Hilbert-Huang Based Approach for Structural Damage Detection

When measured data contain damage events of the structure, it is important to extract the information of damage as much as possible from the data. In this paper, two methods are proposed for such a purpose. The first method, based on the empirical mode decomposition (EMD), is intended to extract damage spikes due to a sudden change of structural stiffness from the measured data thereby detecting the damage time instants and damage locations. The second method, based on EMD and Hilbert transform is capable of (1) detecting the damage time instants, and (2) determining the natural frequencies and damping ratios of the structure before and after damage. The two proposed methods are applied to a benchmark problem established by the ASCE Task Group on Structural Health Monitoring. Simulation results demonstrate that the proposed methods provide new and useful tools for the damage detection and evaluation of structures.     

Multivariate Statistical Approach to Structural Damage Detection

The issue of structural damage detection is addressed through an innovative multivariate statistical approach in this paper. By invoking principal component analysis, the vibration responses acquired from the structure being monitored are represented by the multivariate data of the sample principal component coefficients (PCCs). A damage indicator is then defined based on a multivariate exponentially weighted moving average control chart analysis formulation, involving special procedures to allow for the effects of the estimated parameters and to determine the upper control limits in the control chart analysis for structural damage detection applications. Also, a data shuffling procedure is proposed to remove the autocorrelation probably present in the obtained sample PCCs. This multivariate statistical structural damage detection scheme can be applied to either the time domain responses or the frequency domain responses. The efficacy and advantages of the scheme are demonstrated by the numerical examples of a five-story shear frame and a shear wall as well as the experimental example of the I-40 Bridge benchmark.     

Multimetric Sensing for Structural Damage Detection

Vibration-based damage detection methods have been widely studied for structural health monitoring of civil infrastructure. Acceleration measurements are frequently employed to extract the dynamic characteristics of the structure and locate damage because they can be obtained conveniently and possess relatively little noise. However, considering the fact that damage is a local phenomenon, the sole use of acceleration measurements that are intrinsically global structural responses limits damage detection capabilities. This paper investigates the possibility of using both global and local measurements to improve the accuracy and robustness of damage detection methods. A multimetric approach based on the damage locating vector method is proposed. Numerical simulations are conducted to verify the efficacy of the proposed approach.     

Structural Damage Detection from Modal Strain Energy Change

A structural damage detection method based on modal strain energy (MSE) change before and after damage is presented in this paper. The localization of damage based on MSE of each structural element is briefly presented, and the sensitivity of the MSE with respect to a damage is derived. The sensitivity is not based on any series expansion and is a function of the analytical mode shape changes and the stiffness matrix. Only incomplete measured mode shapes and analytical system matrices are required in this damage localization and quantification approach. Results from a numerical example and an experiment on a single-bay, two-story portal steel frame structure are investigated. The effects of measurement noise and truncated analytical mode shapes are discussed. Results indicate that the proposed approach is noise sensitive, but it can localize single and multiple damages. Damage quantification of two damages is successful with a maximum of 14% error under a 5% measurement noise.     

Structural Damage Detection and Assessment from Modal Response

This paper presents a global damage detection and assessment algorithm based on a parameter estimation method using a finite-element model and the measured modal response of a structure. Damage is characterized as a reduction of the member constitutive parameter from a known baseline value. An optimization scheme is proposed to localize damaged parts of the structure. The algorithm accounts for the possibility of multiple solutions to the parameter estimation problem that arises from using spatially sparse measurements. Errors in parameter estimates caused by sensitivity to measurement noise are reduced by selecting a near-optimal measurement set from the data at each stage of the localization algorithm. Damage probability functions are computed upon completion of the localization process for candidate elements. Monte Carlo methods are used to compute the required probabilities based on the statistical distributions of the parameters for the damaged and the associated baseline structure. The algorithm is tested in a numerical simulation environment using a planar bridge truss as a model problem.     

