Showing papers on "Structural health monitoring published in 2014"
TL;DR: The main challenges arising from the use of FBGs in composite materials are reviewed, with a focus on issues related to temperature-strain discrimination, demodulation of the amplitude spectrum during and after the curing process as well as connection between the embedded optical fibers and the surroundings.
Abstract: Nowadays, smart composite materials embed miniaturized sensors for structural health monitoring (SHM) in order to mitigate the risk of failure due to an overload or to unwanted inhomogeneity resulting from the fabrication process. Optical fiber sensors, and more particularly fiber Bragg grating (FBG) sensors, outperform traditional sensor technologies, as they are lightweight, small in size and offer convenient multiplexing capabilities with remote operation. They have thus been extensively associated to composite materials to study their behavior for further SHM purposes. This paper reviews the main challenges arising from the use of FBGs in composite materials. The focus will be made on issues related to temperature-strain discrimination, demodulation of the amplitude spectrum during and after the curing process as well as connection between the embedded optical fibers and the surroundings. The main strategies developed in each of these three topics will be summarized and compared, demonstrating the large progress that has been made in this field in the past few years.
380 citations
TL;DR: This review article is devoted to presenting a summary of the basic principles of various optical fiber sensors, innovation in sensing and computational methodologies, development of novel optical Fiber sensors, and the practical application status of the optical fiber sensing technology in structural health monitoring (SHM) of civil infrastructure.
Abstract: In the last two decades, a significant number of innovative sensing systems based on optical fiber sensors have been exploited in the engineering community due to their inherent distinctive advantages such as small size, light weight, immunity to electromagnetic interference (EMI) and corrosion, and embedding capability. A lot of optical fiber sensor-based monitoring systems have been developed for continuous measurement and real-time assessment of diversified engineering structures such as bridges, buildings, tunnels, pipelines, wind turbines, railway infrastructure, and geotechnical structures. The purpose of this review article is devoted to presenting a summary of the basic principles of various optical fiber sensors, innovation in sensing and computational methodologies, development of novel optical fiber sensors, and the practical application status of the optical fiber sensing technology in structural health monitoring (SHM) of civil infrastructure.
209 citations
TL;DR: An experimental study of the effect of temperature on the electrical impedance of the piezoelectric sensors used in the EMI technique showed that the temperature effects were strongly frequency-dependent, which may motivate future research in the SHM field.
Abstract: The electromechanical impedance (EMI) technique is considered to be one of the most promising methods for developing structural health monitoring (SHM) systems. This technique is simple to implement and uses small and inexpensive piezoelectric sensors. However, practical problems have hindered its application to real-world structures, and temperature effects have been cited in the literature as critical problems. In this paper, we present an experimental study of the effect of temperature on the electrical impedance of the piezoelectric sensors used in the EMI technique. We used 5H PZT (lead zirconate titanate) ceramic sensors, which are commonly used in the EMI technique. The experimental results showed that the temperature effects were strongly frequency-dependent, which may motivate future research in the SHM field.
200 citations
TL;DR: In this article, a non-contact dynamic displacement measurement system for railway bridges based on video technology is presented, consisting of a high speed video camera, an optical lens, lighting lamps and a precision target, which can perform measurements with acquisition frame rates ranging from 64 fps to 500 fps.
197 citations
TL;DR: In this article, a modified delay-and-sum algorithm is proposed for detecting impact damage in composite plates with and without a stiffener, which is shown to capture and localize damage with only four transducers.
Abstract: Piezoelectric sensors are increasingly being used in active structural health monitoring, due to their durability, light weight and low power consumption. In the present work damage detection and characterization methodologies based on Lamb waves have been evaluated for aircraft panels. The applicability of various proposed delay-and-sum algorithms on isotropic and composite stiffened panels have been investigated, both numerically and experimentally. A numerical model for ultrasonic wave propagation in composite laminates is proposed and compared to signals recorded from experiments. A modified delay-and-sum algorithm is then proposed for detecting impact damage in composite plates with and without a stiffener which is shown to capture and localize damage with only four transducers.
