Abstract: A method for automatically detecting the free vibration response segment of the high-speed railway bridges after trains passing. First, pre-select the test response sequence to be decomposed based on the maximum of the time instants corresponding to the absolute maximums of the response vectors at various measuring point. Then, Extract the single-frequency modal response from the test response by the iterative variational mode decomposition and fit the envelope amplitude of the modal response by Hilbert transform. Finally, the vibration features at each time instants are marked as decay vibration or non-decay vibration. The longest structural response segment that meets the decay vibration features is determined as the detected free vibration response segment for modal identification. This invention can effectively detect the free vibration data segment without human participation, which is of great significance for the real-time accurate modal analysis of high-speed railway bridges.

Abstract: The present invention belongs to the technical field of data analysis for structural testing, and relates to a method of the physical mode exaction for flexibility identification of engineering structures. In the present invention combined deterministic-stochastic subspace identification algorithm is first adopted to calculate basic modal parameters and modal scaling factors from state-space models of different orders. Subsequently, the relative scaling factor difference is added as a new modal indicator to the classic stabilization diagram to better clean out the stabilization diagram. And check the correctness of the selection of the stable axis using single-modal frequency-domain similarity index (SFSI) between single-order FRF and measured FRF. Then, further determine the physical modes from the modes in the stable axis using multi-modal frequency-domain similarity index (MFSI) between lower-order superposition FRF and measured FRF.

Abstract: Structural health monitoring providing sparse component analysis method for structural modal identification with incomplete number of sensors.

Abstract: The presented invention belongs to the technical field of data analysis for engineering structural monitoring, and relates to a method of complex modal identification for the structure with proportional damping. Firstly, the set of single source points containing the real modal shapes are obtained by short time Fourier transform and single source point detection, and then the real modal shapes are calculated by hierarchical distance method. Then, the impulse response and its Hilbert transform are obtained by natural excitation technology and Hilbert transform, respectively. The relationship between modal response and impulse response with its Hilbert transform is established. Finally, the complex modal parameters are solved. In this invention, the procedures of hidden complex modes of structures with proportional damping are given by explicit expression, which reveal the structural dynamic characteristics essentially.

Abstract: Structural health monitoring relating to an automatic method for tracking structural modal parameters. First, Natural Excitation Technique is used to transform the random responses into correlation functions and Eigensystem Realization Algorithm combined with the stabilization diagram is used to estimate modal parameters from various response segments. Then, modes from the latter response segment are classified as traceable modes or untraceable modes according to correlations between their observability vectors and subspaces of the existing reference modes. Final, traceable modes will be grouped into specified clusters with the same structural characteristics on the basis of maximum modal observability vector correlation and minimum frequency difference. Meanwhile, union of the untraceable modes and existing reference modes are updated as the new reference modes which can be applied into the next tracking process.

Abstract: The present invention belongs to the technical field of health monitoring for civil structures, and a dynamically non-Gaussian anomaly identification method is proposed for structural monitoring data. First, define past and current observation vectors for the monitoring data and pre-whiten them; second, establish a statistical correlation model for the whitened past and current observation vectors to obtain dynamically whitened data; then, divide the dynamically whitened data into two parts, i.e., the system-related and system-unrelated parts, which are further modelled by the independent component analysis; finally, define two statistics and determine their corresponding control limits, respectively, it can be decided that there is anomaly in the monitoring data when each of the statistics exceeds its corresponding control limit.

Abstract: The present invention belongs to the technical field of health monitoring for civil structures, and a performance alarming method for bridge expansion joints based on temperature displacement relationship model is proposed. First, the canonically correlated temperature is proposed to maximize the correlation between bridge temperature field and expansion joint displacement; second, a temperature displacement relationship model for bridge expansion joints is established based on canonically correlated temperatures; then, a mean-value control chart is constructed to the error of temperature displacement relationship model; finally, reasonable control limits are determined for the mean-value control chart. A more accurate temperature displacement relationship model can be established based on canonically correlated temperatures, which is of important value to improve the performance alarming ability for expansion joint.