Structural Identification and Damage Detection from Noisy Modal Data

In this paper we present a simple, yet powerful, method for the identification of stiffness matrices of structural and mechanical systems from information about some of their measured natural frequencies and corresponding mode shapes of vibration. The method is computationally efficient and is shown to perform remarkably well in the presence of measurement errors in the mode shapes of vibration. It is applied to the identification of the stiffness distribution along the height of a simple vibrating structure. An example illustrating the method’s ability to detect structural damage that could be highly localized in a building structure is also given. The efficiency and accuracy with which the method yields estimates of the system’s stiffness from noisy modal measurement data makes it useful for rapid, on-line damage detection of structures.     

Structural Damage Detection Using Empirical Mode Decomposition: Experimental Investigation

This paper presents an experimental investigation on the applicability of the empirical mode decomposition (EMD) for identifying structural damage caused by a sudden change of structural stiffness. A three-story shear building model was constructed and installed on a shaking table with two springs horizontally connected to the first floor of the building to provide additional structural stiffness. Structural damage was simulated by suddenly releasing two pretensioned springs either simultaneously or successively. Various damage severities were produced using springs of different stiffness. A series of free vibration, random vibration, and earthquake simulation tests were performed on the building with sudden stiffness changes. Dynamic responses including floor accelerations and displacements, column strains, and spring releasing time instants were measured. The EMD was then applied to measured time histories to identify damage time instant and damage location for various test cases. The comparison of identified results with measured ones showed that damage time instants could be accurately detected in terms of damage spikes extracted directly from the measurement data by EMD. The damage location could be determined by the spatial distribution of the spikes along the building. The influence of damage severity, sampling frequency, and measured quantities on the performance of EMD for damage detection was also discussed.     

Structural Damage Detection from Wavelet Coefficient Sensitivity with Model Errors

The sensitivity of the wavelet coefficient from structural responses with respect to the system parameters is analytically derived. It is then used in a sensitivity-based inverse problem for structural damage detection with sinusoidal or impulsive excitation and acceleration and strain measurements. The sensitivity of the wavelet coefficient is shown more sensitive than the response sensitivity with an example of a single story plane frame. It is further found not sensitive to different types of model errors in the initial model including the support stiffness, mass density and flexural rigidity of members, damping ratio, and the excitation force. Simulation results show that the damage information is carried mostly in the higher vibration modes of the structure as diagnosed with the corresponding wavelet coefficients from its dynamic responses. A wavelet combination encompasses all the frequency bandwidth is used in the successful identification of a reinforced concrete beam in the laboratory.     

Structural Damage Detection Using Dynamic Properties Determined from Laboratory and Field Testing

Studies have shown that experimentally determined dynamic properties can be used to identify the characteristics of a structure. In this paper, a damage detection technique is developed and demonstrated using system identification, finite-element modeling, and a modal update process. The proposed approach, SFM, provides a rapid estimate of damage locations and magnitudes. The proposed methodology is applied to three case studies. The first is a numerical simulation using computer generated data. The second is an ASCE benchmark problem for structural health monitoring, where the results can be compared to other researchers. The third is a full-scale highway bridge that was field tested using a forced vibration shaking machine. In this case study, the bridge was shaken in several states of damage and the proposed methodology was utilized to detect and determine the location and extent of the damage. It was found that, using the collected data, the SFM approach was able to consistently predict the location of damage as well as estimate the magnitude of the damage.     

Electromagnetic Image Reconstruction for Damage Detection

Advanced composite materials are being used for seismic retrofit of reinforced concrete (RC) columns designed and constructed by old specifications. The writers are currently developing an electromagnetic (EM) imaging technology for assessing damage of the columns covered by such jackets, specifically debonding at the adhesive bonding layer between the jacket and the column. This technology takes advantage of the difference in electrical properties (including conductivity and dielectric constant) between the adhesive epoxy (under a good bonding condition) and air (in case of debonding). In this paper, an inversion method is presented that can efficiently and accurately reconstruct electrical property images (profiles) from continuous sinusoidal EM waves of different frequencies launched toward and reflected from the layered medium, such as the composite-adhesive-concrete medium. This inversion method consists of a forward model and an iterative optimization process. By comparing the wave signals sent to and reflected from the medium, the reflection coefficient of each layer of the medium can be measured and the electrical property profile (EM image) can be inverted. Using the inverted electrical property, the forward model computes the reflection coefficient. The Jacobian matrix method is applied to modify the inverted profile interactively to minimize the error between the measured and calculated reflection coefficient. A constrained optimization technique is introduced in order to achieve maximum stability of the inverted profile against noise. At the present stage, simulation study is carried out in which the electric property image for the bonding layer is satisfactorily reconstructed and debonding successfully detected using the proposed inversion method.     