190 citations
TL;DR: This paper proposes a cyber-physical codesign approach to structural health monitoring based on wireless sensor networks that closely integrates flexibility- based damage localization methods that allow a tradeoff between the number of sensors and the resolution of damage localization, and an energy-efficient, multilevel computing architecture specifically designed to leverage the multiresolution feature of the flexibility-based approach.
Abstract: Our deteriorating civil infrastructure faces the critical challenge of long-term structural health monitoring for damage detection and localization. In contrast to existing research that often separates the designs of wireless sensor networks and structural engineering algorithms, this paper proposes a cyber-physical codesign approach to structural health monitoring based on wireless sensor networks. Our approach closely integrates 1) flexibility-based damage localization methods that allow a tradeoff between the number of sensors and the resolution of damage localization, and 2) an energy-efficient, multilevel computing architecture specifically designed to leverage the multiresolution feature of the flexibility-based approach. The proposed approach has been implemented on the Intel Imote2 platform. Experiments on a simulated truss structure and a real full-scale truss structure demonstrate the system's efficacy in damage localization and energy efficiency.
189 citations
TL;DR: In this paper, structural health monitoring relies on the repeated observation of damage-sensitive features such as strains or natural frequencies, and the major problem is that regular changes in temperature, relative...
Abstract: Structural health monitoring relies on the repeated observation of damage-sensitive features such as strains or natural frequencies. A major problem is that regular changes in temperature, relative...
183 citations
TL;DR: In this article, a novel, multi-modal, nanomaterial based sensor technology that can provide wide area detection of damage was used for structural health monitoring (SHM), which seeks to provide ongoing monitoring of a structure's integrity.
181 citations
TL;DR: It is demonstrated how structural reliability methods can be used to effectively model the VoI and an efficient algorithm for its computation is proposed and demonstrated by an illustrative application to monitoring of a structural system subjected to fatigue deterioration.
150 citations
TL;DR: In this article, a hybrid approach for characterizing fatigue damage was developed, using two genres of damage indices constructed based on the linear and the nonlinear features of acousto-ultrasonic waves.
147 citations
TL;DR: In this article, machine learning algorithms based on Artificial Neural Networks (ANNs) including an Auto-Associative Neural Network (AANN) based on a standard ANN form and a novel approach to auto-association with Radial Basis Functions (RBFs) networks are used, which are optimised for fast and efficient runs.
TL;DR: In this article, a prismatic sensors made of cement paste doped with carbon nanotubes have been proposed as embedded sensors for concrete structures for structural health monitoring of concrete structures.
TL;DR: In this article, an alternative method for the monitoring of bridge dynamic behaviour is proposed, with accelerometers fitted to the axles of the trailer of a truck-trailer vehicle.
Abstract: Bridge structures are continuously subject to degradation due to the environment, ageing and excess loading. Periodic monitoring of bridges is therefore a key part of any maintenance strategy as it can give early warning if a bridge becomes unsafe. This article investigates an alternative method for the monitoring of bridge dynamic behaviour: a truck–trailer vehicle system, with accelerometers fitted to the axles of the trailer. The method aims to detect changes in the damping of a bridge, which may indicate the existence of damage. A simplified vehicle–bridge interaction model is used in theoretical simulations to assess the effectiveness of the method in detecting those changes. The influence of road profile roughness on the vehicle vibration is overcome by recording accelerations from both axles of a trailer and then analysing the spectra of the difference in the accelerations between the two axles. The effectiveness of the approach in detecting damage simulated as a loss in stiffness is also investiga...
TL;DR: A rational framework for assessment of the impact of the SHM on decision‐making is researched and proposed and demonstrated on the case study of the Streicker Bridge, a new pedestrian bridge on Princeton University campus.