Abstract: The present invention belongs to the technical field of health monitoring for civil structures, and an anomaly identification method considering spatial-temporal correlation is proposed for structural monitoring data. First, define current and past observation vectors for the monitoring data and pre-whiten them; second, establish a statistical correlation model for the pre-whitened current and past observation vectors to simultaneously consider the spatial-temporal correlation in the monitoring data; then, divide the model into two parts, i.e., the system-related and system-unrelated parts, and define two corresponding statistics; finally, determine the corresponding control limits of the statistics, and it can be decided that there is anomaly in the monitoring data when each of the statistics exceeds its corresponding control limit.

Abstract: Data analysis for structural health monitoring relating to a method of modal identification for structures with non-proportional damping based on extended sparse component analysis. Hilbert transform constructs analytical signal of acceleration response. Analytical signal is transformed into time-frequency domain using short-time-Fourier transform. The criterion is taken as the correlation coefficient of adjacent frequency points is close to 1. Points contributed by only one mode are detected from the time-frequency plane. Phases calculated at single-source-points are used to remove local outliers through local outlier factor method. Amplitudes of complex-valued mode shapes are estimated by Hierarchical clustering of amplitudes for time-frequency coefficients at single-source-points. Averaged phases of grouped single-source-points are estimated phases of complex-valued mode shapes. Finally, complex-valued mode shapes are acquired. Modal responses are estimated by sparse reconstruction method.

Abstract: A method for automatically detecting the free vibration response segment of the high-speed railway bridges after trains passing. First, pre-select the test response sequence to be decomposed based on the maximum of the time instants corresponding to the absolute maximums of the response vectors at various measuring point. Then, Extract the single-frequency modal response from the test response by the iterative variational mode decomposition and fit the envelope amplitude of the modal response by Hilbert transform. Finally, the vibration features at each time instants are marked as decay vibration or non-decay vibration. The longest structural response segment that meets the decay vibration features is determined as the detected free vibration response segment for modal identification. This invention can effectively detect the free vibration data segment without human participation, which is of great significance for the real-time accurate modal analysis of high-speed railway bridges.

Abstract: Sensor placement for structural health monitoring and sensor placement method for reducing uncertainty of structural modal identification. Influences of structural model error and measurement noise on measured responses are separated. Structural stiffness variation is used as model error, and Gaussian noise is used as measurement noise. Monte Carlo method simulates a large number of possible cases, and structural mode shape matrices under each model error condition are obtained. Conditional information entropy index quantifies and calculates uncertainty of identified modal parameter results. Conditional information entropy index solves the problem of uncertain Fisher information matrix, which cannot be solved by traditional information entropy method. Optimal sensor placement corresponds to maximum conditional information entropy index value.

Abstract: Data analysis for structural health monitoring relating to a method of modal identification for structures with non-proportional damping based on extended sparse component analysis. Hilbert transform constructs analytical signal of acceleration response. Analytical signal is transformed into time-frequency domain using short-time-Fourier transform. The criterion is taken as the correlation coefficient of adjacent frequency points is close to 1. Points contributed by only one mode are detected from the time-frequency plane. Phases calculated at single-source-points are used to remove local outliers through local outlier factor method. Amplitudes of complex-valued mode shapes are estimated by Hierarchical clustering of amplitudes for time-frequency coefficients at single-source-points. Averaged phases of grouped single-source-points are estimated phases of complex-valued mode shapes. Finally, complex-valued mode shapes are acquired. Modal responses are estimated by sparse reconstruction method.

Abstract: Structural health monitoring providing sparse component analysis method for structural modal identification with incomplete number of sensors.

Abstract: Structural health monitoring relating to an automatic method for estimating structural modal parameters by clustering. The structural modal parameters from state-space models are calculated in different orders by Natural Excitation Technique in combination with Eigensystem Realization Algorithm According to the characteristics that physical modes are those with high similarity and stably appearing at different orders while spurious modes are those with little similarity and unstably appearing at different orders, the modal dissimilarity between two nearest modes in consecutive order are considered as feature of the mode in the lower order. Then, features of modes are used in fuzzy C-means clustering to adaptively acquire the stable cluster where modes are with high similarity. Finally, Hierarchical clustering is used to group the stable modes with identical modal parameters together and thus each physical mode can be obtained.

