Shamim N. Pakzad

Health monitoring of civil infrastructures using wireless sensor networks

A Wireless Sensor Network (WSN) for Structural Health Monitoring (SHM) is designed, implemented, deployed and tested on the 4200 ft long main span and the south tower of the Golden Gate Bridge (GGB). Ambient structural vibrations are reliably measured at a low cost and without interfering with the operation of the bridge. Requirements that SHM imposes on WSN are identified and new solutions to meet these requirements are proposed and implemented. In the GGB deployment, 64 nodes are distributed over the main span and the tower, collecting ambient vibrations synchronously at 1 kHz rate, with less than 10 mus jitter, and with an accuracy of 30 muG. The sampled data is collected reliably over a 46-hop network, with a bandwidth of 441 B/s at the 46th hop. The collected data agrees with theoretical models and previous studies of the bridge. The deployment is the largest WSN for SHM.

Wireless sensor networks for structural health monitoring

Sukun Kim†, Shamim Pakzad‡, David Culler†, James Demmel† Gregory Fenves‡, Steve Glaser‡, Martin Turon? {binetude, culler, demmel}@eecs.berkeley.edu {shamimp, fenves, glaser}@ce.berkeley.edu mturon@xbow.com † Electrical Engineering and Computer Sciences and ‡ Civil and Environmental Engineering ? Crossbow Technology, Inc. University of California at Berkeley 4145 N. First Street Berkeley, CA 94720 San Jose, CA 95134

Statistical Analysis of Vibration Modes of a Suspension Bridge Using Spatially Dense Wireless Sensor Network

A spatially dense wireless sensor network was designed, developed and installed on a long-span suspension bridge for a 3-month deployment to record ambient acceleration. A total 174 sets of data (1.3 GB) were collected from 64 sensor nodes on the main span and south tower of the Golden Gate Bridge. Analysis of the vibration data using power spectral densities and peak picking provide approximate estimates of vibration modes with minimal computation. For more detailed analysis of the data, autoregressive with moving average models (ARMA) give parametric estimates of vibration modes for frequencies up to 5 Hz. Statistical analysis of the multiple realizations give the distributions of the vibration frequencies, damping ratios, and mode shapes and 95% confidence intervals. The statistical results are compared with vibration properties using the peak picking method and previous studies of the bridge using measured data and a finite-element model. Analysis of the ambient vibration data and system identification results demonstrate that high spatial and temporal sensing using the wireless sensor network give a high resolution and confidence in the identified vibration modes. The estimation errors for the identified vibration properties are generally low, with frequencies being the most accurate and damping ratios the least accurate.

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.

Localized Structural Damage Detection: A Change Point Analysis

Many current damage detection techniques rely on the skill and experience of a trained inspector and also require a priori knowledge about the struc- tures properties. However, this study presents adapta- tion of several change point analysis techniques for their performance in civil engineering damage detection. Lit- erature shows different statistical approaches which are developed for detection of changes in observations for different applications including structural damage detec- tion. However, despite their importance in damage de- tection, control charts and statistical frameworks are not properly utilized in this area. On the other hand, most of the existing change point analysis techniques were originally developed for applications in the stock mar- ket or industrial engineering processes; utilizing them in structural damage detection needs adjustments and ver- ification. Therefore, in this article several change point detection methods are evaluated and adjusted for a dam- age detection scheme. The effectiveness of features from a statistics based local damage detection algorithm called Influenced Coefficient Based Damage Detection Algo- rithm (IDDA) is expanded for a more complex structural system. The statistics used in this study include the uni- variate Cumulative Sum, Exponentially Weighted Mov- ing Average (EWMA), Mean Square Error (MSE), and multivariate Mahalanobis distances, and Fisher Crite- rion. They are used to make control charts that detect and localize the damage by correlating locations of a sen- sor network with the damage features. A Modified MSE statistic, called ModMSE statistic, is introduced to re- move the sensitivity of the MSE statistic to the variance of a data set. The effectiveness of each statistic is analyzed.

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...

Generalized Response Surface Model Updating Using Time Domain Data

AbstractIn finite-element (FE) model updating using response surface (RS) models as surrogate, the procedure of finding an appropriate design to build the RS models requires a number of trial-and-error approaches with different designs and subset models. To address this issue, a procedure is proposed in this paper to design and fit proper RS models in FE model updating problems. Also, formulation of the problem in an iterative format in time domain is proposed to extract more information from measured signals and compensate for the error present in the regressed models. This procedure is applicable to both linear and nonlinear models under static or dynamic analysis. The proposed methodology is applied to a numerical case study of a steel frame with global nonlinearity. Appropriate design and model order are successfully established and optimization in time performs well in all the simulated scenarios. Finally, the performance of this method in presence of measurement noise is compared with a method based...

STRIDE for Structural Identification Using Expectation Maximization: Iterative Output-Only Method for Modal Identification

AbstractThis paper introduces structural identification using expectation maximization (STRIDE), a novel application of the expectation maximization (EM) algorithm and approach for output-only modal identification. The EM algorithm can be used to estimate the maximum likelihood parameters of a state-space model. In this context, the state-space model represents the equation of motion for a linear dynamic system. STRIDE is an iterative procedure that uses Kalman filtering and Rauch-Tung-Striebel (RTS) smoothing equations to produce estimates of the unobserved states; these calculations are based on the observed data and prior estimates of the state-space parameters. With this information, the conditional likelihood of the model is maximized and the state-space parameters are updated at each iteration. Once an iteration meets user-prescribed convergence criterion, the algorithm ends—yielding maximum likelihood estimates (MLE) for the state-space model parameters. The modal properties of the structure are th...

Application of state-of-the-art in measurement and data analysis techniques for vibration evaluation of a tall building

Recent advancements in sensing and data acquisition technology have made monitoring of structures and infrastructure more affordable and, at the same time, more comprehensive. Examples of such advancements are application of wireless technology for communication, the utilisation of fully automated systems for long-term monitoring and the remote control of the sensing system over Internet. Although each of these technologies has been used in different structural health monitoring projects in the recent years, inclusion of an all-in-one sensing system represents the state-of-the-art in measurement techniques. This paper presents the integration of all of the above-mentioned advanced monitoring approaches in one sensing system for forensic quantification of an in-service tall building. The inclusive measurement and monitoring system along with advanced data analysis techniques enabled extraction of detailed information about dynamic characteristics of the building structure and development of reliable conclusions regarding its performance. It is shown that the performance of the investigated structural components is satisfactory in terms of strength demand. However, the level of vibration in some portions of the structure does not meet the limits of human comfort. In addition, wind-speed spectrum, acceleration response spectrum and the modes of lateral vibration are extracted to assist with evaluation of the structures performance.