Hae-Bum Yun

Comparison of Modeling Approaches for Full-scale Nonlinear Viscous Dampers

A study is presented comparing several identification approaches, both parametric and nonparametric, for developing reduced-order nonlinear models of full-scale nonlinear viscous dampers commonly used with large flexible bridges. Such models are useful for incorporation into large-scale computational models, as well as for use as part of structural health monitoring studies based on vibration signature analysis. The paper reports the analysis results from a large collection of experimental tests on a 1112 kN (250 kip) orifice viscous damper under a wide range of frequency and amplitude oscillations. A simplified parametric design model is used in the parametric phase, as well as two different nonparametric methods: the Restoring Force Method, and artificial neural networks. The variations of model parameters with the excitation and response characteristics are investigated, and the relative accuracy and fidelity of the modeling approaches are compared and evaluated.

Improvement of Crack-Detection Accuracy Using a Novel Crack Defragmentation Technique in Image-Based Road Assessment

AbstractA common problem of crack-extraction algorithms is that extracted crack image components are usually fragmented in their crack paths. A novel crack-defragmentation technique, MorphLink-C, is proposed to connect crack fragments for road pavement. It consists of two subprocesses, including fragment grouping using the dilation transform and fragment connection using the thinning transform. The proposed fragment connection technique is self-adaptive for different crack types, without involving time-consuming computations of crack orientation, length, and intensity. The proposed MorphLink-C is evaluated using realistic flexible pavement images collected by the Florida Department of Transportation (FDOT). Statistical hypothesis tests are conducted to analyze false positive and negative errors in crack/no-crack classification using an artificial neural network (ANN) classifier associated with feature subset selection methods. The results show that MorphLink-C improves crack-detection accuracy and reduces...

Application of Orthogonal Decomposition Approaches to Long-Term Monitoring of Infrastructure Systems

The long-range monitoring of civil infrastructure systems monitored with dense sensor arrays that are capable of generating voluminous amounts of data from continuous online monitoring requires the implementation of a proper data processing and archiving scheme to maximize the benefits of structural health monitoring operations. This paper focuses on the areas of data management, data quality control, and feature extraction of meaningful parameters to describe the response of large-scale infrastructure systems to ambient excitation in the context of structural health monitoring (SHM). Recordings from the monitoring system installed on the Vincent Thomas Bridge (VTB) in San Pedro, California form the database of the proposed data-management and archiving methodology. The data processing methodology for the VTB is based on the calculation of the sensor array acceleration covariance matrices for every hour of available data and the subsequent orthogonal decomposition of the covariance matrices. The dominant proper orthogonal modes of the bridge are determined, and their statistical variations over an extended observation period covering several months of continuous data are quantified and analyzed. The empirical probability density functions for the mean daily bridge accelerations are computed and used to compare the statistical variations in different periods of operation of the bridge (working days, weekends, holidays). It is shown that the computed statistical distributions of the bridge response can provide a quantitative baseline through which to facilitate the early detection of any anomalies indicative of a possible structural deterioration resulting from fatigue (service loads) or extreme loading events, i.e., earthquakes, artificial hazards, or other natural hazards.

Stochastic change detection in uncertain nonlinear systems using reduced-order models: system identification

The reliable detection of relatively small changes in the characteristics of monitored systems, which simultaneously involve nonlinear phenomena as well as uncertain parameters, is a challenging problem whose resolution is crucial to the development of practical structural health monitoring methodologies slated for use with complex physical systems. This paper reports the results of a comprehensive experimental study involving an adaptive nonlinear component (an actively controlled magnetorheological damper) that was used to investigate the representation and propagation of uncertainties in a probabilistic format that provides a convenient means for reliable detection of small changes in uncertain nonlinear systems. In experimental studies of the MR damper, the uncertainty of the system characteristics was precisely controlled with known input-current statistics. A total of 4000 tests were performed, and the MR damper was identified using the restoring force method with both orthogonal and non-orthogonal basis functions. The identification results show that the identified coefficients involving orthogonal basis functions have several desirable features that are ideal for condition assessment purposes when dealing with complex nonlinear systems whose underlying physics is not amenable to easy modeling: (1) no a priori knowledge of the systems characteristics is required; (2) the orthogonal coefficients are statistically unbiased with respect to model complexity; and (3) the distributions of the orthogonal coefficients can be reliably used to detect changes in uncertain nonlinear systems, even if a reduced-order model is used in the identification process.

