• Structural Engineering and Mechanics
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• v.54 no.2
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• pp.291-304
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• 2015

Compared with the identification of linear structures, it is more challenging to conduct identification of nonlinear structure systems, especially when the locations of structural nonlinearities are not clear in structural systems. Moreover, it is highly desirable to develop methods of parametric identification using partial measurements of structural responses for practical application. To cope with these issues, an identification method is proposed in this paper for the detection and parametric identification of structural nonlinear restoring forces using only partial measurements of structural responses. First, an equivalent linear structural system is proposed for a nonlinear structure and the locations of structural nonlinearities are detected. Then, the parameters of structural nonlinear restoring forces at the locations of identified structural nonlinearities together with the linear part structural parameters are identified by the extended Kalman filter. The proposed method simplifies the identification of nonlinear structures. Numerical examples of the identification of two nonlinear multi-story shear frames and a planar nonlinear truss with different nonlinear models and locations are used to validate the proposed method.

• Smart Structures and Systems
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• v.15 no.1
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• pp.57-80
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• 2015

Extended Kalman Filter (EKF) has been widely used for structural identification and damage detection. However, conventional EKF approaches require that external excitations are measured. Also, in the conventional EKF, unknown structural parameters are included as an augmented vector in forming the extended state vector. Hence the sizes of extended state vector and state equation are quite large, which suffers from not only large computational effort but also convergence problem for the identification of a large number of unknown parameters. Moreover, such approaches are not suitable for intelligent structural damage detection due to the limited computational power and storage capacities of smart sensors. In this paper, a two-stage and two-step algorithm is proposed for the identification of structural damage as well as unknown external excitations. In stage-one, structural state vector and unknown structural parameters are recursively estimated in a two-step Kalman estimator approach. Then, the unknown external excitations are estimated sequentially by least-squares estimation in stage-two. Therefore, the number of unknown variables to be estimated in each step is reduced and the identification of structural system and unknown excitation are conducted sequentially, which simplify the identification problem and reduces computational efforts significantly. Both numerical simulation examples and lab experimental tests are used to validate the proposed algorithm for the identification of structural damage as well as unknown excitations for structural health monitoring.

• Structural Engineering and Mechanics
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• v.46 no.3
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• pp.433-445
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• 2013

Vibration-based structural identification has become an important tool for structural health monitoring and safety evaluation. However, various kinds of uncertainties (e.g., observation noise) involved in the field test data obstruct automation system identification for accurate and fast structural safety evaluation. A practical way including a data preprocessing procedure and a vector backward auto-regressive (VBAR) method has been investigated for practical bridge identification. The data preprocessing procedure serves to improve the data quality, which consists of multi-level uncertainty mitigation techniques. The VBAR method provides a determinative way to automatically distinguish structural modes from extraneous modes arising from uncertainty. Ambient test data of a cantilever beam is investigated to demonstrate how the proposed method automatically interprets vibration data for structural modal estimation. Especially, structural identification of a truss bridge using field test data is also performed to study the effectiveness of the proposed method for real bridge identification.

• Structural Engineering and Mechanics
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• v.31 no.3
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• pp.333-347
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• 2009

Due to structural complicacy, structural health monitoring for civil engineering needs more accurate and effectual methods of damage identification. This study aims to import multi-source information fusion (MSIF) into structural damage diagnosis to improve the validity of damage detection. Firstly, the essential theory and applied mathematic methods of MSIF are introduced. And then, the structural damage identification method based on multi-mode information fusion is put forward. Later, on the basis of a numerical simulation of a concrete continuous box beam bridge, it is obviously indicated that the improved modal strain energy method based on multi-mode information fusion has nicer sensitivity to structural initial damage and favorable robusticity to noise. Compared with the classical modal strain energy method, this damage identification method needs much less modal information to detect structural initial damage. When the noise intensity is less than or equal to 10%, this method can identify structural initial damage well and truly. In a word, this structural damage identification method based on multi-mode information fusion has better effects of structural damage identification and good practicability to actual structures.

• Structural Engineering and Mechanics
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• v.63 no.6
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• pp.789-797
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• 2017

Recently, some integrated structural identification/damage detection and reliability evaluation of structures with uncertainties have been proposed. However, these techniques are applicable for off-line synthesis of structural identification and reliability evaluation. In this paper, based on the recursive formulation of the extended Kalman filter, an on-line integration of structural identification/damage detection and reliability evaluation of stochastic building structures is investigated. Structural limit state is expanded by the Taylor series in terms of uncertain variables to obtain the probability density function (PDF). Both structural component reliability with only one limit state function and system reliability with multi-limit state functions are studied. Then, it is extended to adopt the recent extended Kalman filter with unknown input (EKF-UI) proposed by the authors for on-line integration of structural identification/damage detection and structural reliability evaluation of stochastic building structures subject to unknown excitations. Numerical examples are used to demonstrate the proposed method. The evaluated results of structural component reliability and structural system reliability are compared with those by the Monte Carlo simulation to validate the performances of the proposed method.

