• Smart Structures and Systems
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• v.18 no.3
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• pp.501-523
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• 2016

Electromechanical impedance (EMI) based structural health monitoring is performed by measuring the variation in the impedance due to the structural local damage. The impedance signals are acquired from the piezoelectric patches that are bonded on the structural surface. The impedance variation, which is directly related to the mechanical properties of the structure, indicates the presence of local structural damage. Two traditional EMI-based damage detection methods are based on calculating the difference between the measured impedance signals in the frequency domain from the baseline and the current structures. In this paper, a new structural damage detection approach by analyzing the time domain impedance responses is proposed. The measured time domain responses from the piezoelectric transducers will be used for analysis. With the use of the Time Frequency Autoregressive Moving Average (TFARMA) model, a damage index based on Singular Value Decomposition (SVD) is defined to identify the existence of the structural local damage. Experimental studies on a space steel truss bridge model in the laboratory are conducted to verify the proposed approach. Four piezoelectric transducers are attached at different locations and excited by a sweep-frequency signal. The impedance responses at different locations are analyzed with TFARMA model to investigate the effectiveness and performance of the proposed approach. The results demonstrate that the proposed approach is very sensitive and robust in detecting the bolt damage in the gusset plates of steel truss bridges.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.2A
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• pp.85-95
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• 2012

This paper presents an investigation on the ultimate behavior of steel cable-stayed bridges using nonlinear finite element analysis method. Cable-stayed bridges exhibit various geometric nonlinearities as well as material nonlinearities, so rational nonlinear finite element analysis should be performed for investigation of the ultimate behavior. In this study, ultimate behavior of steel cable-stayed bridges was studied using rational ultimate analysis method. Nonlinear equivalent truss element and nonlinear frame element were used for modeling the cable, girder and mast. Moreover, refined plastic hinge method was adopted for considering the material nonlinearity of steel members. In this study, the 2-step analysis method was used. Before live load analysis, initial shape analysis was performed in order to consider the dead load condition. For investigation of the ultimate behavior of steel cable-stayed bridges, analysis models which span length is 920.0 m were used. Radiating type and fan type were considered as the cable-arrangement types. With various quantitative evidences such as load-displacement curves, deformed shapes, locations of the yield point or region, bending moment distribution and so on, the ultimate behavior of steel cable-stayed bridges was investigated and described in this paper.

• 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 Engineering and Mechanics
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• v.8 no.1
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• pp.27-38
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• 1999

Cables are used in many applications such as cable-stayed bridges, suspension bridges, transmission lines, telephone lines, etc. Generally, the linear relationship is inadequate to present the behavior of cable structure. In finite element analysis, cables have always been modeled as truss elements. For these types of model, the nonlinear behavior of cables has been always ignored. In order to investigate the importance of the nonlinear effect on the structural system, the effect of cable stiffness has been studied. The nonlinear behavior of cable is due to its sag. Therefore, the cable pretension provides a large portion of the inherent stiffness. Since a cable-stayed bridge has numerous degrees of freedom, analytical methods at present are not convenient to solve this type of structures but numerical methods may be feasible. It is necessary to provide a different and more representative analytical model in order to present the effect of cable stiffness on cable-stayed bridges in numerical analysis. The characteristics of cable deformation have also been well addressed. A formulation of modified modulus of elasticity has been proposed using a numerical parametric study. In order to investigate realistic bridges, a cable-stayed bridge having the geometry similar to that of Quincy Bayview Bridge is considered. The numerical results indicate that the characteristics of the cable stiffness are strongly nonlinear. It also significantly affects the structural behaviors of cable-stayed bridge systems.

• Smart Structures and Systems
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• v.31 no.6
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• pp.585-599
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• 2023

Field monitoring techniques offer an attractive approach for understanding bridge behavior under in-service loads. However, the investigations on bridge behavior under high-speed train load using field monitoring data are limited. The focus of this study is to explore the structural behavior of an in-service long-span steel truss arch bridge based on field monitoring data. First, the natural frequencies of the structure, as well as the train driving frequencies, are extracted. Then, the train-induced bearing displacement and structural strain are explored to identify the effects of train loads and bearings. Subsequently, a sensitivity analysis is performed for the impact factor of strain responses with respect to the train speed, train weight, and temperature to identify the fundamental issues affecting these responses. Additionally, a similar sensitivity analysis is conducted for the peak acceleration. The results indicate that the friction force in bearings provides residual deformations when two consecutive trains are in opposite directions. In addition, the impact factor and peak acceleration are primarily affected by train speed, particularly near train speeds that result in the resonance of the bridge response. The results can provide additional insight into the behavior of the long-span steel truss bridges under in-service high-speed train loads.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.1A
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• pp.1-13
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• 2010

