Minwoo Chang

Optimal Sensor Placement for Modal Identification of Bridge Systems Considering Number of Sensing Nodes

A series of optimal sensor placement (OSP) techniques is discussed in this paper. A framework for deciding the optimum number and location of sensors is proposed, to establish an effective structural health monitoring (SHM) system. The vibration response from an optimized sensor network reduces the installation and operational cost, simplifies the computational processes for a SHM system, and ensures an accurate estimation of monitoring parameters. In particular, the proposed framework focuses on the determination of the number of sensors and their proper locations to estimate modal properties of bridge systems. The relative importance of sensing locations in terms of signal strength was obtained by applying several OSP techniques, which include effective influence (EI), EI-driving point residue (EI-DPR), and kinetic energy (KE) methods. Additionally, the modified variance (MV) method, based on the principal component analysis (PCA), was developed with the assumption of independence in modal ordinates at each sensing location. Modal assurance criterion (MAC) between the target and interpolated mode shapes from an optimal sensor set was utilized as an effective measure to determine the number of sensors. The proposed framework is verified by three examples: (1) a numerically simulated simply supported beam, (2) finite-element (FE) model of the Northampton Street Bridge (NSB), and (3) modal information identified using a set of wireless sensor data from the Golden Gate Bridge (GGB). These three examples demonstrate the application and efficiency of the proposed framework to optimize the number of sensors and verify the performance of the MV method compared to the EI, EI-DPR, and KE methods.

Observer Kalman Filter Identification for Output-Only Systems Using Interactive Structural Modal Identification Toolsuite

AbstractSeveral modal identification techniques have been developed in the past few decades, and their use is rapidly expanding due to new focus on the instrumentation of major structures. This paper focuses on the expansion of the eigenvalue realization algorithm (ERA)–observer Kalman filter identification (OKID) to identify modal parameters of output-only systems (OO) by splitting the state-space model into deterministic and stochastic subsystems (ERA-OKID-OO). The performance is then compared with other output-only identification methods in terms of the level of accuracy and efficiency. A newly developed software package [Structural Modal Identification Toolsuite (SMIT)] is used to provide a uniform and convenient way of utilizing several system identification (SID) methods, including variations of ERA, auto-regressive with exogenous terms (ARX) models, system realization using information matrix (SRIM), and numerical algorithms for subspace state space system identification (N4SID). The main purpose o...

Modified Natural Excitation Technique for Stochastic Modal Identification

AbstractThis paper presents an improvement to the eigensystem realization algorithm (ERA) with natural excitation technique (NExT), which is called the ERA-NExT-AVG method. The method uses a coded average of row vectors in each Markov parameter for evaluating modal properties of a structure. The modification is important because, for the existing stochastic system identification methods, the state-space model, obtained from output sensor data, is usually overparameterized resulting in large systems. Solving such a problem can be computationally very intensive especially in the applications when using the computational capabilities of embedded sensor networks. As a way to improve the efficiency of the ERA-NExT method, the proposed method focuses on the number of components in a single Markov parameter, which can theoretically be minimized down to the number of structural modes. Applying the coded average column vectors as Markov parameters to the ERA, the computational cost of the algorithm is significantl...

Local Damage Detection in Beam-Column Connections Using a Dense Sensor Ne twork

Damage prognosis for structural health monitoring is a challenging and complex research topic in civil engineering. Critical components of damage detection are identifying the location and severity of damage in a structure, as well as its global effect on the structure. Local damage can increase over time and have additional adverse effects on the entire structure. Traditional damage detection methods using sensor data are effective in recognizing the change in global properties of a structure. However, these methods are neither effective nor sensitive in identifying local damage. The use of dense clustered sensor networks provides promising applications in analysis of structural components and identifying local damage. In this study, a prototype beam-column connection was constructed and instrumented by a dense sensor network. The column ends of the test specimen have fixed connections, and the beam cantilevers from the centerline of the column. The beam was excited with an actuator at its free end, and accelerometer sensors measured the response of the members to dynamic excitations at several locations along the specimen. The response at each sensor location was compared to that of other locations and pairwise influence coefficients were estimated. Damage is introduced to the system by replacing a portion of the beam element with a smaller section, and thus reducing its stiffness. New influence coefficients were calculated and compared to the undamaged values. By statistically comparing the change in influence coefficients, the damage is accurately and effectively identified.

Optimal sensor configuration for flexible structures with multi-dimensional mode shapes

A framework for deciding the optimal sensor configuration is implemented for civil structures with multi-dimensional mode shapes, which enhances the applicability of structural health monitoring for existing structures. Optimal sensor placement (OSP) algorithms are used to determine the best sensor configuration for structures with a priori knowledge of modal information. The signal strength at each node is evaluated by effective independence and modified variance methods. Euclidean norm of signal strength indices associated with each node is used to expand OSP applicability into flexible structures. The number of sensors for each method is determined using the threshold for modal assurance criterion (MAC) between estimated (from a set of observations) and target mode shapes. Kriging is utilized to infer the modal estimates for unobserved locations with a weighted sum of known neighbors. A Kriging model can be expressed as a sum of linear regression and random error which is assumed as the realization of a stochastic process. This study presents the effects of Kriging parameters for the accurate estimation of mode shapes and the minimum number of sensors. The feasible ranges to satisfy MAC criteria are investigated and used to suggest the adequate searching bounds for associated parameters. The finite element model of a tall building is used to demonstrate the application of optimal sensor configuration. The dynamic modes of flexible structure at centroid are appropriately interpreted into the outermost sensor locations when OSP methods are implemented. Kriging is successfully used to interpolate the mode shapes from a set of sensors and to monitor structures associated with multi-dimensional mode shapes.

