Electromechanical Impedance-Based Structural Health Monitoring with Evolutionary Programming

An impedance-based structural health monitoring technique is presented. By analyzing the in-plane vibration of a thin lead–zirconate–titanate (PZT) patch, the electromechanical impedance of the PZT patch is predicted. The force impedances of a beam and a plate with damage are calculated by Ritz method using polynomial as shape functions. The damage is then identified from the changes of the impedance spectra caused by the appearance of damage. A hybrid evolutionary programming is employed as a global search technique to back-calculate the damage. A specially designed fitness function is proposed, which is able to effectively reduce the inaccuracy in representing the real structure using analytical or numerical models. Experiments are carried out on a beam and a plate to verify the numerical predictions. The results demonstrate that the proposed method is able to effectively and reliably locate and quantify the damage in the beam and the plate.     

Vibration-based Damage Detection of Structures by Genetic Algorithm

Vibration-based methods are being rapidly applied to detect structural damage. The usual approaches incorporate sensitivity analysis and the optimization algorithm to minimize the discrepancies between the measured vibration data and the analytical data. However, conventional optimization methods are gradient based and usually lead to a local minimum only. Genetic algorithms explore the region of the whole solution space and can obtain the global optimum. In this paper, a genetic algorithm with real number encoding is applied to identify the structural damage by minimizing the objective function, which directly compares the changes in the measurements before and after damage. Three different criteria are considered, namely, the frequency changes, the mode shape changes, and a combination of the two. A laboratory tested cantilever beam and a frame are used to demonstrate the proposed technique. Numerical results show that the damaged elements can be detected by genetic algorithm, even when the analytical model is not accurate.     

Handling of Constraints in Finite-Element Response Sensitivity Analysis

In this paper, the direct differentiation method (DDM) for finite-element (FE) response sensitivity analysis is extended to linear and nonlinear FE models with multi-point constraints (MPCs). The analytical developments are provided for three different constraint handling methods, namely: (1) the transformation equation method; (2) the Lagrange multiplier method; and (3) the penalty function method. Two nonlinear benchmark applications are presented: (1) a two-dimensional soil-foundation-structure interaction system and (2) a three-dimensional, one-bay by one-bay, three-story reinforced concrete building with floor slabs modeled as rigid diaphragms, both subjected to seismic excitation. Time histories of response parameters and their sensitivities to material constitutive parameters are computed and discussed, with emphasis on the relative importance of these parameters in affecting the structural response. The DDM-based response sensitivity results are compared with corresponding forward finite difference analysis results, thus validating the formulation presented and its computer implementation. The developments presented in this paper close an important gap between FE response-only analysis and FE response sensitivity analysis through the DDM, extending the latter to applications requiring response sensitivities of FE models with MPCs. These applications include structural optimization, structural reliability analysis, and finite-element model updating.     

Calibration of Soil Constitutive Models with Spatially Varying Parameters

Soil constitutive models are frequently calibrated from laboratory tests that utilize global boundary measurements, which necessarily relegate soil response to that of a homogenized equivalent medium. This paper demonstrates the applicability of advanced experimental technologies to enhance the state of model-based predictions in soil mechanics by taking into account the possibility of material heterogeneity during model calibration. By utilizing the full-field displacement measurement technique of three-dimensional digital image correlation, displacements of the surfaces of deforming triaxial sand specimens are measured throughout deformation. These displacements are assimilated into finite-element (FE) models of the test specimen through solution of an inverse problem. During optimization, in which the difference between measured and predicted displacements across the specimen surface form the basis for the objective function, model parameters are allowed to vary spatially throughout the specimen volume. FE models allowing three different levels of spatial variability are tested. Results indicate that accommodating consideration of material heterogeneity during calibration leads to more accurate predictions of global stress-strain behavior that are more faithful to observed full-field response.     