Genetic Programming-Based Approach for Structural Optimization

The main purpose of this paper is to introduce genetic programming into civil engineering problem solving. This paper describes a genetic programming-based approach for simultaneous sizing, geometry, and topology optimization of structures. An encoding strategy is presented to map between the real structures and the genetic programming parse trees. Numerical results for two examples reveal that the proposed approach is capable of producing the topology and shape of the desired trusses and the sizing of all the structural components. Hence, this approach can potentially be a powerful search and optimization technique in civil engineering problem solving.     

Adaptive Quadratic Sum-Squares Error for Structural Damage Identification

The ability to detect damages online, based on vibration data measured from sensors, will ensure the reliability and safety of structures. Innovative data analysis techniques for the damage detection of structures have received considerable attention recently, although the problem is quite challenging. In this paper, we proposed a new data analysis method, referred to as the quadratic sum-squares error (QSSE) approach, for the online or almost online identification of structural parameters. Analytical recursive solution for the proposed QSSE method, which is not available in the previous literature, is derived and presented. Further, an adaptive tracking technique recently proposed is implemented in the proposed QSSE approach to identify the time-varying system parameters of the structure, referred to as the adaptive quadratic sum-squares error. The accuracy and effectiveness of the proposed approach are demonstrated using both linear and nonlinear structures. Simulation results using the finite-element models demonstrate that the proposed approach is capable of tracking the changes of structural parameters leading to the identification of structural damages.     

Microwave Subsurface Imaging Technology for Damage Detection

This paper presents a technology for detecting invisible damage inside concrete, which is based on reconstruction of dielectric profile (image) of the concrete illuminated with microwaves sent from and received by antenna arrays controlled by specialized software. The imaging system developed in this study consists of an 8×8 transmitting and an 8×8 receiving arrays, an innovative numerical bifocusing operator for improving image resolution, and imaging software for reconstructing a two-dimensional image from the scattered field. The effectiveness of the developed technology in detecting steel and voids inside concrete has been demonstrated through numerical simulation and experiments.     

结构钢表面疲劳损伤演化过程的显微硬度研究方法

通过对疲劳过程中结构钢表面显微硬度测试结果的检验，证明了不同疲劳阶段的显微硬度测试值服从正态概率分布。依据损伤力学的基本理论建立了用显微硬度定义疲劳损伤变量的概率表达式，并应用于材料表面的损伤行为研究。分析表明，材料微观性能劣化是导致材料表面萌生显微裂纹的主要原因，而在微细观尺度上材料的损伤演化将呈现出显的不均匀性和概率统计特征。并从疲劳裂纹萌生的无损检测角度指出了显微硬度方法是一种较方便和灵敏     

Flexibility Based Approach for Damage Characterization: Benchmark Application

A flexibility based damage characterization technique is described and its performance is examined in the context of Phase 1 of the benchmark study developed by the IASC-ASCE SHM Task Group. Noteworthy features of the analytical development are: (1) the methodology used to extract a matrix that is proportional to the flexibility when the excitation is stochastic; (2) the technique used to interrogate the changes in flexibility (or flexibility proportional matrices) with regards to the location of the damage; and (3) the method used to quantify the damage without the use of a model. The strategy proved successful in all the cases considered.     

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 damage 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.     

