An experiment-based frequency sensitivity enhancing control approach for structuraldamage detection

Bayesian‐optimized unsupervised learning approach for structural damage detection

Distributed long gauge strain sensing technology has solved the problem of difficult identification of local damage in traditional ‘point’ monitoring, and has received extensive attention in the field of structural damage identification. Owing to the inevitable presence of measurement noise and environmental factors in the macro strain response measurement, a single damage index has also underlined some drawbacks generally arising when multiple damages occur, or errors affect the identified dynamic properties of the systems. To address these challenges, this paper proposes a data fusion method based on the Dempster–Shafer evidence theory, relying on distributed strain sensing technology. The identification results of the modal macro strain-based index and quasi-static macro strain energy-based damage index are fused to make a comprehensive decision on structural damage location. Damage identification studies are conducted on different types of structures under impact loads and random wind loads to verify the effectiveness and accuracy of the proposed data fusion method in the case of single and multiple damage conditions. The results show that the proposed data fusion method can accurately identify the damage location and effectively reduce misjudgment on undamaged locations; it has potential application value in practical structural health monitoring.

It is still necessary to investigate the detection of structural damage under ambient excitations since the excitations are random and unmeasured while measurement noises are inevitable. In this paper, a method based on the synthesis of cross-correlation functions of partial structural responses and the extended Kalman filter (EKF) approach is proposed for the identification and damage detection of structures under ambient excitations, in which both independent stationary and non-stationary white noise excitations in the product models are discussed. First, the equations of cross-correlation functions of structural responses are established when the ambient excitations are independent stationary white noise processes. Then, the EKF approach is utilized to identify structural parameters and cross-correlation functions using partial measurements of structural acceleration responses. Structural damage is detected based on the degradations of the identified structural element stiffness parameters. Finally, the proposed method is extended to deal with independent non-stationary white noise excitations in the product models. The numerical simulation examples of the ASCE structural health monitoring benchmark building subject to ambient excitation, a moment resisting frame model under white noise excitation, and a cantilever beam model under multiple independent non-stationary excitations are used to validate the feasibility of the proposed method. It is shown that the method is not sensitive to measurement noises. Also, a lab experimental study of the identification of a multi-story shear structure is investigated to further illustrate the applicability of the proposed method.

A generalized flexibility matrix based approach for structural damage detection

Damage in structures can negatively affect their functionality and safety and leads to failure. Thus it is very important to monitor structures for occurrence, location and severity of damage. Structural health monitoring techniques provide information on the life expectancy of structures simultaneously detects and locates structural damage. Damage identification of structures based on vibration has always been important subjects and are being rapidly used to damage and location of structures. Artificial Neural Networks (ANNs) as a soft computing method using dynamic parameters of structures have been utilized increasingly for structural damage detection due to their excellent pattern recognition capability. Dynamic parameters of structure are easy to implement for damage identification and can directly linked to the topology of structure. This study presents the application of ANN for damage identification in steel beams using dynamic parameters. For identification of severity and location of damage, at first, five individual neural networks corresponding to mode 1 to mode 5 are considered. At the second step, a method based on neural network ensemble is proposed to combine the outcomes of the individual neural networks to a single network. Ensemble results were evaluated and discussed according to the differences between predicted output by ANN and desire data (target data) obtained from experimental modal analysis of structure.KeywordsArtificial neural networksMean square errorModal analysisDamage detectionNeural network ensemble

The results of numerical simulations to test a method to locate and estimate the severity of damage in a structure in the blind mode, by using the simulated data from a four-story steel frame structure analyzed at the Univ. of British Columbia, are reported here. The blind simulated data include the response in the time domain of the undamaged structure and the responses of the structure with two sets of simulated damage conditions. Elements of the theory utilized here for damage detection and localization (the so-called damage index method) are first summarized. Modal analyses are performed to extract the modal characteristics (i.e., resonant frequencies and mode shapes) for the undamaged structure (designated, Case 1) and the damaged structures (here designated, Damage Cases 2 and 3). The results of the modal analyses are reported in detail. Using the proposed nondestructive damage evaluation technique, the predicted damage locations, and estimation of the damage severity are provided for the two damage cases. The actual locations of damage in the test structure for each of the damage cases were revealed subsequent to the damage predictions proposed by the writers during the 16th International Modal Analysis Conference. The predicted damage locations and severity estimates are compared with the damage inflicted in the original test cases. The current method correctly predicts the inflicted damage locations and provides realistic estimates of the associated damage severities.