Abstract: Structural health monitoring (SHM) is a process aimed at providing accurate and in-time information concerning structural health condition and performance, which can serve as an objective basis for decision-making regarding operation, maintenance, and repair. However, at the current state of practice, SHM is less used on real structures, and one reason for this is the lack of understanding of the Value of Information obtained from SHM. Consequently, even when SHM is implemented, bridge managers often make decisions based on experience or common sense, frequently considering with reserve and sometimes disregarding the suggestions arising from SHM. Managers weigh the SHM results based on their prior perception of the state of the structure and the confidence that they have in the specific applied SHM system and then make decisions considering the perceived effects of the actions they can undertake. In order to address and overcome the aforementioned identified limitations in the use of the SHM, a rational framework for assessment of the impact of the SHM on decision-making is researched and proposed in this paper. The framework is based on the concept of Value of Information and demonstrated on the case study of the Streicker Bridge, a new pedestrian bridge on Princeton University campus.
TL;DR: In this paper, the authors presented a full-scale bridge benchmark problem organized by the Center of Structural Monitoring and Control at the Harbin Institute of Technology, where two critical and vulnerable components of cable-stayed bridges were evaluated.
Abstract: A structural health monitoring (SHM) system provides an efficient way to diagnose the condition of critical and large-scale structures such as long-span bridges. With the development of SHM techniques, numerous condition assessment and damage diagnosis methods have been developed to monitor the evolution of deterioration and long-term structural performance of such structures, as well as to conduct rapid damage and post-disaster assessments. However, the condition assessment and the damage detection methods described in the literature are usually validated by numerical simulation and/or laboratory testing of small-scale structures with assumed deterioration models and artificial damage, which makes the comparison of different methods invalid and unconvincing to a certain extent. This paper presents a full-scale bridge benchmark problem organized by the Center of Structural Monitoring and Control at the Harbin Institute of Technology. The benchmark bridge structure, the SHM system, the finite element model of the bridge, and the monitored data are presented in detail. Focusing on two critical and vulnerable components of cable-stayed bridges, two benchmark problems are proposed on the basis of the field monitoring data from the full-scale bridge, that is, condition assessment of stay cables (Benchmark Problem 1) and damage detection of bridge girders (Benchmark Problem 2). For Benchmark Problem 1, the monitored cable stresses and the fatigue properties of the deteriorated steel wires and cables are presented. The fatigue life prediction model and the residual fatigue life assessment of the cables are the foci of this problem. For Benchmark Problem 2, several damage patterns were observed for the cable-stayed bridge. The acceleration time histories, together with the environmental conditions during the damage development process of the bridge, are provided. Researchers are encouraged to detect and to localize the damage and the damage development process. All the datasets and detailed descriptions, including the cable stresses, the acceleration datasets, and the finite element model, are available on the Structural Monitoring and Control website (http://smc.hit.edu.cn). Copyright © 2013 John Wiley & Sons, Ltd.
TL;DR: In this paper, a neural network-based approach for structural damage detection using frequency response function (FRF) data is presented, which reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for actual damage localization and quantification using recognized damage patterns from the algorithm.
TL;DR: In this article, two subspace-based methods are considered that take these statistical uncertainties into account, first modal parameter and their confidence interval estimation for a direct comparison of the structural states, and second a statistical null space based damage detection test that completely avoids the identification step.
TL;DR: In this article, the authors present and discuss the approach and the first results of a long-term dynamic monitoring campaign on an offshore wind turbine in the Belgian North Sea, focusing on the vibration levels and modal parameters of the fundamental modes of the support structure.
Abstract: This article will present and discuss the approach and the first results of a long-term dynamic monitoring campaign on an offshore wind turbine in the Belgian North Sea. It focuses on the vibration levels and modal parameters of the fundamental modes of the support structure. These parameters are crucial to minimize the operation and maintenance costs and to extend the lifetime of offshore wind turbine structure and mechanical systems. In order to perform a proper continuous monitoring during operation, a fast and reliable solution, applicable on an industrial scale, has been developed. It will be shown that the use of appropriate vibration measurement equipment together with state-of-the art operational modal analysis techniques can provide accurate estimates of natural frequencies, damping ratios, and mode shapes of offshore wind turbines. The identification methods have been automated and their reliability has been improved, so that the system can track small changes in the dynamic behavior of offshore...
TL;DR: In this article, the authors presented predictive modeling of nonlinear guided wave propagation for structural health monitoring using both finite element method and analytical approach, where the nonlinearity of the guided waves is generated by interaction with a nonlinear breathing crack.