Wavelet-Based Approach for Structural Damage Detection

A wavelet-based approach is proposed for structural damage detection and health monitoring. Characteristics of representative vibration signals under the wavelet transformation are examined. The methodology is then applied to simulation data generated from a simple structural model subjected to a harmonic excitation. The model consists of multiple breakable springs, some of which may suffer irreversible damage when the response exceeds a threshold value or the number of cycles of motion is accumulated beyond their fatigue life. In cases of either abrupt or accumulative damages, occurrence of damage and the moment when it occurs can be clearly determined in the details of the wavelet decomposition of these data. Similar results are observed for the real acceleration data of the seismic response recorded on the roof of a building during the 1971 San Fernando earthquake. Effects of noise intensity and damage severity are investigated and presented by a detectability map. Results show the great promise of the wavelet approach for damage detection and structural health monitoring.     

Natural Excitation Technique and Eigensystem Realization Algorithm for Phase I of the IASC-ASCE Benchmark Problem: Simulated Data

A benchmark study in structural health monitoring based on simulated structural response data was developed by the joint IASC–ASCE Task Group on Structural Health Monitoring. This benchmark study was created to facilitate a comparison of various methods employed for the health monitoring of structures. The focus of the problem is simulated acceleration response data from an analytical model of an existing physical structure. Noise in the sensors is simulated in the benchmark problem by adding a stationary, broadband signal to the responses. A structural health monitoring method for determining the location and severity of damage is developed and implemented herein. The method uses the natural excitation technique in conjunction with the eigensystem realization algorithm for identification of modal parameters, and a least squares optimization to estimate the stiffness parameters. Applying this method to both undamaged and damaged response data, a comparison of results gives indication of the location and extent of damage. This method is then applied using the structural response data generated with two different models, different excitations, and various damage patterns. The proposed method is shown to be effective for damage identification. Additionally the method is found to be relatively insensitive to the simulated sensor noise.     

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.     

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.     

Experimentally Validated Added Mass Identification Algorithm Based on Frequency Response Functions

A methodology is presented for detecting added mass in structural systems maintaining a linear response. A single frequency response function measured at several frequencies along with a correlated analytical model of the structure in its original state are used to detect and quantify the added mass. A computationally efficient method of recalculating a single frequency response function is utilized in the identification algorithm. Experimental results from a frame structure are presented to validate and assess the proposed approach.     

Identification of Parametric Variations of Structures Based on Least Squares Estimation and Adaptive Tracking Technique

An important objective of health monitoring systems for civil infrastructures is to identify the state of the structure and to detect the damage when it occurs. System identification and damage detection, based on measured vibration data, have received considerable attention recently. Frequently, the damage of a structure may be reflected by a change of some parameters in structural elements, such as a degradation of the stiffness. Hence it is important to develop data analysis techniques that are capable of detecting the parametric changes of structural elements during a severe event, such as the earthquake. In this paper, we propose a new adaptive tracking technique, based on the least-squares estimation approach, to identify the time-varying structural parameters. In particular, the new technique proposed is capable of tracking the abrupt changes of system parameters from which the event and the severity of the structural damage may be detected. The proposed technique is applied to linear structures, including the Phase I ASCE structural health monitoring benchmark building, and a nonlinear elastic structure to demonstrate its performance and advantages. Simulation results demonstrate that the proposed technique is capable of tracking the parametric change of structures due to damages.     

Impedance-Based Method for Nondestructive Damage Identification

A structural damage identification technique based on the impedance method is presented in this paper using smart piezoelectric transducer (PZT) patches. A modeling framework is developed to determine the structural impedance response and the dynamic output forces of PZT patches from the electric admittance measurements. A damage identification scheme for solving the nonlinear optimization problem is proposed to locate and quantify the structural damage through the minimization of the discrepancy between the structural impedance response and the numerically computed frequency response. The proposed technique does not use modal analysis or model reduction, and only the electric admittance measurements of PZT patches and the analytical system matrices are required. A beam example has been employed to illustrate the effectiveness of the proposed algorithm numerically. Furthermore, the influence of the measurement noise on the results has been investigated.     

