• Structural Engineering and Mechanics
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• v.46 no.1
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• pp.75-90
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• 2013

In order to identify damage of highway bridges rapidly, a method for damage identification using dynamic response of bridge induced by moving vehicle and static test data is proposed. To locate damage of the structure, displacement energy damage index defined from the energy of the displacement response time history is adopted as the indicator. The displacement response time histories of bridge structure are obtained from simulation of vehicle-bridge coupled vibration analysis. The vehicle model is considered as a four-degree-of-freedom system, and the vibration equations of the vehicle model are deduced based on the D'Alembert principle. Finite element method is used to discretize bridge and finite element model is set up. According to the condition of displacement and force compatibility between vehicle and bridge, the vibration equations of the vehicle and bridge models are coupled. A Newmark-${\beta}$ algorithm based professional procedure VBAP is developed in MATLAB, and used to analyze the vehicle-bridge system coupled vibration. After damage is located by employing the displacement energy damage index, the damage extent is estimated through the least-square-method based model updating using static test data. At last, taking one simply supported bridge as an illustrative example, some damage scenarios are identified using the proposed damage identification methodology. The results indicate that the proposed method is efficient for damage localization and damage extent estimation.

• Smart Structures and Systems
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• v.32 no.1
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• pp.49-59
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• 2023

A bridge transportation network supplies products from various source nodes to destination nodes through bridge structures in a target region. However, recent frequent earthquakes have caused damage to bridge structures, resulting in extreme direct damage to the target area as well as indirect damage to other lifeline structures. Therefore, in this study, a surrogate model-based comprehensive framework to estimate the seismic resilience of bridge transportation networks is proposed. For this purpose, total system travel time (TSTT) is introduced for accurate performance indicator of the bridge transportation network, and an artificial neural network (ANN)-based surrogate model is constructed to reduce traffic analysis time for high-dimensional TSTT computation. The proposed framework includes procedures for constructing an ANN-based surrogate model to accelerate network performance computation, as well as conventional procedures such as direct Monte Carlo simulation (MCS) calculation and bridge restoration calculation. To demonstrate the proposed framework, Pohang bridge transportation network is reconstructed based on geographic information system (GIS) data, and an ANN model is constructed with the damage states of the transportation network and TSTT using the representative earthquake epicenter in the target area. For obtaining the seismic resilience curve of the Pohang region, five epicenters are considered, with earthquake magnitudes 6.0 to 8.0, and the direct and indirect damages of the bridge transportation network are evaluated. Thus, it is concluded that the proposed surrogate model-based framework can efficiently evaluate the seismic resilience of a high-dimensional bridge transportation network, and also it can be used for decision-making to minimize damage.

• Smart Structures and Systems
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• v.31 no.1
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• pp.29-43
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• 2023

This study investigates the possibility of damage detection of a real bridge by means of a modal parameter-based finite element (FE) model update. Field moving vehicle experiments were conducted on an actual steel plate girder bridge. In the damage experiment, cracks were applied to the bridge to simulate damage states. A fast Bayesian FFT method was employed to identify and quantify uncertainties of the modal parameters then these modal parameters were used in the Bayesian model update. Material properties and boundary conditions are taken as uncertainties and updated in the model update process. Observations showed that although some differences existed in the results obtained from different model classes, the discrepancy between modal parameters of the FE model and those experimentally obtained was reduced after the model update process, and the updated parameters in the numerical model were indeed affected by the damage. The importance of boundary conditions in the model updating process is also observed. The capability of the MCMC model update method for application to the actual bridge structure is assessed, and the limitation of FE model update in damage detection of bridges using only modal parameters is observed.

• Earthquakes and Structures
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• v.13 no.6
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• pp.599-611
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• 2017

This paper improves seismic fragility of a typical steel-concrete composite bridge with the deck-to-pier connection joint configuration at the concrete crossbeam (CCB). Based on the quasi-static test on a typical steel-concrete composite bridge model under the SEQBRI project, the damage states for both of the critical components, the CCB and the pier, are identified. The finite element model is developed, and calibrated using the experimental data to model the damage states of the CCB and the bridge pier as observed from the experiment of the test specimen. Then the component fragility curves for both of the CCB and the pier are derived and combined to develop the system fragility curves of the bridge. The uncertainty associated with the mean system fragility has been discussed and quantified. The study reveals that the CCB is more vulnerable than the pier for certain damage states and the typical steel-concrete composite bridge with CCB exhibits desirable seismic performance.

• Smart Structures and Systems
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• v.12 no.6
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• pp.679-694
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• 2013

In this paper the application of the Interpolation Damage Detection Method to the numerical model of a suspension bridge instrumented with a network of Micro-Electro-Mechanical System sensors is presented. The method, which, in its present formulation, belongs to Level II damage identification method, can identify the presence and the location of damage from responses recorded on the structure before and after a seismic damaging event. The application of the method does not require knowledge of the modal properties of the structure nor a numerical model of it. Emphasis is placed herein on the influence of recorded signals noise on the reliability of the results given by the Interpolation Damage Detection Method. The response of a suspension bridge to seismic excitation is computed from a numerical model and artificially corrupted with random noise characteristic of two families of Micro-Electro-Mechanical System accelerometers. The reliability of the results is checked for different damage scenarios.