Abstract: This article presents predictive modeling of nonlinear guided wave propagation for structural health monitoring using both finite element method and analytical approach. In our study, the nonlinearity of the guided waves is generated by interaction with a nonlinear breathing crack. Two nonlinear finite element method techniques are used to simulate the breathing crack: (a) element activation/deactivation method and (b) contact analysis. Both techniques are available in ANSYS software package. The solutions obtained by these two finite element method techniques compare quite well. A parametric analytical predictive model is built to simulate guided waves interacting with linear/nonlinear structural damage. This model is coded into MATLAB, and the WaveFormRevealer graphical user interface is developed to obtain fast predictive waveform solutions for arbitrary combinations of sensor, structural properties, and damage. The predictive model is found capable of describing the nonlinear wave propagation phenomen...
TL;DR: In this article, two novel solutions for strain sensing using nanocomposite materials were developed for the monitoring of local information on large-scale structures, which are different in their applications and physical principles.
Abstract: The authors have recently developed two novel solutions for strain sensing using nanocomposite materials. While they both aim at providing cost-effective solutions for the monitoring of local information on large-scale structures, the technologies are different in their applications and physical principles. One sensor is made of a cementitious material, which could make it suitable for embedding within the core of concrete structures prior to casting, and is a resistor, consisting of a carbon nanotube cement-based transducer. The other sensor can be used to create an external sensing skin and is a capacitor, consisting of a flexible conducting elastomer fabricated from a nanocomposite mix, and deployable in a network setup to cover large structural surfaces. In this paper, we advance the understanding of nanocomposite sensing technologies by investigating the potential of both novel sensors for the dynamic monitoring of civil structures. First, an in-depth dynamic characterization of the sensors using a uniaxial test machine is conducted. Second, their performance at dynamic monitoring of a full-scale concrete beam is assessed, and compared against off-the-shelf accelerometers. Experimental results show that both novel technologies compare well against mature sensors at vibration-based structural health monitoring, showing the promise of nanocomposite technologies for the monitoring of large-scale structural systems.
TL;DR: The numerical and experimental studies verify that the Electromechanical Impedance method can accurately predict changes in the amount of damage in reinforced concrete slabs.
Abstract: Piezoelectric lead zirconate titanate (PZT) is being gradually applied into practice as a new intelligent material for structural health monitoring. In order to study the damage detection properties of PZT on concrete slabs, simply supported reinforced concrete slabs with piezoelectric patches attached to their surfaces were chosen as the research objects and the Electromechanical Impedance method (EMI) was adopted for research. Five kinds of damage condition were designed to test the impedance values at different frequency bands. Consistent rules are found by calculation and analysis. Both the root mean square deviation (RMSD) and the correlation coefficient deviation (CCD) damage indices are capable of detecting the structural damage. The newly proposed damage index Ry/Rx can also predict the changes well. The numerical and experimental studies verify that the Electromechanical Impedance method can accurately predict changes in the amount of damage in reinforced concrete slabs. The damage index changes regularly with the distance of damages to the sensor. This relationship can be used to determine the damage location. The newly proposed damage index Ry/Rx is accurate in determining the damage location.
TL;DR: In this article, the authors carried out an extensive review of full-scale structural testing of wind turbine blades, including static testing and fatigue testing, and the current status in China is presented in order to discover the pros and cons of these techniques.
Abstract: The blades that play a key role to collect wind energy are the most critical components of a wind turbine system Meanwhile, they are also the parts most susceptible to damage Structural health monitoring (SHM) system has been proposed to continuously monitor the wind turbine Nevertheless, no system has yet been developed to a stage compatible with the requirements of commercial wind turbines Therefore, full-scale structural testing is the main means available so far for validating the comprehensive performance of wind turbine blades It is now normally used as part of a blade certification process It also allows an insight into the failure mechanisms of wind turbine blades, which are essential to the success of SHM Furthermore, it provides a unique opportunity to exercise SHM and non-destructive testing (NDT) techniques Recognizing these practical significances, this paper therefore aims to carry out an extensive review of full-scale structural testing of wind turbine blades, including static testing and fatigue testing In particular, the current status in China is presented One focus of this review is on the failure mechanisms of wind turbine blades, which are vital for optimizing the design of themselves as well as the design of their SHM system Another focus is on the strengths and weaknesses of various SHM and NDT techniques, which are useful for evaluating their applicability on wind turbine blades In addition, recent advances in photogrammetry and digital image correlation have allowed new opportunities for blade monitoring These techniques are currently being explored on a few wind turbine blade applications and can provide a wealth of additional information that was previously unobtainable These works are also summarized in this paper in order to discover the pros and cons of these techniques
TL;DR: In this paper, a Bayesian compressive sensing (BCS) method is investigated that uses sparse Bayesian learning to reconstruct signals from a compressive sensor, which can achieve perfect loss-less compression performance with quite high compression ratio.
