Real-time Structural Damage Research Articles

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

This paper presents a recursive stochastic subspace identification (RSSI) technique foron-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. Toreduce the computation time in conducting LQ decomposition in RSSI, the Givensrotation as well as the matrix operation appending a new data set are derived. Therelationship between the size of the Hankel matrix and the data length in each shiftingmoving window is examined so as to extract the time-varying features of the systemwithout loss of generality and to establish on-line and almost real-time systemidentification. The result from the RSSI technique can also be applied to structuraldamage diagnosis. Off-line data-driven stochastic subspace identification was usedfirst to establish the system matrix from the measurements of an undamaged(reference) case. Then the RSSI technique incorporating a Kalman estimator isused to extract the dynamic characteristics of the system through continuousmonitoring data. The predicted residual error is defined as a damage feature andthrough the outlier statistics provides an indicator of damage. Verification ofthe proposed identification algorithm by using the bridge scouring test data andwhite noise response data of a reinforced concrete frame structure is conducted.

Agent-based artificial immune system approach for adaptive damage detection in monitoring networks

This paper presents an agent-based artificial immune system approach for adaptive damage detection in distributed monitoring networks. The presented approach establishes a new monitoring paradigm by embodying desirable immune attributes, such as adaptation, immune pattern recognition, and self-organization, into monitoring networks. In the artificial immune system-based paradigm, a group of autonomous mobile monitoring agents mimic immune cells (such as B-cells) in the natural immune system, interact locally with monitoring environment, and respond to emerging problems through simulated immune responses. The presented immune-inspired monitoring paradigm has been applied to structural health monitoring. The “antibody” of a mobile monitoring agent is a pattern recognition algorithm tuned to a certain type of structural damage pattern. The mobile monitoring agent performs damage diagnosis based on structural dynamic response data. Mobile monitoring agents communicate with each other and collaborate with network components based on agent interaction protocols defined in agent standards, the Foundation for Intelligent Physical Agents standards. A mobile agent system embedded in sensor nodes supports the selective generation, migration, communication, and management of mobile monitoring agents automatically. The active structural health monitoring is achieved by distributing mobile monitoring agents to the sites where they are needed. The structural damage diagnosis using mobile monitoring agents and artificial immune pattern recognition method has been tested using a scaled steel bridge structure. The test result shows the feasibility of using this approach for real-time structural damage diagnosis.

Real-time structural damage detection using wireless sensing and monitoring system

A wireless sensing system is designed for application to structural monitoring and damage detection applications. Embedded in the wireless monitoring module is a two-tier prediction model, the auto-regressive (AR) and the autoregressive model with exogenous inputs (ARX), used to obtain damage sensitive features of a structure. To validate the performance of the proposed wireless monitoring and damage detection system, two near full scale single-story RC-frames, with and without brick wall system, are instrumented with the wireless monitoring system for real time damage detection during shaking table tests. White noise and seismic ground motion records are applied to the base of the structure using a shaking table. Pattern classification methods are then adopted to classify the structure as damaged or undamaged using time series coefficients as entities of a damage-sensitive feature vector. The demonstration of the damage detection methodology is shown to be capable of identifying damage using a wireless structural monitoring system. The accuracy and sensitivity of the MEMS-based wireless sensors employed are also verified through comparison to data recorded using a traditional wired monitoring system.

Structural health monitoring using piezoelectric impedance measurements

This paper presents an overview and recent advances in impedance-based structural health monitoring. The basic principle behind this technique is to apply high-frequency structural excitations (typically greater than 30kHz) through surface-bonded piezoelectric transducers, and measure the impedance of structures by monitoring the current and voltage applied to the piezoelectric transducers. Changes in impedance indicate changes in the structure, which in turn can indicate that damage has occurred. An experimental study is presented to demonstrate how this technique can be used to detect structural damage in real time. Signal processing methods that address damage classifications and data compression issues associated with the use of the impedance methods are also summarized. Finally, a modified frequency-domain autoregressive model with exogenous inputs (ARX) is described. The frequency-domain ARX model, constructed by measured impedance data, is used to diagnose structural damage with levels of statistical confidence.

Real-Time Structural Damage Monitoring by Input Error Function

A newly developed structural damage monitoring technique is presented. The study focuses on capturing the initiation of multiple damages as they occur in a structure, which is similar to the concept of the fault detection filter. Previously, it has been shown that modified interaction matrix formulation provides a series of input error functions that generate a nonzero residual signal when the system experiences erroneous inputs. Error functions for each individual structural member are developed from the analogy between actuator failure and damageinduced residual force. When each individual error function is monitored, multiple damages as they occur in a structure can be simultaneously detected and isolated. Because the technique does not require frequency-domain measurements, it is readily applicable to online monitoring systems. This real-time technique also accommodates nonlinear breathing cracks and works for any type of excitation. A numerical simulation using a spring‐mass system and truss structure successfully demonstrates the proposed method.

Triboluminescent materials for structural damage monitoring

Triboluminescent materials have been known for at least four centuries. The majority of work to date has been academic in nature – reporting a new triboluminescent material and/or presenting a spectroscopic study in an effort to explain the mechanism underlying the fracture-induced light emission. Recently, the advantages of triboluminescent materials as real-time structural damage sensors have been highlighted. These sensors can be exploited in both commercial and military markets. In addition to covering some of the recent advances in the field, this paper aims to provide a timely overview of those triboluminescent materials which may be suitable as structural damage sensors.

Damage Detection and Damage Detectability— Analysis and Experiments

A technique to identify structural damage in real time using limited instrumentation is presented. Contrast maximization is used to find the excitation forces that create maximum differences in the response of the damaged structure and the analytical response of the undamaged structure. The optimal excitations for the damaged structure are then matched against a database of optimal excitations to locate the damage. To increase the reliability of the approach under modeling and measurement errors, the contrast maximization approach is combined with an approach based on changes in frequency signature. The detectability of any particular damage with the proposed technique depends on the ratio of the magnitude of damage and the magnitude of errors in the measurements, as well as on how much the damage influences the measurements. A damage detectability prediction measure, that incorporates these effects, is developed. The technique is first tested numerically on a 36 degree-of-freedom space truss. To simulate experimental conditions, an extensive study is carried out in the presence of noise. A similar truss is then built and the finite-element method (FEM) model of the structure is corrected using experimental data. The technique is applied to locate the damage in several members. The experimental results indicate that this technique can robustly identify the damaged member with limited measurements and real-time computation. The effectiveness of the damage detection measure is also demonstrated.

Two kinds of neural network algorithms suitable for fiber optic sensing array signal processing

Neural networks are employed to process signals from fiber optic sensing arrays embedded in physical structures to monitor structure parameters and to estimate structural damage in real time. The backpropagation neural network, the Kohonen neural network, and its variants the LVQ1 to LVQ5 algorithms (learning-vector quantization) are discussed in detail. Simulation experiments have been done to test the suitability of neural networks for signal processing of fiber optic sensing arrays. The results of these experiments are also given.