• Earthquakes and Structures
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• v.8 no.5
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• pp.1113-1126
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• 2015

A damage localization algorithm based on Operational Deformed Shapes and known as Interpolation Damage Detection Method, is herein applied to the finite element model of a cable stayed bridge for detecting and localizing damages in the stays and the supporting steel beams under the bridge deck. Frequency Response Functions have been calculated basing on the responses of the bridge model to low intensity seismic excitations and used to recover the Operational Deformed Shapes both in the transversal and in the vertical direction. The analyses have been carried in the undamaged configuration and repeated in several different damaged configurations. Results show that the method is able to detect the damage and its correct location, provided an accurate estimation of the Operational Deformed Shapes is available. Furthermore, the damage detection algorithm results effective also when damages coexist at the same time at several location of the cable-stayed bridge members.

• International Journal of Concrete Structures and Materials
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• v.1 no.1
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• pp.19-28
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• 2007

A dynamic constitutive damage model for reinforced concrete (RC) structures and formulations of blast loading for contact or near-contact charges are considered and adapted from literatures. The model and the formulations are applied to the input parameters needed in commercial finite element method (FEM) codes which is validated by the laboratory blast tests of RC slabs from literature. The results indicate that the dynamic constitutive damage model based on the damage mechanics and the blast loading formulations work well. The framework on the dynamic constitutive damage model and the blast loading equations can therefore be used for the simulation of failure of bridge components in engineering applications.

• Smart Structures and Systems
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• v.26 no.1
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• pp.35-47
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• 2020

This paper proposes the use of transmissibility functions combined with a machine learning algorithm, Artificial Neural Networks (ANNs), to assess damage in a truss bridge. A new approach method, which makes use of the input parameters calculated from the transmissibility function, is proposed. The network not only can predict the existence of damage, but also can classify the damage types and identity the location of the damage. Sensors are installed in the truss joints in order to measure the bridge vibration responses under train and ambient excitations. A finite element (FE) model is constructed for the bridge and updated using FE software and experimental data. Both single damage and multiple damage cases are simulated in the bridge model with different scenarios. In each scenario, the vibration responses at the considered nodes are recorded and then used to calculate the transmissibility functions. The transmissibility damage indicators are calculated and stored as ANNs inputs. The outputs of the ANNs are the damage type, location and severity. Two machine learning algorithms are used; one for classifying the type and location of damage, whereas the other for finding the severity of damage. The measurements of the Nam O bridge, a truss railway bridge in Vietnam, is used to illustrate the method. The proposed method not only can distinguish the damage type, but also it can accurately identify damage level.

• Structural Monitoring and Maintenance
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• v.7 no.2
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• pp.149-166
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• 2020

This paper presents the results of an experimental investigation on a vibration-based damage identification framework for a steel girder type and a truss bridge based on acceleration responses to operational loading. The method relies on sensor clustering-based time-series analysis of the operational acceleration response of the bridge to the passage of a moving vehicle. The results are presented in terms of Damage Features from each sensor, which are obtained by comparing the actual acceleration response from the sensors to the predicted response from the time-series model. The damage in the bridge is detected by observing the change in damage features of the bridge as structural changes occur in the bridge. The relative severity of the damage can also be quantitatively assessed by observing the magnitude of the changes in the damage features. The experimental results show the potential usefulness of the proposed method for future applications on condition assessment of real-life bridge infrastructures.

• Smart Structures and Systems
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• v.28 no.6
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• pp.799-810
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• 2021

When monitoring the structural integrity of a bridge using data collected through accelerometers, identifying the profile of the load exerted on the bridge from the vehicles passing over it becomes a crucial task. In this study, the speed and location of vehicles on the deck of a bridge is reconfigured using real-time video to implicitly associate the load applied to the bridge with the response from the bridge sensors to develop an image-based deep learning network model. Instead of directly measuring the load that a moving vehicle exerts on the bridge, the intention in the proposed method is to replace the correlation between the movement of vehicles from CCTV images and the corresponding response by the bridge with a neural network model. Given the framework of an input-output-based system identification, CCTV images secured from the bridge and the acceleration measurements from a cantilevered beam are combined during the process of training the neural network model. Since in reality, structural damage cannot be induced in a bridge, the focus of the study is on identifying local changes in parameters by adding mass to a cantilevered beam in the laboratory. The study successfully identified the change in the material parameters in the beam by using the deep-learning neural network model. Also, the method correctly predicted the acceleration response of the beam. The proposed approach can be extended to the structural health monitoring of actual bridges, and its sensitivity to damage can also be improved through optimization of the network training.