Abstract: In structural health monitoring (SHM) systems
for civil structures, massive amounts of data are often
generated that need data compression techniques to
reduce the cost of signal transfer and storage, meanwhile
offering a simple sensing system. Compressive sensing
(CS) is a novel data acquisition method whereby the
compression is done in a sensor simultaneously with
the sampling. If the original sensed signal is sufficiently
sparse in terms of some orthogonal basis (e.g., a sufficient
number of wavelet coefficients are zero or negligibly
small), the decompression can be done essentially
perfectly up to some critical compression ratio; otherwise
there is a trade-off between the reconstruction error
and how much compression occurs. In this article,
a Bayesian compressive sensing (BCS) method is investigated
that uses sparse Bayesian learning to reconstruct
signals from a compressive sensor. By explicitly quantifying
the uncertainty in the reconstructed signal from
compressed data, the BCS technique exhibits an obvious
benefit over existing regularized norm-minimization
CS methods that provide a single signal estimate. However,
current BCS algorithms suffer from a robustness
problem: sometimes the reconstruction errors are very
large when the number of measurements K are a lot
less than the number of signal degrees of freedom N that are needed to capture the signal accurately in a directly
sampled form. In this article, we present improvements
to the BCS reconstruction method to enhance its
robustness so that even higher compression ratios N/K
can be used and we examine the trade-off between efficiently
compressing data and accurately decompressing
it. Synthetic data and actual acceleration data collected
from a bridge SHM system are used as examples.
Compared with the state-of-the-art BCS reconstruction
algorithms, the improved BCS algorithm demonstrates
superior performance. With the same acceptable error
rate based on a specified threshold of reconstruction error,
the proposed BCS algorithm works with relatively
large compression ratios and it can achieve perfect loss-less
compression performance with quite high compression
ratios. Furthermore, the error bars for the signal reconstruction
are also quantified effectively.
TL;DR: In this article, a regression-based methodology is proposed to generate numerical models, which capture the relationships between temperature distributions and structural response, from distributed measurements collected during a reference period, and compared the performance of various regression algorithms such as multiple linear regression (MLR), robust regression (RR), and support vector regression (SVR) for application within the proposed methodology.
TL;DR: In this paper, the authors investigated the role of ambient temperature in causing changes to the structural wave propagation, as sensed by piezoelectric transducers, in a newer perspective.
Abstract: This article investigates the role of ambient temperature in causing changes to the structural wave propagation, as sensed by piezoelectric transducers, in a newer perspective. A novel approach is proposed to compensate the influence of temperature on piezo-sensor response using both analytical models and numerical simulations. Parametric studies using numerical simulations for plates with surface-mounted piezoelectric transducers establish linear functional relationship between change in sensor signals and specific combination of material properties, within certain temperature range. A numerical temperature compensation model is developed based on this functional relationship to reconstruct piezo-sensor signals at elevated temperatures. Matching pursuit–based signal analysis and reconstruction schemes are used in this study. Practical efficacy of the compensation model is tested for metallic structures with both simple and complex geometries. Model-based reconstruction of first wave packets in the sensor...
25 Mar 2014
TL;DR: In this paper, an updated review on innovations and applications in structural health monitoring (SHM) for infrastructures carried out by researchers at Dalian University of Technology has been presented.
