Journal of Engineering Mechanics

A nonparametric structural damage detection methodology based on nonlinear system identification approaches is presented for the health monitoring of structure-unknown systems. In its general form, the method requires no information about the topology or the nature of the physical system being monitored. The approach relies on the use of vibration measurements from a “healthy” system to train a neural network for identification purposes. Subsequently, the trained network is fed comparable vibration measurements from the same structure under different episodes of response in order to monitor the health of the structure and thereby provide a relatively sensitive indicator of changes (damage) in the underlying structure. For systems with certain topologies, the method can also furnish information about the region within which structural changes have occurred. The approach is applied to an intricate mechanical system that incorporates significant nonlinear behavior typically encountered in the applied mechanics field. The system was tested in its “virgin” state as well as in “damaged” states corresponding to different degrees of parameter changes. It is shown that the proposed method is a robust procedure and a practical tool for the detection and overall quantification of changes in nonlinear structures whose constitutive properties and topologies are not known.

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.

A new method, named the limit-state surface element (LSSE) method, for problems in structural reliability is presented. The method is based on discretization of the limit-state surface into a finite number of elements. This allows for better representations of the failure surface in regions of high curvature than is possible with standard FORM techniques. The reliability is estimated as a sum of integrals over subdomains in the space of standard normal variables associated with the surface elements. Several benchmark problems are presented to demonstrate the accuracy and utility of the method, and a problem in fatigue reliability with NDE inspections is presented as a practical application of the method and a demonstration of the potential interrelations between QNDE and reliability assessment.

The purpose of this paper is to examine the adaptive identification method and the nonlinear system identification technique through numerical simulation on seismic response of building structures. For the identification of a time-variant system and a system with an abrupt change of modal parameters, two parameter adaptation algorithms in recursive identification methods are discussed: recursive least square algorithm with constant trace and adaptive fading Kalman filter method. Based on the equivalent linear model, the time-variant model parameters can be identified. The tracking ability on the identification of a time-variant system is discussed. For the identification of the nonlinear system, the forward including algorithm and the neural network training algorithm were used to estimate the modal parameter of the nonlinear autoregressive moving average model, and the accuracy on the predicted output of nonlinear model identification was discussed.

This research uses laser ultrasonic techniques to monitor a (directly measurable) ultrasonic property—frequency-dependent Rayleigh wave velocity (material dispersion)—and then relates changes in this acoustic property to changes in the material's properties (such as stiffness) that characterize damage. The subject material system is a thick, glass-reinforced, vinylester (thermosetting) fiber-reinforced polymer (FRP) composite. Laser ultrasonics is an ideal methodology to monitor changes in the Rayleigh phase (or group) velocity of this material because of its high fidelity, broad bandwidth, point source/receiver, and noncontact nature. The experimental procedure consists of measuring a series of transient elastic waveforms in a thick FRP specimen and then operating on these waveforms with the 2D fast Fourier transform to develop the dispersion relationship for that specimen. Material degradation (damage) is introduced into these specimens with environmental aging, and the dispersion curves are used to quantitatively track changes in material properties as a function of degradation.

The most critical issues that have been identified as being needed to implement meaningful and beneficial applications of health monitoring for infrastructure management are discussed. Issues related to infrastructure health monitoring for management can be defined in three overlapping categories. In this context, engineering tools, methods, and knowledge should be developed and utilized to answer societal and educational aspects of the problem while taking advantage of the information tools and methodologies available today. The need for integrated research, education, and practice for infrastructure management is emphasized. The challenges in the integration of intelligent transportation and structural systems concepts within an optimum integrated asset management framework are described and illustrated. Examples from ongoing research on the health monitoring of short-span bridge families and long-span bridges are presented to illustrate the issues.

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.

An integrated procedure based on a direct adaptive control algorithm is applied to structural systems for both vibration suppression and damage detection. The wider class of noncollocated actuator-sensor schemes is investigated through parameterized linear functions of the state variables that preserve the minimum phase property of the system. A larger number of mechanical parameters are shown to be identifiable in noncollocated configurations. Proper output selection allowing for model reference control and tracking error based parameters estimation under persistent excitation is described. Using full-state feedback, these capabilities are effectively exploited for oscillation reduction and health monitoring of uncertain multi-degree-of-freedom (MDOF) shear-type structures.

