing our DNN tracker with feature point matching approach, SiamSDN improves the displacement measuring accuracy by 66.16% and 57.54%, respectively, and the frequency characteristics are obtained precisely.

Displacement measurement and nonlinear structural system identification: A vision‐based approach with camera motion correction using planar structures

Displacement measurement and nonlinear structural system identification: A vision‐based approach with camera motion correction using planar structures

Parameter identification of nonlinear structural systems through frequency response sensitivity analysis

Nonlinearity is ubiquitously encountered in structural systems, and it may have a great and complicated influence on the dynamic behaviours, including bifurcation, internal resonance, load history dependence, etc. Identifying the nonlinear system parameters is essential for analysis and design of the structure. To this end, a new approach is developed in this paper for nonlinear system parameter identification from frequency response sensitivity analysis. At first, the harmonic balance equation is established to govern the frequency response of the nonlinear system, upon which the frequency response and sensitivity analysis can be conducted. A remarkable feature is that the harmonic balance equation is algebraic so that the sensitivity analysis, pertaining to a linearized equation, is rather simple and straightforward. Then, parameter identification is modelled as a nonlinear least-squares problem, and the sensitivity approach is adopted in conjunction with the trust-region constraint for convergent solution. Numerical examples are conducted to demonstrate the feasibility and performance of the proposed approach.

Planning low-error SHM strategy by constrained observability method

Structural identification using dynamical parameters (such as the natural vibration frequencies and mode shapes) is an important issue, especially in bridges or high-rise buildings. However, incorrect decisions could happen on the Structural Health Monitoring (SHM) strategy and the Structural System Identification (SSI) analysis that makes the sometimes expensive and time-consuming process useless due to the large uncertainty of the resulting estimations. This paper discusses the role of the SHM strategy and the SSI analysis based on the constrained observability method (COM) and decision trees (DT) in reducing the estimation error. Here, the COM uses subsets of natural frequencies and/or modal-shapes to deal with the nonlinearity of the SSI derived from the operational aspects of the methods, and combines the unknown items including frequencies and mode shapes into an optimization process. Next, a decision-support tool based on decision trees is applied to help engineers to establish the best SHM + SSI strategy yielding the most accurate estimations. The principle and steps of this new method, the combination of constrained observability m,ethod and decision trees, are presented for the first time. After that, a numerical model of a bridge case is used to show how to choose the optimal strategy, when factors such as the structure layout, span length, measurement set, and parameters of the COM are included as decision variables. The importance ranking of these four factors is the layout, measurement set, parameters of the COM, and length through the sensitivity analysis of the COM estimated. Last, a real bridge is used to validate this methodology under the undamaged and damaged scenarios by comparing an Error Index, which shows the optimal SHM + SSI strategy works well no matter the bridge is damaged or not. The presented analysis leads to significant insights that can help the decision-making of the optimal SHM + SSI strategy, avoiding erroneous decisions if this tool is not used beforehand.

Role of Sensors in Error Propagation with the Dynamic Constrained Observability Method

The inverse problem of structural system identification is prone to ill-conditioning issues; thus, uniqueness and stability cannot be guaranteed. This issue tends to amplify the error propagation of both the epistemic and aleatory uncertainties, where aleatory uncertainty is related to the accuracy and the quality of sensors. The analysis of uncertainty quantification (UQ) is necessary to assess the effect of uncertainties on the estimated parameters. A literature review is conducted in this paper to check the state of existing approaches for efficient UQ in the parameter identification field. It is identified that the proposed dynamic constrained observability method (COM) can make up for some of the shortcomings of existing methods. After that, the COM is used to analyze a real bridge. The result is compared with the existing method, demonstrating its applicability and correct performance by a reinforced concrete beam. In addition, during the bridge system identification by COM, it is found that the best measurement set in terms of the range will depend on whether the epistemic uncertainty involved or not. It is concluded that, because the epistemic uncertainty will be removed as the knowledge of the structure increases, the optimum sensor placement should be achieved considering not only the accuracy of sensors, but also the unknown structural part.