Abstract: The developments and implementations of the structural health monitoring (SHM) system for large infrastructures have been gradually recognized by researchers, engineers and administrative authorities in the last decades. This paper summarizes an updated review on innovations and applications in SHM for infrastructures carried out by researchers at Dalian University of Technology. Invented sensors and data acquisition system are firstly briefly described. And then, some proposed theories and methods including the sensing technology, sensor placement method, signal processing and data fusion, system identification and damage detection are discussed in details. Following those, the activities on the standardization of SHM and several case applications on specific types of structure are reviewed. Finally, existing problems and promising research efforts in the field of SHM are given.
TL;DR: Wang et al. as mentioned in this paper proposed an online updating Gaussian Mixture Model (GMM)-based damage evaluation method to improve damage evaluation reliability under time-varying conditions, where Lambwave-signal variation indexes and principle component analysis (PCA) are adopted to obtain the signal features.
Abstract: Structural health monitoring technology for aerospace structures has gradually turned from fundamental research to practical implementations. However, real aerospace structures work under time-varying conditions that introduce uncertainties to signal features that are extracted from sensor signals, giving rise to difficulty in reliably evaluating the damage. This paper proposes an online updating Gaussian Mixture Model (GMM)-based damage evaluation method to improve damage evaluation reliability under time-varying conditions. In this method, Lambwave-signal variation indexes and principle component analysis (PCA) are adopted to obtain the signal features. A baseline GMM is constructed on the signal features acquired under timevarying conditions when the structure is in a healthy state. By adopting the online updating mechanism based on a moving feature sample set and inner probability structural reconstruction, the probability structures of the GMM can be updated over time with new monitoring signal features to track the damage progress online continuously under time-varying conditions. This method can be implemented without any physical model of damage or structure. A real aircraft wing spar, which is an important load-bearing structure of an aircraft, is adopted to validate the proposed method. The validation results show that the method is effective for edge crack growth monitoring of the wing spar bolts holes under the time-varying changes in the tightness degree of the bolts.
TL;DR: In this paper, the authors review previous studies on the development and application of fiber optic sensors, and various analysis methods were transferred to required parameters such as displacement, force and pressure which can more directly reflect the safety of geotechnical structures under complex engineering stress condition.
TL;DR: The methodology of sensor placement optimization for SHM is studied that addresses three key aspects: finding a high quality placement of a set of sensors that satisfies civil engineering requirements; ensuring the communication efficiency and low complexity for sensor placement; and reducing the probability of a network failure.
Abstract: Structural health monitoring (SHM) refers to the process of implementing a damage detection and characterization strategy for engineering structures. Its objective is to monitor the integrity of structures and detect and pinpoint the locations of possible damages. Although wired network systems still dominate in SHM applications, it is commonly believed that wireless sensor network (WSN) systems will be deployed for SHM in the near future, due to their intrinsic advantages. However, the constraints (e.g., communication, fault tolerance, energy) of WSNs must be considered before their deployment on structures. In this article, we study the methodology of sensor placement optimization for WSN-based SHM. Sensor placement plays a vital role in SHM applications, where sensor nodes are placed on critical locations that are of civil/structural engineering importance. We design a three-phase sensor placement approach, named TPSP, aiming to achieve the following objectives: finding a high-quality placement for a given set of sensors that satisfies the engineering requirements, ensuring communication efficiency and reliability and low placement complexity, and reducing the probability of failures in a WSN. Along with the sensor placement, we enable sensor nodes to develop “connectivity trees” in such a way that maintaining structural health state and network connectivity, for example, in case of a sensor fault, can be done in a distributed manner. The trees are constructed once (unlike dynamic clusters or trees) and do not incur additional communication costs for the WSN. We optimize the performance of TPSP by considering multiple objectives: low communication cost, fault tolerance, and lifetime prolongation. We validate the effectiveness and performance of TPSP through both simulations using real datasets and a proof-of-concept system on a physical structure.
TL;DR: In this paper, a framework for determining the optimum number and location of sensors to establish an effective structural health monitoring (SHM) system is proposed, which reduces the installation and operational cost, simplifies the computational processes for a SHM system, and ensures an accurate estimation of monitoring parameters.
Abstract: 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.