Validation of a wireless sensor network using local damage detection algorithm for beam-column connections

There has been a rapid advancement in wireless sensor network (WSN) technology in the past decade and its application in structural monitoring has been the focus of several research projects. The evaluation of the newly developed hardware platform and software system is an important aspect of such research efforts. Although much of this evaluation is done in the laboratories and using generic signal processing techniques, it is important to validate the system for its intended application as well. In this paper the performance of a newly developed accelerometer sensor board is evaluated by using the data from a beam-column connection specimen with a local damage detection algorithm. The sensor board is a part of a wireless node that consists of the Imote2 control/communication unit and an advanced antenna for improved connectivity. A scaled specimen of a steel beam-column connection is constructed in ATLSS center at Lehigh University and densely instrumented by synchronized networked systems of both traditional piezoelectric and wireless sensors. The column ends of the test specimen have fixed connections, and the beam cantilevers from the centerline of the column. The specimen is subjected to harmonic excitations in several test runs and its acceleration response is collected by both systems. The collected data is then used to estimate two sets of system influence coefficients with the wired one as the reference baseline. The performance of the WSN is evaluated by comparing the quality of the influence coefficients and the rate of convergence of the estimated parameters.

Are Today’s SHM Procedures Suitable for Tomorrow’s BIGDATA?

Large SHM datasets often result from special applications such as long-term monitoring, dense sensor arrays, or high sampling rates. Through the development of novel sensing techniques as well as advances in sensing devices and data acquisition technology, it is expected that such large volumes of measurement data become commonplace. In anticipation of datasets magnitudes larger than today’s, it is important to evaluate current SHM processing methods at BIGDATA standards and identify potential limitations within computational procedures. This paper will focus on the processing of large SHM datasets and the computational sensitivity of common SHM procedures. Processing concerns encompass efficiency and scalability of SHM software, particularly the computational sensitivity of common system identification and damage detection algorithms with respect to a large amount of sensors and samples.

Modal Identification Using SMIT

The objective of this paper is to introduce a Structural Modal Identification Toolsuite (SMIT) for MATLAB that has been recently developed to facilitate system identification of structural systems. SMIT is an integrated toolbox, supporting a user-friendly Graphical User Interface (GUI) for modal identification. In this paper, the results of several system identification methods are compared in terms of accuracy and efficiency. The toolbox is capable of performing several common system identification methods with a standardized process, which is composed of input, eigenvalue estimation, and post processing procedures. The toolbox can present the estimated modal parameters graphically, and conveniently store and recall the identification results. The implemented identification methods consist of several classes of system identification algorithms, including Eigensystem Realization Algorithm (ERA), Auto-Regressive Moving-Average method with eXogenous terms (ARMAX), and Stochastic Subspace Identification (SSI). The performance of SMIT was verified by identifying the modal parameters of Northampton Steel Bridge (NSB), using five identification algorithms. A set of ambient acceleration responses was collected using a Wireless Sensor Network (WSN), and a subset of the sensing nodes was selected to identify vertical and torsional modes of NSB. The comparison of identification results to examine the accuracy and efficiency of each method supports that the SMIT is applicable to identify civil infrastructures effectively.

Modal Parameter Uncertainty Quantification Using PCR Implementation with SMIT

The System Identification (SID) techniques for output-only systems, combined with the use of Wireless Sensor Network (WSN), provide many opportunities to monitor large scale civil infrastructure. Recently, research associated with uncertainties in the measurement data has been conducted to quantify the level of noise and to improve the performance of SID methods. This paper presents the effect of measurement noise when the data is used in Eigensystem Realization Algorithm (ERA) based methods including Observer Kalman filter Identification (OKID), Natural Excitation Technique (NExT), and NExT using average scheme (NExT-AVG). Each algorithm estimates impulse response for ERA algorithm differently, which results in different noise level in terms of Physical Contribution Ratio (PCR) and affects the accuracy of identification results. In order to compare the effect of noise from each SID methods, modal parameters are estimated using the numerically simulated response from simply supported beam model and wireless sensor data from Golden Gate Bridge (GGB). All identification procedures are supported by Structural Modal Identification Toolsuite (SMIT) which provides a convenient environment to access various SID methods.

A Parameter Optimization for Mode Shapes Estimation Using Kriging Interpolation

A parametric study of Kriging interpolation for Optimal Sensor Placement (OSP) is presented in this paper. A Kriging model uses geostatistical information to interpolate and extrapolate the values for unobserved locations with a weighted sum of known neighbors. The accuracy of mode shape estimates is evaluated by Modal Assurance Criteria (MAC), compared to the target mode shapes. The performance of OSP is enhanced by the Kriging results. For the quality estimation of mode shape, a parametric study is conducted in this paper. The Kriging model is composed of linear regression model with random error which is assumed as a realization of a stochastic process. A Gaussian function is used to characterize the covariance function between two random errors in terms of their relative distance. Three parameters are involved to define covariance function: regression model order and two amplification parameters. The parameter optimization approach aims at OSP solution with the minimum number of sensors. The effect of parameters is evaluated using numerically simulated harmonic modes, and modes from Northampton Street Bridge (NSB). Modified Variance (MV) is used to rank the signal strength at candidate sensing locations. The results show that the accuracy of estimated mode shapes is dependent on the eigenvalue of covariance matrix and the number of sensors can be minimized when the Kriging model is optimally designed.