One of the important goals of structural health monitoring is damage detection. Although many methods have been proposed to detect the existence of structural damage, relatively few studies are found on higher-level damage diagnosis such as identification of the location and extent of damage. In this paper, multiple substructural damage identification models based on regression between internal responses and boundary responses of individual beam elements in either plane or three-dimensional space are derived. Three damage indexes are defined from regression model characteristics, and two change-point analysis methods are adopted to capture changes in damage index sequences which are extracted from structural monitoring data sets from healthy and unknown states. Possible damage locations are identified as where the most significant changes in the damage indexes occur, and a voting scheme is used to synthesize the results from different algorithms. This damage detection approach is straightforward and efficient, with the regression coefficients directly related to the structural stiffness properties. The numerical and experimental application results show that the method successfully identifies and locates structural change in most of the cases.

Many existing damage identification or quantification methods can be employed only if the internal and external mass changes are negligible when tested at two different states of a structure. This paper presents a new Modal Kinetic Energy (MKE)-based method to detect and quantify damage using modal properties of structures, which can be employed even in situations when mass change is not more than a certain extent. A new damage sensitivity parameter has been developed using measured modal characteristics of baseline structure. The MKE change (MKEC) concept is then employed to locate damage and to estimate relative perturbation at each element. The relative damage extent vector is estimated by searching the best correlation between the analytical and experimental MKEC vectors with the help of genetic algorithm optimization tool. The extent of damage is calculated after computing damage scaling coefficient using measured eigenvalue change vector. A numerical study is carried out on a simply supported single span beam to confirm its performance under various test conditions. The robustness of the proposed MKE method and the significance of mass variation in the damage detection approach are evaluated by comparing the damage quantification results with a traditional approach. Finally, the proposed damage detection method is applied on a two-span simply supported beam for single and multiple damage scenarios by extracting the modal properties experimentally. The results revealed that the proposed approach is capable of detecting and estimating single and multiple damages with reasonable accuracy even in moderate noise contaminated and mass change environments.

SUMMARY In this study, a data-driven approach for structural seismic damage detection and localization in a multiple-degree-of-freedom shear-type building structure subjected to strong ground motion is presented. The proposed method is based on the joint implementation of time–frequency analysis and fractal dimension (FD) characteristics. The approximate analytical wavelet transform is first used to obtain the time–frequency feature (TFF) of the transient response at the measured story. The TFF is defined as the real part of the wavelet coefficients. Next, the box-counting method is used to acquire the FD of the TFF within the fundamental frequency band. It is verified that the proposed FDs at all stories of the linear system are identical, whereas the FDs at the stories with nonlinearities will be different from those at the stories with linearity. Therefore, the nonlinearity of the structure caused by strong ground motion can be detected and localized by comparing the FDs at the measured stories. A numerical simulation on a 10-story shear-type building was conducted. The simulation results indicate that the aforementioned approach is capable of detecting and localizing single-location or multiple-location seismic damages in shear-type building structures under various seismic excitations and is robust to measurement noise. Finally, the sensor placement for this approach, the effect of damping ratio, and choice of interstory drifts or accelerations as the measured signals are investigated and discussed. Copyright © 2012 John Wiley & Sons, Ltd.

In the past few decades, the need for efficient and reliable Structural Health Monitoring strategies has led to the development of several approaches for damage detection and characterization purposes. Among them, the Nonlinear Vibro-Acoustic Modulation (VAM) exploits the modulation arising from the interaction of two concurrently applied driving waves, namely the probe and the pump excitations, in the presence of nonlinear scatters such as cracks and defects. Therefore, the VAM provides information on the emergence of internal damage by extracting the nonlinear modulated components of the response of a damaged system. Originally proposed for granular media, the method has shown to be effective in detecting the presence of defects also in metals and composite materials. Nonetheless, its efficacy is highly affected by the excitation frequencies, which are usually chosen among the system resonances. The need for a preliminary modal analysis and, at once, the risk of selecting pump-probe frequency combinations with low sensitivity to damage may make the procedure time-consuming and not fully reliable, preventing the VAM technique from being widely accepted as a robust monitoring tool. To overcome these limitations, a broadband excitation may be used. This study assesses the effectiveness of the VAM technique when a combination of a frequency-swept pump excitation and a mono-harmonic probe wave is applied to drive the sample. Experimental tests were conducted on a composite laminated beam mounted on an electrodynamic shaker and tested in both pristine and damaged conditions. Low-profile surface-bonded piezoceramic transducers were used for both probe excitation and sensing. Barely visible impact damage (BVID) was introduced in the composite beam to examine the potential of the approach for the detection of very small, localized damage. The results show that the use of VAM with a broadband low-frequency excitation may be an effective option for identifying nonlinearities associated with typical damage occurring in composite structures.