Crack Recognition and Segmentation Using Morphological Image-Processing Techniques for Flexible Pavements

MorphLink-C is a novel image-processing algorithm to connect crack fragments that are a common problem in crack recognition applications. The algorithm consists of two subprocesses: (a) the grouping of fragments by using a morphological dilation transform and (b) the connection of fragments by using a morphological thinning transform. MorphLink-C can be used with various crack extraction methods to connect crack fragments in crack line paths and for complicated crack shapes, such as single cracks, branched cracks, block cracks, and alligator cracks. MorphLink-C also provides a simple but accurate way to estimate an averaged crack width that is important in measuring cracking severity. The proposed method was validated by using realistic road surface images in different pavement cracking conditions. The results of the statistical hypothesis test showed that the proposed method could improve crack detection accuracy with the proposed crack defragmentation algorithm.

Statistical Selection and Interpretation of Imagery Features for Computer Vision-Based Pavement Crack–Detection Systems

AbstractThis paper aims to explore the statistics of pavement cracks using computer-vision techniques. The knowledge discovered by mining the crack data can be used to avoid subjective crack featur...

A System Identification and Change Detection Methodology for Stochastic Nonlinear Dynamic Systems

In this paper a component‐level detection methodology for system identification and change detection is discussed. The methodology is based on non‐parametric, data‐driven, stochastic system identification classifications using statistical pattern recognition techniques. In order to validate the methodology discussed in this paper an experimental study was performed using a complex nonlinear magneto‐rheological (MR) damper. The results of this study show that the proposed methodology is very promising to detect interpret changes in critical structural components such as nonlinear springs joints as well as various types of dampers.

Some data-driven modeling approaches for detecting changes in nonlinear dampers

Various identification methods are compared for full-scale nonlinear viscous dampers, including a parametric approach using a simplified design model (SDM), the non-parametric Restoring Force Method (RFM), and the non-parametric Artificial Neural Network (ANN) approach. Advantages and disadvantages of each method are discussed for monitoring purposes. In the comparison, it is shown that the RFM is superior to other methods in regard to the following aspects: (1) no assumption is needed on the nature of the monitored systems; (2) the method is applicable to a wide range of nonlinear system types; (3) the same identification model can be used for the unknown system changes, including the change of system type as well as the change of system parameter values; and (4) physical interpretation of system changes are possible, using the identified values of the series expansion coefficients. A set of experiments was also conducted using magneto-rheological (MR) dampers to validate the feasibility of system change detection. For small changes in the magnetic field strength, the corresponding changes in the dynamic characteristics of the MR damper were detected, using the identified RFM coefficients.

Evaluation of Uniaxial Strain Transducer for Railroad Infrastructure

The uniaxial strain transducer (UAST) is a microelectromechanical system that features a high resolution, a high sampling rate, and absolute encoding. It needs no calibration and has no strain drift over time. Strains measured with a UAST were compared with those measured with a typical electrical resistance strain gauge. Some discrepancies between the two sets of measurements were observed in a bending test; however, there was a good correlation between the two measurements in tension and compression tests. Strains measured with the UAST under a cyclic load followed closely the haversine loading curves generated with a Materials Testing System testing machine. To obtain reasonably accurate strain measurements under moving trainloads, a 12-bit operational mode for the UAST with a resolution of 2.81 μ∈ at 64 Hz for a loading cycle is recommended. This research project seeks to evaluate the potential for the development of a prototype hybrid UAST, which would incorporate strain data reduction algorithms into a UAST for railroad application by using existing UAST technologies.