Micromorphic Approach for Gradient Elasticity, Viscoplasticity, and Damage

A unifying thermomechanical framework is presented that reconciles several classes of gradient elastoviscoplasticity and damage models proposed in the literature during the last 40?years. It is based on the introduction of the micromorphic counterpart χ? of a selected state or internal variable ? in a standard constitutive model. In addition to the classical balance of momentum equation, a balance of micromorphic momentum is derived that involves generalized stress tensors. The corresponding additional boundary conditions are also deduced from the procedure. The power of generalized forces is assumed to contribute to the energy balance equation. The free energy density function is then chosen to depend on a relative generalized strain, typically ?-χ?, and the microstrain gradient ?χ?. When applied to the deformation gradient itself, ? ≡ , the method yields the micromorphic theory of Eringen and Mindlin together with its extension to finite deformation elastoviscoplasticity by Forest and Sievert. If the selected variable is the cumulative plastic strain, the theory reduces to the so-called “nonlocal implicit gradient-enhanced elastoplasticity model” by Engelen, Geers, and Peerlings, provided that simplified linear relationships are adopted between generalized stresses and strains. The same holds if the micromorphic variable coincides with a microdamage variable. If the internal constraint is introduced that the micromorphic variable χ? remains as close as possible to the macroscopic variable ?, the micromorphic model reduces to the second gradient or gradient of internal variable approach as defined by Maugin. If the selected variable is the cumulative plastic strain or the full plastic strain tensor, the constrained micromorphic theory delivers Aifantis-like strain gradient plasticity models. The advantage of the micromorphic approach is that it provides the generalized balance equation under nonisothermal conditions and offers the setting for anisotropic nonlinear constitutive relations between generalized stress and strains in contrast to most existing models. In rate-independent plasticity, it is shown that there is generally no need for a variational formulation of the yield condition.     

Terrestrial Laser Scanning-Based Structural Damage Assessment

Terrestrial laser scanning (TLS) provides a rapid, remote sensing technique to model 3D objects. Previous work applying TLS to structural analysis has demonstrated its effectiveness in capturing simple beam deflections and modeling existing structures. This paper extends TLS to the application of damage detection and volumetric change analysis for a full-scale structural test specimen. Importantly, it provides a framework necessary for such applications, in combination with an analysis approach that does not require tedious development of complex surfaces. Intuitive slicing analysis methods are presented, which can be automated for rapid generation of results. In comparison with conventional photographic and surface analysis methods, the proposed approach proved consistent. Furthermore, the TLS data provided additional insight into geometric change not apparent using conventional methods. As with any digital record, a key benefit to the proposed approach is the resulting virtual test specimen, which is available for posttest analysis long after the original specimen is demolished. Uncertainties that can be introduced from large TLS data sets, mixed pixels and parallax in the TLS analysis are also discussed.     

Bayesian Probabilistic Inference for Nonparametric Damage Detection of Structures

This paper presents a Bayesian hypothesis testing-based probabilistic assessment method for nonparametric damage detection of building structures, considering the uncertainties in both experimental results and model prediction. A dynamic fuzzy wavelet neural network method is employed as a nonparametric system identification model to predict the structural responses for damage evaluation. A Bayes factor evaluation metric is derived based on Bayes’ theorem and Gaussian distribution assumption of the difference between the experimental data and model prediction. The metric provides quantitative measure for assessing the accuracy of system identification and the state of global health of structures. The probability density function of the Bayes factor is constructed using the statistics of the difference of response quantities and Monte Carlo simulation technique to address the uncertainties in both experimental data and model prediction. The methodology is investigated with five damage scenarios of a four-story benchmark building. Numerical results demonstrate that the proposed methodology provides an effective approach for quantifying the damage confidence in the structural condition assessment.     

某工业构筑物混凝土基座腐蚀损伤检测

工业构筑物混凝土基座由于介质的不断侵蚀,造成了混凝土构件腐蚀损伤。需对此类混凝土结构进行检测,确定其腐蚀损伤程度,为后续隐患治理提供技术依据。这也是加固维修前的必要工作。总结了工业构筑物混凝土结构腐蚀损伤检测内容、检测方法,对类似因工业介质侵蚀的混凝土结构工程的检测鉴定具有借鉴意义。