This paper presents an investigation on the geometric nonlinear behavior of cable-stayed bridges using geometric nonlinear finite element analysis method. The girder and mast in cable-stayed bridges show the combined axial load and bending moment interaction due to horizontal and vertical forces of inclined cable. So these members are considered as beam-column member. In this study, the nonlinear finite element analysis method is used to resolve the geometric nonlinear behavior of cable-stayed bridges in consideration of beam-column effect, large displacement effect (known as P-${\delta}$ effect) and cable sag effect. To analyze a cable-stayed bridge model, nonlinear 6-degree of freedom frame element and nonlinear 3-degree of freedom equivalent truss element is used. To resolve the geometric nonlinear behavior for various live load cases, the initial shape analysis is performed for considering dead load before live load analysis. Then the geometric nonlinear analysis for each live load case is performed. The deformed shapes of each model, load-displacement curves of each point and load-tensile force curves for each cable are presented for quantitative study of geometric nonlinear behavior of cable-stayed bridges.

• 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.31 no.4
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• pp.421-436
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• 2023

This paper presents a vibration displacement measurement and damage identification method for a space truss structure from its vibration videos. Features from Accelerated Segment Test (FAST) algorithm is combined with adaptive threshold strategy to detect the feature points of high quality within the Region of Interest (ROI), around each node of the truss structure. Then these points are tracked by Kanade-Lucas-Tomasi (KLT) algorithm along the video frame sequences to obtain the vibration displacement time histories. For some cases with the image plane not parallel to the truss structural plane, the scale factors cannot be applied directly. Therefore, these videos are processed with homography transformation. After scale factor adaptation, tracking results are expressed in physical units and compared with ground truth data. The main operational frequencies and the corresponding mode shapes are identified by using Subspace Stochastic Identification (SSI) from the obtained vibration displacement responses and compared with ground truth data. Structural damages are quantified by elemental stiffness reductions. A Bayesian inference-based objective function is constructed based on natural frequencies to identify the damage by model updating. The Success-History based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) is applied to minimise the objective function by tuning the damage parameter of each element. The locations and severities of damage in each case are then identified. The accuracy and effectiveness are verified by comparison of the identified results with the ground truth data.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.232-239
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• 2001

Young Jong Grand Bridge is approach traffic road of New Inchon International Airport which covers hub airport function in northeast asia. The total span length of this bridge is $4,420{\cal}m$ and this main bridge type is, the first in the world, Double Deck Self Anchored Suspension Bridge, designed as double deck systems to be arranged by road and railroad. Approach bridges to be connected with main span also are composed double deck steel truss and steel box girder to consider a continuity with this span. Our company erected $1,375{\cal}m$(about 60,000tons) of double deck steel truss bridge type which is composed by 6 traffic lane on upper deck and 4 traffic lane and Double track railroad on lower deck. The original installation method of this bridge was planed to install about 75 meters bridge blocks to use floating crane, after temporary bent was constructed between permanent piers. But this method which had to construct many temporary bents in the sea had the matter that construction periods can become lengthen and construction cost can be risen. To overcome the uncertainty to ensure high qualify of bridge and economic project execution, our company developed new bridge erection method to assure both quality control and economic construction work. The new erection method which was developed by us was one that could transport and install long bridge block, $120{\cal}m$ unit at a time and that temporary bent was not required. We hope that this paper is used as technical data which will erect bridge in the western sea and others marine region.

• Smart Structures and Systems
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• v.15 no.3
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• pp.683-698
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• 2015

Railroad bridges form an integral part of railway infrastructure throughout the world. To accommodate increased axel loads, train speeds, and greater volumes of freight traffic, in the presence of changing structural conditions, the load carrying capacity and serviceability of existing bridges must be assessed. One way is through system identification of in-service railroad bridges. To dates, numerous researchers have reported system identification studies with a large portion of their applications being highway bridges. Moreover, most of those models are calibrated at global level, while only a few studies applications have used globally and locally calibrated model. To reach the global and local calibration, both ambient vibration tests and controlled tests need to be performed. Thus, an approach for system identification of a railroad bridge that can be used to assess the bridge in global and local sense is needed. This study presents system identification of a railroad bridge using free vibration data. Wireless smart sensors are employed and provided a portable way to collect data that is then used to determine bridge frequencies and mode shapes. Subsequently, a calibrated finite element model of the bridge provides global and local information of the bridge. The ability of the model to simulate local responses is validated by comparing predicted and measured strain in one of the diagonal members of the truss. This research demonstrates the potential of using measured field data to perform model calibration in a simple and practical manner that will lead to better understanding the state of railroad bridges.