• Structural Engineering and Mechanics
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• v.11 no.3
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• pp.237-258
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• 2001

Structural identification via modal analysis in structural mechanics is gaining popularity in recent years, despite conceptual difficulties connected with its use. This paper is devoted to illustrate both the capabilities and the indeterminacy characterizing structural identification problems even in quite simple instances, as well as the cautions that should be accordingly adopted. In particular, we discuss an application of an identification technique of variational type, based on the measurement of eigenfrequencies and mode shapes, to a steel frame with friction joints under various assembling conditions. Experience has suggested, so as to restrict the indeterminacy frequently affecting identification issues, having resort to all the a priori acknowledged information on the system, to the symmetry and presence of structural elements with equal stiffness, to mention one example, and mindfully selecting the parameters to be identified. In addition, considering that the identification techniques have a local character and correspond to the updating of a preliminary model of the structure, it is important that the analytical model on the first attempt should be adequately accurate. Secondly, it has proved determinant to cross the results of the dynamic identification with tests of other typology, for instance, static tests, so as to fully understand the structural behavior and avoid the indeterminacy due to the nonuniqueness of the inverse problem.

• Smart Structures and Systems
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• v.30 no.3
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• pp.273-286
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• 2022

In recent years, vision-based monitoring has received great attention. However, structural identification using vision-based displacement measurements is far less established. Especially, simultaneous identification of structural systems and unknown excitation using vision-based displacement measurements is still a challenging task since the unknown excitations do not appear directly in the observation equations. Moreover, measurement accuracy deteriorates over a wider field of view by vision-based monitoring, so, only a portion of the structure is measured instead of targeting a whole structure when using monocular vision. In this paper, the identification of structural system and excitations using vision-based displacement measurements is investigated. It is based on substructure identification approach to treat of problem of limited field of view of vision-based monitoring. For the identification of a target substructure, substructure interaction forces are treated as unknown inputs. A smoothing extended Kalman filter with unknown inputs without direct feedthrough is proposed for the simultaneous identification of substructure and unknown inputs using vision-based displacement measurements. The smoothing makes the identification robust to measurement noises. The proposed algorithm is first validated by the identification of a three-span continuous beam bridge under an impact load. Then, it is investigated by the more difficult identification of a frame and unknown wind excitation. Both examples validate the good performances of the proposed method.

• Proceedings of the KSR Conference
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• 2004.10a
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• pp.121-126
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• 2004

In this paper, a nonlinear structural damage identification algorithm is derived by taking into account the structurally damped spectral element model thinking over a real situation. The structural damage identification analyses are conducted by using the Newton-Raphson method. It is found that, in general Structural Damage Identification by using the Structurally Damped Spectral Element Model provides the same exact damage identification results when compared with the results obtained by the structurally undamped spectral model.

• Smart Structures and Systems
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• v.15 no.1
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• pp.209-225
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• 2015

Compared to ambient vibration testing, impact testing has the merit to extract not only structural modal parameters but also structural flexibility. Therefore, structural deflections under any static load can be predicted from the identified results of the impact test data. In this article, a signal processing procedure for structural flexibility identification is first presented. Especially, practical issues in applying the proposed procedure for structural flexibility identification are investigated, which include sensitivity analyses of three pre-defined parameters required in the data pre-processing stage to investigate how they affect the accuracy of the identified structural flexibility. Finally, multiple-reference impact test data of a three-span reinforced concrete T-beam bridge are simulated by the FE analysis, and they are used as a benchmark structure to investigate the practical issues in the proposed signal processing procedure for structural flexibility identification.

• Smart Structures and Systems
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• v.11 no.1
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• pp.123-134
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• 2013

Identifying structural modal parameters, especially those modes within high frequency range, from ambient data is still a challenging problem due to various kinds of uncertainty involved in vibration measurements. A procedure applying an ensemble empirical mode decomposition (EEMD) method is proposed for accurate and robust structural modal identification. In the proposed method, the EEMD process is first implemented to decompose the original ambient data to a set of intrinsic mode functions (IMFs), which are zero-mean time series with energy in narrow frequency bands. Subsequently, a Sub-PolyMAX method is performed in narrow frequency bands by using IMFs as primary data for structural modal identification. The merit of the proposed method is that it performs structural identification in narrow frequency bands (take IMFs as primary data), unlike the traditional method in the whole frequency space (take original measurements as primary data), thus it produces more accurate identification results. A numerical example and a multiple-span continuous steel bridge have been investigated to verify the effectiveness of the proposed method.