• Computers and Concrete
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• 제20권5호
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• pp.555-562
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• 2017

The accuracy and integrity of stress data acquired by bridge heath monitoring system is of significant importance for bridge safety assessment. However, the missing and abnormal data are inevitably existed in a realistic monitoring system. This paper presents a data reconstruction approach for bridge heath monitoring based on the wavelet multi-resolution analysis and support vector machine (SVM). The proposed method has been applied for data imputation based on the recorded data by the structural health monitoring (SHM) system instrumented on a prestressed concrete cable-stayed bridge. The effectiveness and accuracy of the proposed wavelet-based SVM prediction method is examined by comparing with the traditional autoregression moving average (ARMA) method and SVM prediction method without wavelet multi-resolution analysis in accordance with the prediction errors. The data reconstruction analysis based on 5-day and 1-day continuous stress history data with obvious preternatural signals is performed to examine the effect of sample size on the accuracy of data reconstruction. The results indicate that the proposed data reconstruction approach based on wavelet multi-resolution analysis and SVM is an effective tool for missing data imputation or preternatural signal replacement, which can serve as a solid foundation for the purpose of accurately evaluating the safety of bridge structures.

• Smart Structures and Systems
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• 제21권5호
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• pp.601-609
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• 2018

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.

• Smart Structures and Systems
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• 제5권4호
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• pp.369-379
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• 2009

The sudden collapse of Interstate 35 Bridge in Minneapolis gave a wake-up call to US municipalities to re-evaluate aging bridges. In this situation, structural health monitoring (SHM) technology can provide the essential help needed for monitoring and maintaining the nation's infrastructure. Monitoring long span bridges such as cable-stayed bridges effectively requires the use of a large number of sensors. In this article, we introduce a probabilistic approach to identify optimal locations of sensors to enhance damage detection. Probability distribution functions are established using an artificial neural network trained using a priori knowledge of damage locations. The optimal number of sensors is identified using multi-objective optimization that simultaneously considers information entropy and sensor cost-objective functions. Luling Bridge, a cable-stayed bridge over the Mississippi River, is selected as a case study to demonstrate the efficiency of the proposed approach.

• Smart Structures and Systems
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• 제31권5호
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• pp.501-516
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• 2023

Dynamic irregularity and acceleration of bridges subjected to high-speed trains provide crucial information for comprehensive evaluation of the health state of under-track structures. This paper proposes a novel approach for real-time estimation of vertical track dynamic irregularity and bridge acceleration using deep generative adversarial network (GAN) and vibration data from in-service train. The vehicle-body and bogie acceleration responses are correlated with the two target variables by modeling train-bridge interaction (TBI) through least squares generative adversarial network (LSGAN). To realize supervised learning required in the present task, the conventional LSGAN is modified by implementing new loss function and linear activation function. The proposed approach can offer pointwise and accurate estimates of track dynamic irregularity and bridge acceleration, allowing frequent inspection of high-speed railway (HSR) bridges in an economical way. Thanks to its applicability in scenarios of high noise level and critical resonance condition, the proposed approach has a promising prospect in engineering applications.

• Smart Structures and Systems
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• 제8권2호
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• pp.173-190
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• 2011

As a practical and effective seismic resisting technology, the base isolation system has seen extensive applications in buildings and bridges. However, a few problems associated with conventional lead-rubber bearings have been identified after historical strong earthquakes, e.g., excessive permanent deformations of bearings and potential unseating of bridge decks. Recently the applications of shape memory alloys (SMA) have received growing interest in the area of seismic response mitigation. As a result, a variety of SMA-based base isolators have been developed. These novel isolators often lead to minimal permanent deformations due to the self-centering feature of SMA materials. However, a rational design approach is still missing because of the fact that conventional design method cannot be directly applied to these novel devices. In light of this limitation, a displacement-based design approach for highway bridges with SMA isolators is proposed in this paper. Nonlinear response spectra, derived from typical hysteretic models for SMA, are employed in the design procedure. SMA isolators and bridge piers are designed according to the prescribed performance objectives. A prototype reinforced concrete (RC) highway bridge is designed using the proposed design approach. Nonlinear dynamic analyses for different seismic intensity levels are carried out using a computer program called "OpenSees". The efficacy of the displacement-based design approach is validated by numerical simulations. Results indicate that a properly designed RC highway bridge with novel SMA isolators may achieve minor damage and minimal residual deformations under frequent and rare earthquakes. Nonlinear static analysis is also carried out to investigate the failure mechanism and the self-centering ability of the designed highway bridge.

