• Proceedings of the Korea Concrete Institute Conference
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• 2006.05a
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• pp.194-197
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• 2006

This study represents results of fragility curve development for 4-span continuous bridge. 2 type bridge model is chosen frame type and 2-roller 1-hinge type. To research the response of bridge under earthquake excitation, Monte Carlo simulation is performed to study nonlinear dynamic analysis. For nonlinear time history analysis a set of 150 synthetic time histories were generated. Fragility curves in this study are represented by lognormal distribution functions with two parameters and developed as a function of PGA. Five damage states were defined to express the condition of damage based on the actual experimental damage data of bridge column. As a result of this research, the value of damage probability corresponding to each damage state were determined and frame type bridge are favorable under seismic event.

• 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.

• Computers and Concrete
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• v.20 no.5
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• pp.521-527
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• 2017

This paper proposes using the multi-type sensor vibration measurements, such as from a relative displacement sensors and a traditional accelerometer for the damage detection of shear connectors in composite bridge under moving loads. Hilbert-Huang Transform (HHT) spectra of these responses will be fused with a data fusion approach i.e., Dempster-Shafer method, to detect the damage of shear connectors. Experimental studies on a composite bridge model in the laboratory are conducted to demonstrate the effectiveness and performance of using the proposed approach in detecting the damage of shear connectors in composite bridges. Both undamaged and damaged scenarios are considered. The detection results with the data fusion of multi-type sensor measurements show a more reliable and robust performance and accuracy, avoiding the false identifications.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.208-209
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• 2022

This study aims to analyze the risk factors caused by object damage and third-party damage loss in actual bridge construction based on past insurance premium payment data from major domestic insurers for bridge construction projects, and develop a quantitative loss prediction model. For the development of quantitative bridge construction loss model, the dependent variable was selected as the loss ratio, and the independent variable adopted 1) Technical factors: superstructure type, foundation type, construction method, and bridge length 2) Natural hazards: flood anf Typhoon, 3) Project information: total construction duration, total cost and ranking. Among the selected independent variables, superstructure type, construction method, and project period were shown to affect the ratio of bridge construction losses, while superstructure, foundation, flood and ranking were shown to affect the ratio of the third-party losses.

• Smart Structures and Systems
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• v.13 no.5
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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.

• Interaction and multiscale mechanics
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• v.1 no.3
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• pp.357-368
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• 2008

The result of damage detection from on-site measurements is commonly polluted by unavoidable measurement noises. It is widely recognized that this side influence could be reduced to some extent if the sensor placement was properly designed. Although many methods have been proposed to find the optimal number and location of mono-type sensors, the optimal layout of multi-type sensors need further investigation, because a network of heterogeneous sensors is commonly used in engineering. In this paper, a new criterion of the optimal placement for different types of sensors is proposed. A corresponding heuristic is developed to search for good results. In addition, Monte Carlo simulation is suggested to design a robust damage detection system which contains certain redundancies. The validity of these methods is illustrated by two bridge examples.

• Structural Engineering and Mechanics
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• v.46 no.5
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• pp.615-627
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• 2013

One of the most common defects in reinforced concrete bridge decks is corrosion of steel reinforcing bars. This invisible defect reduces the deck stiffness and affects the bridge's serviceability. Regular monitoring of the bridge is required to detect and control this type of damage and in turn, minimize repair costs. Because the corrosion is hidden within the deck, this type of damage cannot be easily detected by visual inspection and therefore, an alternative damage detection technique is required. This research develops a non-destructive method for detecting reinforcing bar corrosion. Experimental modal analysis, as a non-destructive testing technique, and finite element (FE) model updating are used in this method. The location and size of corrosion in the reinforcing bars is predicted by creating a finite element model of bridge deck and updating the model characteristics to match the experimental results. The practicality and applicability of the proposed method were evaluated by applying the new technique to a two spans bridge for monitoring steel bar corrosion. It was shown that the proposed method can predict the location and size of reinforcing bars corrosion with reasonable accuracy.

• Structural Engineering and Mechanics
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• v.39 no.2
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• pp.187-206
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• 2011

This paper uses dynamic computer simulation techniques to develop and apply a multi-criteria procedure using non-destructive vibration-based parameters for damage assessment in truss bridges. In addition to changes in natural frequencies, this procedure incorporates two parameters, namely the modal flexibility and the modal strain energy. Using the numerically simulated modal data obtained through finite element analysis of the healthy and damaged bridge models, algorithms based on modal flexibility and modal strain energy changes before and after damage are obtained and used as the indices for the assessment of structural health state. The application of the two proposed parameters to truss-type structures is limited in the literature. The proposed multi-criteria based damage assessment procedure is therefore developed and applied to truss bridges. The application of the approach is demonstrated through numerical simulation studies of a single-span simply supported truss bridge with eight damage scenarios corresponding to different types of deck and truss damage. Results show that the proposed multi-criteria method is effective in damage assessment in this type of bridge superstructure.

• Structural Engineering and Mechanics
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• v.7 no.4
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• pp.387-400
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• 1999

Damage detection in civil engineering structures using the change in dynamic system parameters has gained a lot of scientific interest during the last decade. By repeating a dynamic test on a structure after a certain time of use, the change in modal parameters can be used to quantify and qualify damages. To be able to use the modal parameters confidentially for damage evaluation, the effect of other parameters such as excitation type, ambient conditions,... should be considered. In this paper, the influence of excitation type on the dynamic system parameters of a highway prestressed concrete bridge is investigated. The bridge, B13, lies between the villages Vilvoorde and Melsbroek and crosses the highway E19 between Brussels and Antwerpen in Belgium. A drop weight and ambient vibration are used to excite the bridge and the response at selected points is recorded. A finite element model is constructed to support and verify the dynamic measurements. It is found that the difference between the natural frequencies measured using impact weight and ambient vibration is in general less than 1%.