The high structural deficient rate poses serious risks to the operation of many bridges and buildings. To prevent critical damage and structural collapse, a quick structural health diagnosis tool is needed during normal operation or immediately after extreme events. In structural health monitoring (SHM), many existing methods will have limited usefulness in the quick damage identification process because (1) the damage event needs to be identified quickly, and (2) postdamage information is usually unavailable. To address these drawbacks, we propose a new damage detection and localization approach based on stochastic time series analysis. Specifically, damage sensitive features, which are extracted from vibration signals, follow different distributions before and after a damage event. Hence, we use optimal change-point detection theory to find the time of damage occurrence. Because existing change-point detectors require the postdamage feature distribution, which is unavailable in SHM, we propose a maximum likelihood method for learning the distribution parameters from the time-series data. The proposed damage detection using estimated parameters achieves optimal performance. Also, we utilize the detection results to find damage location without any further computation. Validation results show highly accurate damage identification in American Society of Civil Engineers benchmark structures and two shake table experiments.

Most of the vibration based damage detection methods are based on changes in frequencies, mode shapes, mode shape curvature, and flexibilities. These methods are limited and typically can only detect the presence and location of damage. Current methods seldom can identify the exact severity of damage to structures. This paper will present research in the development of a new non-destructive evaluation method to identify the existence, location, and severity of damage for structural systems. The method utilizes the concept of invariant stress resultants (ISR). The basic concept of ISR is that at any given cross section the resultant internal force distribution in a structural member is not affected by the inflicted damage. The method utilizes dynamic analysis of the structure to simulate direct measurements of acceleration, velocity and displacement simultaneously. The proposed dynamic ISR method is developed and utilized to detect the damage of corresponding changes in mass, damping and stiffness. The objectives of this research are to develop the basic theory of the dynamic ISR method, apply it to the specific types of structures, and verify the accuracy of the developed theory. Numerical results that demonstrate the application of the method will reflect the advanced sensitivity and accuracy in characterizing multiple damage locations.

This paper presents a two-step approach for structural damage identification in beam structure using the influence line and bird mating optimizer (BMO). Local damage is simulated as the reduction of the elemental Young’s modulus and mass of beam element. The technique for damage localization based on influence line and its derivatives before and after damage for beam structure was outlined. An objective function comprised of dynamic acceleration is utilized for BMO algorithm. The dynamic response data under external force is calculated by Newmark integration method. Numerical examples of a simply supported beam was investigated. Effect of measurement noise is studied. Studies in the paper indicate that the proposed method is efficient and robust for identifying damages in beam structures.

A coupled approach for damage detection of framed structures using piezoelectric signature

Nowadays, structural health monitoring has attracted much attention, due to the construction of important and complex structures and their safety. One branch of structural health monitoring is damage detection, estimating the location and extent of eventual damages in the structural systems. In this study, an efficient method is introduced to determine the location and extent of damage in the frame, beam and truss structures using time domain response and an optimization algorithm. First of all, the structural damage detection problem is formulated as a standard optimization problem. So that, the optimization objective function is defined by using the acceleration of damaged structures and analytical model acceleration. The acceleration is obtained using Newmark method. Damage is simulated by reducing the elasticity modulus of structural members. Then, damage problem converted to an optimization problem are solved by teaching–learning-based optimization (TLBO) algorithm therefore, the exact location and extent of damage in structure can be determined. In order to show the capabilities of the proposed approach for identifying structural damage, four illustrative test examples are considered with considering measurement noise effect. The results clearly show that the proposed method is as a powerful method to detect multiple damage in structures.