Jian-Huang Weng

Experimental Study of Damage Detection by Data-Driven Subspace Identification and Finite-Element Model Updating

The need to identify the physical properties of a structure given its force-response (input-output) relationship is driven primarily by the need to validate the approximate solution models, such as finite-element models. This paper proposes a method, which combines the structural system identification and model updating techniques, for the damage detection of a steel frame structure and a RC frame. The damage detection procedure consists of two steps: (1) identifying the system dynamic characteristics using the subspace identification (SI) technique from input/output measurements and (2) developing a damage assessment method for structural members (including joints) based on a progressive finite-element model updating and a large-scale optimization using a nonlinear least-square technique. The proposed method was verified through a shaking table experimental study using: (1) a 1/4-scale six-story steel frame structure by loosening the connection bolts for damage simulations and (2) a two-story RC frame subject to different levels of ground excitations back to back. As demonstrated by experimental results, the proposed damage detection method, based on the combination of SI technique and the model updating approach, is very effective for the damage assessment of frame structures. The method not only can detect the damage locations but also can quantify the damage severities.

Structural damage diagnosis based on on-line recursive stochastic subspace identification

This paper presents a recursive stochastic subspace identification (RSSI) technique for on-line and almost real-time structural damage diagnosis using output-only measurements. Through RSSI the time-varying natural frequencies of a system can be identified. To reduce the computation time in conducting LQ decomposition in RSSI, the Givens rotation as well as the matrix operation appending a new data set are derived. The relationship between the size of the Hankel matrix and the data length in each shifting moving window is examined so as to extract the time-varying features of the system without loss of generality and to establish on-line and almost real-time system identification. The result from the RSSI technique can also be applied to structural damage diagnosis. Off-line data-driven stochastic subspace identification was used first to establish the system matrix from the measurements of an undamaged (reference) case. Then the RSSI technique incorporating a Kalman estimator is used to extract the dynamic characteristics of the system through continuous monitoring data. The predicted residual error is defined as a damage feature and through the outlier statistics provides an indicator of damage. Verification of the proposed identification algorithm by using the bridge scouring test data and white noise response data of a reinforced concrete frame structure is conducted.

Turning the building into a smart structure: integrating health monitoring

The objective of this paper is to develop a novel sensing system which can conduct continuous monitoring of a building structure and generate a monitoring report. The building monitoring data will focus on the ambient vibration responses. Two servers are used in this SHM system: 1) wireless measurement server which takes care of measuring and archiving all the structural responses and environmental situation, and 2) analysis server which conducts the signal processing on the received signals. The measurement server is in charge of the collection of signals and broadcast wirelessly from all sensors to the analysis server. Dominant frequencies and mode shapes of the building will be estimated in the analysis server from the continuous monitoring of the ambient vibration data (velocity) of the building by using AR-Model, Frequency Domain Decomposition and Stochastic Subspace Identification methods. The proposed continuous monitoring system can effectively identify the building current health condition and generate a report to the owner.

Feature Extraction within the Fei-Tsui Arch Dam under Environmental Variations

The objective of this research is to develop methods for analyzing the seismic response data and the long-term static data of the Fei-tsui arch dam, and based on the result of analysis to set an early warning threshold level for dam safety early warning evaluation. First, the input/output subspace identification technique is used to analysis the recorded seismic data from 84 earthquake events in order to identify the modal properties of the dam under different water level. Considering the spatial variability of input excitation, two kinds of system model are applied to subspace identification technique: the single-input and the multiple-input system. The regression curves between the identified system natural frequencies and water level are developed from the statistical analysis of identification results. Second, two different approaches are applied to extract features of the long-term data of the dam. The methods include the singular spectrum analysis with AR model (SSA-AR) and the nonlinear principal component analysis (NPCA) using auto-associate neural network method (AANN). By using these methods, the residual deformation between the estimated and the recorded data was generated, through statistical analysis, the threshold level of the dam static deformation can be determined. Discussion on (1) the difference between two kinds of input model for subspace identification and (2) proposed methods to extract static data are also made in this research.

Application of higher order SVD to vibration-based system identification and damage detection

Singular value decomposition (SVD) is a powerful linear algebra tool. It is widely used in many different signal processing methods, such principal component analysis (PCA), singular spectrum analysis (SSA), frequency domain decomposition (FDD), subspace identification and stochastic subspace identification method ( SI and SSI ). In each case, the data is arranged appropriately in matrix form and SVD is used to extract the feature of the data set. In this study three different algorithms on signal processing and system identification are proposed: SSA, SSI-COV and SSI-DATA. Based on the extracted subspace and null-space from SVD of data matrix, damage detection algorithms can be developed. The proposed algorithm is used to process the shaking table test data of the 6-story steel frame. Features contained in the vibration data are extracted by the proposed method. Damage detection can then be investigated from the test data of the frame structure through subspace-based and nullspace-based damage indices.

Online Tracking of Structural Parameters Based on Recursive Subspace Identification

An important objective of structural health monitoring (SHM) for civil infrastructure is to identify the state of the structure and to detect these changes when it occurred. The changes of features in a structural system may due to the nonlinear inelastic response of structure or due to the structural damage subjected to severe external loading. Therefore, damage detection in large structural system, such as buildings and bridges, can improve safety and reduce maintenance costs and the design of damage detection system is one of the goals of SHM. The objective of this paper is to develop an on-line and almost real-time system parameter estimation and damage detection technique from the response measurements through using the Recursive Subspace identification (RSI) or Recursive Stochastic Subspace Identification (RSSI) approaches. Verification of the proposed method by using data from both numerical simulation and the shaking table test of a steel structure with abrupt change of inter-story stiffness are discussed. With the implementation of forgetting factor in RSSI/RSI methods the ability of on-line damage detection of structural system can be enhanced.Copyright

Finite element-based damage identification of RC frames

The objective of this study is the application of finite-element based damage detection techniques to identify the damage severity of reinforced concrete structures subject to different intensity level of earthquake excitation. The damage detection procedure consists of two parts: (1) identify the system dynamic characteristics using the system identification technique (subspace identification, SI) from direct input/output measurements, (2) develop damage assessment of structural members (joints) based on the progressive finite element model and large-scale optimization with nonlinear least-square technique. To verify the proposed method, first, four specimens of two-bay one-story reinforced concrete frame and one two-story reinforced frame structure, with the same design detail and same construction material properties, are tested on the shaking table with different intensity level of earthquake excitations. Integrate the Subspace Identification method and the Finite-element based model updating, damage identification of the structures are investigated. The proposed finite-element based damage identification method were found to be a very effective method for damage assessment of frame structure.