Synergistic bridge modal analysis using frequency domain decomposition, observer Kalman filter identification, stochastic subspace identification, system realization using information matrix, and autoregressive exogenous model

This paper presents multiple system identification of large-scale bridge structures proposing the combined usage of different modal parameter findings, namely from Frequency Domain Decomposition, Observer Kalman Filter Identification/Eigensystem Realization Algorithm, Combined Deterministic Stochastic Subspace Identification, System Realization Using Information Matrix, and Autoregressive Exogenous Model. A method-centric democratic ranking approach visualizes and quantifies the harmony among different system identification methods in terms of modal parameters, then ranks them based on the correlation among each other, and consequently complies with the highest rank modal parameter outputs. The synergistic scheme is applied on a numerical beam and two bridge structures including one healthy and another subjected to progressive damage. Looking at the top-rank selections, one can see that outlier identification results from a population of modal parameters can intuitively become extinct. The collaboration among methods is dependent on the chosen methods; therefore, method selection relies on care and fair representation of the identification features. Lack of agreement between methods can indicate low confidence in the outranking method and is quantified by median absolute deviation. Nevertheless, the majority of the algorithm population agrees on specific results, which are valuable to produce state knowledge despite low signal to noise ratio, especially without the presence of a reference. Thus, the collaborative usage of multiple methods in a systematic and ranking-based manner reduces significant error and outlier possibilities in modal identification due to algorithm-related issues, which is the novel contribution of this study.

Efficient Model Updating Method for System Identification Using a Convolutional Neural Network

Model updating processes are important for improving a model’s accuracy by considering experimental data. Structural system identification was achieved here by applying the degree-of-freedom-based reduction method and the inverse perturbation method. Experimental data were obtained using the specific sensor location selection method. Experimental vibration data were restored to a full finite element model using the reduction method to compare and update the numerical model. Applied iteratively, the improved reduced system method boosts model accuracy during full model restoration; however, iterative processes are time consuming. The computation efficiency was improved using the system equivalent reduction–expansion process in concert with proper orthogonal decomposition. A convolutional neural network was trained and applied to the updating process. We propose the use of an efficient model updating method using a convolutional neural network to reduce computation time. Experimental and numerical examples were adopted to examine the efficiency and accuracy of the model updating method using a convolutional neural network.

Structural Health Monitoring of 2D Plane Structures

This paper presents the application of the observability technique for the structural system identification of 2D models. Unlike previous applications of this method, unknown variables appear both in the numerator and the denominator of the stiffness matrix system, making the problem non-linear and impossible to solve. To fill this gap, new changes in variables are proposed to linearize the system of equations. In addition, to illustrate the application of the proposed procedure into the observability method, a detailed mathematical analysis is presented. Finally, to validate the applicability of the method, the mechanical properties of a state-of-the-art plate are numerically determined.

Structural system identification under seismic excitation via Markov chain Monte Carlo method

Structural system identification under seismic excitation via Markov chain Monte Carlo method

Machine learning in structural engineering

This article presents a review of selected articles about structural engineering applications of machine learning (ML) in the past few years. It is divided into the following areas: structural system identification, structural health monitoring, structural vibration control, structural design, and prediction applications. Deepneural networkalgorithms have beenthe subject of a large number of articles in civil and structural engineering.There are, however, otherML algorithms with great potential in civil and structural engineering that are worth exploring. Four novel supervised ML algorithms developed recently by the senior author and his associates with potential applications in civil/structural engineering are reviewed in this paper. They are the Enhanced Probabilistic Neural Network (EPNN), the Neural Dynamic Classification (NDC) algorithm, the Finite Element Machine (FEMa), and the Dynamic Ensemble Learning (DEL) algorithm