Application of a Statistical Model Updating Approach on Phase I of the IASC-ASCE Structural Health Monitoring Benchmark Study

This paper addresses the problem of structural health monitoring (SHM) and damage detection based on a statistical model updating methodology which utilizes the measured vibration responses of the structure without any knowledge of the input excitation. The emphasis in this paper is on the application of the proposed methodology in Phase I of the benchmark study set up by the IASC–ASCE Task Group on structural health monitoring. Details of this SHM benchmark study are available on the Task Group web site at 〈http://wusceel.cive.wustl.edu/asce.shm〉. The benchmark study focuses on important issues, such as: (1) measurement noise; (2) modeling error; (3) lack of input measurements; and (4) limited number of sensors. A statistical methodology for model updating is adopted in this paper to establish stiffness reductions due to damage. This methodology allows for an explicit treatment of the measurement noise, modeling error, and possible nonuniqueness issues characterizing this inverse problem. The paper briefly describes the methodology and reports on the results obtained in detecting damage in all six cases of Phase I of the benchmark study assuming unknown (ambient) data. The performance, limitations, and difficulties encountered by the proposed statistical methodology are discussed.     

Two-Stage Structural Health Monitoring Approach for Phase I Benchmark Studies

This paper presents a two-stage structural health monitoring methodology and applies it to the Phase I benchmark study sponsored by the IASC-ASCE Task Group on Structural Health Monitoring. In the first stage, modal parameters are identified using measured structural response from the undamaged system and then from the (possibly) damaged system. In the second stage, these data are used to update a parametrized structural model of the system using Bayesian system identification. The approach allows one to obtain not only estimates of the stiffness parameters but also the probability that damage in any substructure exceeds any specified threshold expressed in terms of a fractional stiffness loss. It successfully identifies the location and severity of damage in all cases of the benchmark problem.     

Development of Static-Response-Based Objective Functions for Finite-Element Modeling of Bridges

The basic mechanisms and procedures of finite-element (FE) modeling and calibration are briefly presented in the context of bridge condition assessment. Different physical parameters of FE models are adjusted to simulate experimental measurements. To quantify the calibration process, static-response-based objective functions are carefully developed based on two powerful condition indices: bridge girder condition indicators and unit influence lines. Critical issues related to the indices are discussed in detail. Using an existing calibration strategy, a nominal FE bridge model is optimized by minimizing this global static-response-based objective function. The value of the objective function is reduced from 12.98 to 4.45%, which indicates convergence of the calibration process. It is shown that the automated calibration becomes practical due to the formulation of the static-response-based objective function.     

Simulated Micromechanical Models Using Artificial Neural Networks

A new method, termed simulated micromechanical models using artificial neural networks (MMANN), is proposed to generate micromechanical material models for nonlinear and damage behavior of heterogeneous materials. Artificial neural networks (ANN) are trained with results from detailed nonlinear finite-element (FE) analyses of a repeating unit cell (UC), with and without induced damage, e.g., voids or cracks between the fiber and matrix phases. The FE simulations are used to form the effective stress-strain response for a unit cell with different geometry and damage parameters. The FE analyses are performed for a relatively small number of applied strain paths and damage parameters. It is shown that MMANN material models of this type exhibit many interesting features, including different tension and compression response, that are usually difficult to model by conventional micromechanical approaches. MMANN material models can be easily applied in a displacement-based FE for nonlinear analysis of composite structures. Application examples are shown where micromodels are generated to represent the homogenized nonlinear multiaxial response of a unidirectional composite with and without damage. In the case of analysis with damage growth, thermodynamics with irreversible processes (TIP) is used to derive the response of an equivalent homogenized damage medium with evolution equations for damage. The proposed damage formulation incorporates the generalizations generated by the MMANN method for stresses and other possible responses from analysis results of unit cells with fixed levels of damage.     