• Smart Structures and Systems
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• 제31권2호
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• pp.131-140
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• 2023

As well-known, the extended Kalman filter (EKF) is a powerful tool for parameter identification with limited measurements. However, traditional EKF is not applicable when the external excitation is unknown. By using least-squares estimation (LSE) for force identification, an EKF with unknown input (EKF-UI) approach was recently proposed by the authors. In this approach, to ensure the influence matrix be of full column rank, the sensors have to be deployed at all the degrees-of-freedom (DOFs) corresponding to the unknown excitation, saying collocated measurements are required. However, it is not easy to guarantee that the sensors can be installed at all these locations. To circumvent this limitation, based on the idea of first-order-holder discretization (FOHD), an improved EKF with unknown input (IEKF-UI) approach is proposed in this study for the simultaneous identification of structural parameters and unknown excitation. By using projection matrix, an improved observation equation is obtained. Few displacement measurements are fused into the observation equation to avoid the so-called low-frequency drift. To avoid the ill-conditioning problem for force identification without collocated measurements, the idea of FOHD is employed. The recursive solution of the structural states and unknown loads is then analytically derived. The effectiveness of the proposed approach is validated via several numerical examples. Results show that the proposed approach is capable of satisfactorily identifying the parameters of linear and nonlinear structures and the unknown excitation applied to them.

• Smart Structures and Systems
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• 제13권5호
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• pp.849-868
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• 2014

An indirect approach is explored for structural health bridge monitoring allowing for wide, yet cost-effective, bridge stock coverage. The detection capability of the approach is tested in a laboratory setting for three different reversible proxy types of damage scenarios: changes in the support conditions (rotational restraint), additional damping, and an added mass at the midspan. A set of frequency features is used in conjunction with a support vector machine classifier on data measured from a passing vehicle at the wheel and suspension levels, and directly from the bridge structure for comparison. For each type of damage, four levels of severity were explored. The results show that for each damage type, the classification accuracy based on data measured from the passing vehicle is, on average, as good as or better than the classification accuracy based on data measured from the bridge. Classification accuracy showed a steady trend for low (1-1.75 m/s) and high vehicle speeds (2-2.75 m/s), with a decrease of about 7% for the latter. These results show promise towards a highly mobile structural health bridge monitoring system for wide and cost-effective bridge stock coverage.

• International Journal of Concrete Structures and Materials
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• 제7권3호
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• pp.183-191
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• 2013

Prestress losses assumed for bridge girder design and deflection analyses are dependent on the concrete modulus of elasticity (MOE). Most design specifications, such as the American Association of State Highways and Transportation Officials (AASHTO) bridge specifications, contain a constant value for the MOE based on the unit weight of concrete and the concrete compressive strength at 28 days. It has been shown in the past that that the concrete MOE varies with the age of concrete. The purpose of this study was to evaluate the effect of a time-dependent and variable MOE on the prestress losses assumed for bridge girder design. For this purpose, three different variable MOE models from the literature were investigated: Dischinger (Der Bauingenieur 47/48(20):563-572, 1939a; Der Bauingenieur 5/6(20):53-63, 1939b; Der Bauingenieur, 21/22(20):286-437, 1939c), American Concrete Institute (ACI) 209 (Tech. Rep. ACI 209R-92, 1992) and CEB-FIP (CEB-FIP Model Code, 2010). A typical bridge layout for the Dallas, Texas, USA, area was assumed herein. A prestressed concrete beam design and analysis program from the Texas Department of Transportation (TxDOT) was utilized to determine the prestress losses. The values of the time dependent MOE and also specific prestress losses from each model were compared. The MOE predictions based on the ACI and the CEB-FIP models were close to each other; in long-term, they approach the constant AASHTO value. Dischinger's model provides for higher MOE values. The elastic shortening and the long term losses from the variable MOE models are lower than that using a constant MOE up to deck casting time. In long term, the variable MOE-based losses approach that from the constant MOE predictions. The Dischinger model would result in more conservative girder design while the ACI and the CEB-FIP models would result in designs more consistent with the AASHTO approach.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2002년도 가을 학술발표회 논문집
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• pp.135-140
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• 2002

Bridge columns in strong earthquake area are to be designed and constructed so that enough ductility should be guaranteed. Therefore, large amount of transverse reinforcement is required to confine core concrete of the bridge column by design specifications. In moderate seismicity regions, however, adopting the full ductility design concept sometimes results in construction problems due to reinforcement congestion. For the moderate seismicity regions, a design based on required ductility and required transverse reinforcement might be a reasonable approach. Ductility demand design or performance-based design might be an appropriate approach especially for regions of moderate seismic risk. The procedure and application of this design approach are presented in this paper.

• Computers and Concrete
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• 제29권1호
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• pp.59-67
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• 2022

In this paper, a reliability-based approach has been implemented to develop seismic analytical fragility curves of highway bridges. A typical bridge class of the Central and South-eastern United States (CSUS) region was selected. Detailed finite element modelling is presented and Incremental Dynamic Analysis (IDA) is used to capture the behavior of the bridge from linear to nonlinear behavior. Bayesian linear regression method is used to define the demand model. A reliability approach is implemented to generate the analytical fragility curves and the proposed approach is compared with the conventional fragility analysis procedure.