Abstract: Structural health monitoring relating to a real-time tracking method for structural modal parameters. The Natural Excitation Technique transforms structural random responses into free decaying responses used to calculate structural modal parameters by the Eigensystem Realization Algorithm combined with the stabilization diagram. Considering influence of environmental excitation level on the number of identified modes, the reference mode list is formed by union of modes obtained from response sets in a day. Then the modes can be tracked automatically according to rules of minimum frequency difference and maximum Modal Assurance Criterion (MAC). To avoid mode mismatch problem caused by absence of threshold, frequency differences and MACs between all modes from the latter response set and all reference modes are calculated and the mode will be tracked into the cluster corresponding to the specified reference mode only in the case that their frequency difference is smallest and the MAC is largest.

Abstract: Sensor placement for structural health monitoring relating to modal estimation of bridge structures using structural data from strain gauges and accelerometers. Arrange strain gauges at large deformation positions of the structure for monitoring local deformation information. Adjust positions of strain gauges to include as much important displacement modal information as possible. Use strain mode shapes of strain gauge positions to estimate the displacement mode shapes of the structure and increase accelerometer to improve distinguishability of estimated displacement mode shapes, while reducing redundancy information among obtained displacement mode shapes. Different structural information contained in the strain gauges and the accelerometers are used, and placement of strain gauges can give local deformation information of key positions of the structure and obtain accurate structural displacement modal information.

Abstract: Structural health monitoring relating to an automatic method for tracking structural modal parameters. First, Natural Excitation Technique is used to transform the random responses into correlation functions and Eigensystem Realization Algorithm combined with the stabilization diagram is used to estimate modal parameters from various response segments. Then, modes from the latter response segment are classified as traceable modes or untraceable modes according to correlations between their observability vectors and subspaces of the existing reference modes. Final, traceable modes will be grouped into specified clusters with the same structural characteristics on the basis of maximum modal observability vector correlation and minimum frequency difference. Meanwhile, union of the untraceable modes and existing reference modes are updated as the new reference modes which can be applied into the next tracking process.

Abstract: Health monitoring for civil structures, and a performance alarming method for long-span bridge girder considering time-varying effects. First, establish accurate relationship model between temperature and strain fields to eliminate the temperature effect in the girder strain; second, build principal component analysis model for the girder strain after eliminating temperature effect to further eliminate the effects of wind and vehicle loads. Then, construct the performance alarming index and determine its reasonable threshold for the strain after eliminating the effects of temperature, wind and vehicle loads. Finally, construct the performance degradation locating index based on the contribution analysis.

Abstract: Data analysis for structural health monitoring, relating to a method of estimating the number of modes for sparse component analysis based structural modal identification. First, structural responses are transformed into time-frequency domain using short-time Fourier transform method. Single-source-point detection method is applied to the time-frequency coefficients to pick out the single-source-points where only one mode makes contribution. The single-source-point vectors are normalized to the upper half unit circle. Three statistics are given to analyze the statistical property. The suggested number of subintervals is given. Through counting, the approximate probabilities in subintervals are calculated and then smoothed through the weighted average procedure. The local maximum values of the averaged probability curve are detected and the number of active modes is equal to the number of local maximum values.

Abstract: A structural modal estimation based sensor placement method of strain gauges and accelerometers, including three steps: selection of initial accelerometer positions, selection of positions to be estimated and selection of strain gauge positions. First, use the modal confidence criterion and modal information redundancy to select the initial accelerometer position. Second, combined with the actual situation, when some positions cannot arrange the accelerometer, define the positions where the displacement modal estimation is needed. Third, use the strain mode shapes estimates the displacement mode shapes of the positions to be estimated, and uses the modal estimation effect to select the positions of the strain gauges. This can fully utilize the monitoring data collected by the strain gauges. The obtained sensor placement conforms to the modal confidence criterion and contains few modal redundancy information, which is an effective joint sensor placement method.