A Bayesian probabilistic methodology for structural health monitoring is presented. The method uses a sequence of identified modal parameter data sets to compute the probability that continually updated model stiffness parameters are less than a specified fraction of the corresponding initial model stiffness parameters. In this approach, a high likelihood of reduction in model stiffness at a location is taken as a proxy for damage at the corresponding structural location. The concept extends the idea of using as indicators of damage the changes in structural model parameters that are identified from modal parameter data sets when the structure is initially in an undamaged state and then later in a possibly damaged state. The extension is needed, since effects such as variation in the identified modal parameters in the absence of damage, as well as unavoidable model error, lead to uncertainties in the updated model parameters that in practice obscure health assessment. The method is illustrated by simulating on-line monitoring, wherein specified modal parameters are identified on a regular basis and the probability of damage for each substructure is continually updated.

System identification of very large structures is of necessity accomplished by analyzing output measurements, as in the case of ambient vibration surveys. Conventional techniques typically identify system parameters by assuming (arguably) that the input is locally Gaussian white, and in so doing, effectively reduce the number of degrees of freedom of the estimation problem to a more tractable number. This paper describes a new approach that has several novel attributes, among them, elimination of the need for the Gaussian white input assumption. The approach involves a filter applied to an identification problem formulated in the frequency domain. The filter simultaneously estimates both system parameters and input excitation characteristics. The estimates we obtain are not guaranteed to be unique (as is true in all other approaches: simultaneous estimation of both system and input possesses too many degrees of freedom to guarantee uniqueness); but we do, nonetheless, identify system parameters and input excitation characteristics that are physically plausible and intuitively reasonable, without making input excitation assumptions. Simulated and laboratory experimental data are used to verify the algorithm and demonstrate its advantages over conventional approaches.

The NSF-sponsored Center for Advanced Cement-Based Materials is actively involved in research aimed at the development of technologies for health monitoring and nondestructive evaluation of the concrete infrastructure. This paper summarizes pertinent research performed at the center. Basic findings from several new laboratory-based nondestructive evaluation techniques for concrete are reported. The described techniques are based on measurements of mechanical waves that propagate in the concrete. First, ultrasonic longitudinal wave (also called the L-wave or P-wave) signal transmission (attenuation) measurements are shown to be sensitive to the presence of damage in the form of distributed cracking in concrete. Next, experimental procedures that enable practical one-sided wave signal transmission measurements to be performed on concrete structures are described. The utility of the signal transmission measurement is demonstrated by two experimental test series; the depths of surface-opening cracks in concrete slabs are estimated and the extent and nature of autogenous healing in concrete disks are studied. Finally, an approach by which fatigue-induced damage in concrete structures is nondestructively monitored is described. Vibration frequencies are shown to be sensitive to the presence of fatigue-induced cracking in concrete specimens; changes in the vibration frequency of a concrete specimen during fatigue tests are related to the remaining fatigue life of the test specimens.

Damage detection in vibrating beams, or beam systems, is dealt with in this paper. Damage is represented by a more or less concentrated decrease in stiffness. A linear behavior is assumed before and after the damage. A peculiar aspect of damage detection, at times neglected, is that damage is frequently concentrated in a few zones, albeit unknown, so that only the modification in the characteristics of a few sections or elements needs to be determined. Attention is focused on the basic aspects of the problem, by discussing the amount of frequencies necessary to locate and quantify the damage uniquely. Two different procedures of damage identification are used, which mainly take advantage of the peculiar characteristics of the problem. Cases with pseudoexperimental and experimental frequencies are solved. A generalization of the procedure based on finite-element models, which makes possible the tackling complex structural cases, is illustrated and discussed with some examples.

Synthetic aperture radar (SAR) provides high-quality images in all weather and all time conditions. It shows promise for acquiring spatial and temporal data for urban analysis. The objective of this study is threefold. First, the mathematical theory and physical background of SAR data acquisition and processing are described and introduced to the civil and engineering mechanics community. Second, a simulation tool based on the electromagnetic formulation is used to simulate SAR imagery of real-sized buildings as a proof of concept to demonstrate the usefulness of SAR applications and their imaging potentials and characteristics relevant to urban areas. Our third and last objective is to study the capabilities of damage and change diagnostic schemes based on simulated complex SAR images of pre- and post-events. A SAR image is obtained from a coherent imagery system and can be described in three dimensions, sometimes referred to as a complex imagery system, whereby two dimensions represent the spatial event of the image and the third dimension represents the phase content of the image. The phase information is relevant to the detection of the finer details in the image.