Automated Modal Analysis for Tracking Structural Change during Construction and Operation Phases

Teng, Jun; Tang, De-Hui; Zhang, Xiao; Hu, Weihua; Said, Samir; Rohrmann, R.

doi:10.3390/s19040927

Cited by 21 publications

(11 citation statements)

References 27 publications

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“…Before the bridge was opened in July 2017, a modal experiment was performed and a finite element method (FEM) was updated. 20 The vibration modes of the steel arch bridge can be divided into two categories: the modes dominated by arch vibration (arch mode) and those dominated by beam vibration (beam mode); however, the latter is also coupled with slight vibration of the arch. Table 1 lists the baseline modal parameters calculated by the FEM and identified by the stochastic subspace identification (SSI) method.…”

Section: Modal Experiments and Automated Modal Identification Of Bridgementioning

confidence: 99%

“…The spurious modes are effectively eliminated by an improved density-based spatial clustering of applications with noise method (DBSCAN). A detailed discussion of the improved automated operational modal tracking algorithm and the variation in modal parameters of the arch bridge are illustrated in Teng et al 20…”

Section: Modal Experiments and Automated Modal Identification Of Bridgementioning

confidence: 99%

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Mechanism of the effect of temperature on frequency based on long-term monitoring of an arch bridge

Teng

Tang

et al. 2020

Structural Health Monitoring

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In this study, the mechanism of the effect of temperature on structural frequency is investigated by integrating correlation analysis, numerical simulation, and neural network techniques. First, the different spatial–temporal influence patterns of the temperature field on frequency are observed using correlation analysis based on the long-term monitoring of an arch bridge. Subsequently, a numerical simulation is performed to quantitatively analyze the effect of temperature on frequency in terms of internal force, geometric size, elastic modulus, and boundary condition. It is observed that an unknown factor—except for the elastic modulus—leads to variation in the frequency of the beam mode. The spatial effect of the temperature field is separated by a genetic-algorithm-optimized backpropagation neural network. It is inferred that the frequency of the beam mode is influenced in terms of the boundary condition and the elastic modulus because of temperature, whereas the frequency of the arch mode is only influenced by the changing elastic modulus.

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Section: Modal Experiments and Automated Modal Identification Of Bridgementioning

confidence: 99%

Section: Modal Experiments and Automated Modal Identification Of Bridgementioning

confidence: 99%

Mechanism of the effect of temperature on frequency based on long-term monitoring of an arch bridge

Teng

Tang

et al. 2020

Structural Health Monitoring

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“…The natural vibration characteristics of bridges, including frequency, vibration mode, and damping, are affected by structural stiffness and the extent of damage, which can provide a reference basis for bridge design and comprehensive performance evaluation [ 1 , 2 , 3 , 4 ]. However, environmental factors, such as temperature, humidity, wind speed, and extremely harsh environments, can also cause significant changes in the natural vibration characteristics of the structure, which may even be greater than the changes in the natural vibration characteristics caused by actual damage, leading to difficulties in damage identification technologies for bridge monitoring [ 5 , 6 , 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Influence of Temperature on the Natural Vibration Characteristics of Simply Supported Reinforced Concrete Beam

Cai

Zhang²,

et al. 2021

Sensors

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Natural vibration characteristics serve as one of the crucial references for bridge monitoring. However, temperature-induced changes in the natural vibration characteristics of bridge structures may exceed the impact of structural damage, thus causing some interference in damage identification. This study analyzed the influence of temperature on the natural vibration characteristics of simply supported beams, which is the most widely used bridge structure. The theoretical formula for the variation of the natural frequency of simply supported beams with temperature was proposed. The elastic modulus of simply supported beams in the range of −40 °C to 60 °C was acquired by means of the falling ball test and the theoretical formula and was compared with the elastic modulus obtained by the three-point bending test at room temperature (20 °C). In addition, the Midas/Civil finite-element simulation was carried out for the natural frequency of simply supported beams at different temperatures. The results showed that temperature was the main factor causing the variation of the natural frequency of simply supported beams. The linear negative correlation between the natural frequency of simply supported beams and their temperature were observed. The natural frequency of simply supported beams decreased by 0.148% for every 1 °C increase. This research contributed to the further understanding of the natural vibration characteristics of simply supported beams under the influence of temperature so as to provide references for natural frequency monitoring and damage identification of beam bridges.

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“…Many methods have been applied to eliminate the influence of ambient factors on modal parameters. The commonly used methods are the Bayesian framework [11,12], time series analysis [13][14][15] and artificial neural network (ANN) [16][17][18]. Behmanesh et al [11] presented a hierarchical Bayesian framework in the absence of noise or model discrepancies to accurately identify parameters subjected to external actions.…”

Section: Introductionmentioning

confidence: 99%

“…Shan et al [17] applied three regression-based numerical models, including multiple linear regression (MLR), back-propagation neural network (BPNN), and support vector regression (SVR) to capture the relations between modal frequencies and temperature distributions from measurements of a concrete beam during a period of 40 days. Teng et al [18] conducted the continuous dynamic monitoring of a bridge and applied ANN to remove the temperature effect on modal frequencies so that a health index can be constructed under operational conditions.…”

Section: Introductionmentioning

confidence: 99%

Ambient Effect Filtering Using NLPCA-SVR in High-Rise Buildings

Zhang

et al. 2020

Sensors

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The modal frequencies of a structure are affected by continuous changes in ambient factors, such as temperature, wind speed etc. This study incorporates nonlinear principal component analysis (NLPCA) with support vector regression (SVR) to build a mathematical model to reflect the correlation between ambient factors and modal frequencies. NLPCA is first used to eliminate the high correlation among different ambient factors and extract the nonlinear principal components. The extracted nonlinear principal components are input into the SVR model for training and predicting. The proposed method is verified by the measured data provided in the Guangzhou New TV Tower (GNTVT) Benchmark. The grid search method (GSM), genetic algorithm (GA) and fruit fly optimization algorithm (FOA) are applied to determine the optimal hyperparameters for the SVR model. The optimized result of FOA is most suitable for the NLPCA-SVR model. As evaluated by the hypothesis test and goodness-of-fit test, the results show that the proposed method has a high generalization performance and the correlation between the ambient factor and modal frequency can be strongly reflected. The proposed method can effectively eliminate the effects of ambient factors on modal frequencies.

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Automated Modal Analysis for Tracking Structural Change during Construction and Operation Phases

Cited by 21 publications

References 27 publications

Mechanism of the effect of temperature on frequency based on long-term monitoring of an arch bridge

Mechanism of the effect of temperature on frequency based on long-term monitoring of an arch bridge

Influence of Temperature on the Natural Vibration Characteristics of Simply Supported Reinforced Concrete Beam

Ambient Effect Filtering Using NLPCA-SVR in High-Rise Buildings

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