Damage Detection based on Single-Input-Single-Output Measurements

A methodology is presented for detecting damage of structural systems maintaining a linear response. A single frequency response function measured at several frequencies along with a correlated analytical model of the undamaged structure are used to detect and assess damage. The method is directed toward situations where the number of damaged elements is generally known to be limited. A computationally efficient method of recalculating a single receptance is presented. Numerical results for a two-dimensional structural frame are presented to validate and assess the proposed approach. Issues for the development of the approach are discussed.     

Application of Wavelet Approach for ASCE Structural Health Monitoring Benchmark Studies

This paper presents an application of wavelet analysis for damage detection and locating damage region(s) for the ASCE structural health monitoring benchmark data. The response simulation data were generated basically by a FEM program provided by the ASCE Task Group on Health Monitoring for a four-story prototype building structure subjected to simulated stochastic wind loading. Damage was introduced in the middle of response by breaking one or more structure elements such as interstory braces. Wavelets were used to analyze the simulation data. It was found that structural damage due to sudden breakage of structural elements and the time when it occurred can be clearly detected by spikes in the wavelet details. The damaged region can be determined by the spatial distribution pattern of the observed spikes. The effects of measurement noise and the severity of damage were investigated. The results in this paper illustrate a great promise of wavelet analysis for structural health monitoring, especially for an on-line application.     

Development of Dynamic-Response-Based Objective Functions for Finite-Element Modeling of Bridges

The basic mechanism and procedures of finite-element (FE) bridge modeling and calibration are briefly presented. Different physical parameters of FE models are adjusted during the calibration process. Dynamic-response-based objective functions are carefully developed based on two powerful indices: the modal assurance criterion and frequency correlation trend line. The nominal bridge models are calibrated by minimizing the quantified difference between analytical results and experimental measurements. Using an existing calibration strategy, a nominal FE bridge model is optimized by minimizing this global dynamic-response-based objective function. The value of the objective function is reduced from 10.70 to 4.61%. The minimization of the objective function indicates the convergence of calibration and it is shown that the automated calibration becomes practical due to the formulation of the dynamic-response-based objective function.     

Finite element investigation of backbone binder removal from MIM copper compact

Abstract

Finite element (FE) model based on kinetic analysis was developed to describe the thermal debinding process of previously solvent debinded metal injection moulded (MIM) copper compacts. Thermophysical properties (specific heat, density, thermal diffusivity and thermal conductivity as a function of temperature) of MIM copper compact were measured using differential scanning calorimeter, laser flash analyser, thermogravimetry analyser and pushrod dilatometer. The proposed model is solved numerically to study binder removal and binder distribution during thermal debinding. The investigations included the analysis of residual (backbone) binder content for cylindrical MIM copper compacts at different temperatures and positions. The FE calculations are strongly based on measured thermophysical data and kinetic analysis of copper system. The FE simulated and experimental results were compared to validate the underlying FE model based on FE temperature field calculations. Drawing the real furnace temperature conditions in finite calculation can result in obtaining more accurate data.     

Development of an Elastoviscoplastic Microstructural-Based Continuum Model to Predict Permanent Deformation in Hot Mix Asphalt

Permanent deformation in hot mix asphalt is caused by a combination of densification (decrease in volume and hence increase in density) and shear deformation. The primary objective of this paper is to develop an elastoviscoplastic model that accounts for the influence of important microstructure properties such as anisotropy and damage on permanent deformation. The model incorporates a yield surface based on the Drucker-Prager function that is modified to capture the influence of stress state on the material response. Also, parameters that reflect the directional distribution of aggregates and damage density in the microstructure are included in this yield surface model. The elastoviscoplastic model is converted into a numerical formulation and is implemented in finite element (FE). The FE model is used in this study to simulate experimental measurements under different confining pressures and strain rates.