• Steel and Composite Structures
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• v.31 no.6
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• pp.575-589
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• 2019

The main focus of this study is to numerically investigate the influence of strong earthquake and tsunami-induced wave impact on the response and behavior of a cable-stayed steel bridge with large caisson foundations, by assuming that the earthquake and the tsunami come from the same fault motion. For this purpose, a series of numerical simulations were carried out. First of all, the tsunami-induced flow speed, direction and tsunami height were determined by conducting a two-dimensional (2D) tsunami propagation analysis in a large area, and then these parameters obtained from tsunami propagation analysis were employed in a detailed three-dimensional (3D) fluid analysis to obtain tsunami-induced wave impact force. Furthermore, a fiber model, which is commonly used in the seismic analysis of steel bridge structures, was adopted considering material and geometric nonlinearity. The residual stresses induced by the earthquake were applied into the numerical model during the following finite element analysis as the initial stress state, in which the acquired tsunami forces were input to a whole bridge system. Based on the analytical results, it can be seen that the foundation sliding was not observed although the caisson foundation came floating slightly, and the damage arising during the earthquake did not expand when the tsunami-induced wave impact is applied to the steel bridge. It is concluded that the influence of tsunami-induced wave force is relatively small for such steel bridge with large caisson foundations. Besides, a numerical procedure is proposed for quantitatively estimating the accumulative damage induced by the earthquake and the tsunami in the whole bridge system with large caisson foundations.

• Wind and Structures
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• v.27 no.6
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• pp.399-416
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• 2018

In order to reveal the independent relationship between track irregularity and wind loads, the stochastic characteristics of train-bridge coupling systems subjected to wind loads were investigated by the multi-sample calculation. The vehicle was selected as 23 degrees of freedom dynamical model, and the bridge was described by three-dimensional finite element model. It was assumed that the wind loads were random processes with strong spatial correlation, while the track irregularities were stationary random ones. As a case study, a high-speed train running on a cable-stayed bridge subjected to wind loads was studied. The effect of rail irregularities was deemed to be independent of the effect of wind excitations on the coupling system in the same wind circumstance for the same project, leading to the conclusion that the effect of wind loads and moving vehicle could be calculated separately. The variance results of the stochastic responses of vehicle-bridge coupling system under the action of wind loads and rail irregularities together were equivalent to the sum of the variance of the responses induced by each excitation. Therefore, when one of the input excitations is different, only the effect of changed loads needs to be assessed. Moreover, the new calculated results were combined with the effect of unchanged loads to present the stochastic response of coupling system subjected to the different excitations, reducing the cost of computations. The stochastic characteristics, the CFD (cumulative distribution function) of the coupling system with different wind velocities, vehicle speed, and vehicle marshalling were studied likewise.

• International journal of steel structures
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• v.18 no.4
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• pp.1191-1199
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• 2018

Concepts of submerged floating tunnels (SFTs) for land connection have been continuously suggested and developed by several researchers and institutes. To maintain their predefined positions under various dynamic environmental loading conditions, the submerged floating tunnels should be effectively moored by reasonable mooring systems. With rational mooring systems, the design of SFTs should be confirmed to satisfy the structural safety, fatigue, and operability design criteria related to tunnel motion, internal forces, structural stresses, and the fatigue life of the main structural members. This paper presents a feasibility study of a submerged floating tunnel moored by an inclined tendon system. The basic structural concept was developed based on the concept of conventional cable-stayed bridges to minimize the seabed excavation, penetration, and anchoring work by applying tower-inclined tendon systems instead of conventional tendons with individual seabed anchors. To evaluate the structural performance of the new type of SFT, a hydrodynamic analysis was performed in the time domain using the commercial nonlinear finite element code ABAQUS-AQUA. For the main dynamic environmental loading condition, an irregular wave load was examined. A JONSWAP wave spectrum was used to generate a time-series wave-induced hydrodynamic load considering the specific significant wave height and peak period for predetermined wave conditions. By performing a time-domain hydrodynamic analysis on the submerged floating structure under irregular waves, the motional characteristics, structural stresses, and fatigue damage of the floating tunnel and mooring members were analyzed to evaluate the structural safety and fatigue performance. According to the analytical study, the suggested conceptual model for SFTs shows very good hydrodynamic structural performance. It can be concluded that the concept can be considered as a reasonable structural type of SFT.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.5
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• pp.43-48
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• 2022

In order to evaluate the seismic performance of the modular expansion joint known for its large expansion allowance and remarkable durability, this study conducts seismic response analysis and seismic simulation test. The bridge selected for the seismic response analysis is a cable stayed bridge with main span length of 1,000m. Three artificial earthquake were generated with respect to the design response spectra of the Korean Standards (KS), AASHTO LRFD and Eurocode, and applied to the selected bridge. The seismic simulation tests reproduced the artificial earthquakes using dynamic hydraulic actuators in the longitudinal and transverse directions. The test results verified the durability and safety of the expansion joint in view of its seismic behavior since abnormal behavior or failure of the expansion joint was not observed when the artificial earthquake waves were applied in the longitudinal direction, transverse direction and both directions.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.3
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• pp.323-331
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• 2022

An experimental study was conducted to investigate the effect of applied tensile force and temperature on the permeability of magnetic flux in prestressing steel. The permeability of magnetic flux is the ratio at which the magnetic flux between two points passes. The prestressing steel used in these experiments included a 7-mm PS wire mainly used for cable-stayed bridges and a 12.7-mm PS strand for prestressed concrete bridges. The experiments to extract the permeability of the magnetic flux of steel wire and strand were conducted under various tensile levels and temperature conditions. From the experimental results, it was observed that the permeability of magnetic flux of the PS tension material was linearly proportional to the applied tensile stress level, and inversely proportional to the temperature. If the experimental relationship among the magnetic permeability, temperature, and prestressing ratio of a PS tension material is known in advance, the current tension stress level on PS members can be evaluated by measuring solely the magnetic permeability and temperature.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.1
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• pp.49-56
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• 2023

With the development of technology worldwide, bridges are becoming larger, and the number of old bridges is also rapidly increasing. Monitoring the structural health of large, aging bridges is essential to preventing large-scale accidents. In this study, the application of a LoRa low-power wide-area network (LPWAN)-based wireless measurement system was investigated, and a LoRa wireless measurement system was established in the cable-stayed bridge section of Cheonsa Bridge, located in Shinan-gun, Jeollanam-do, Korea. The applicability of the LoRa LPWAN-based wireless monitoring system to large marine bridges was reviewed by comparing the performance and economic feasibility with wire-based monitoring systems that were built and operated by establishing a measurement system for the pylons, cables, and reinforcing girders of the bridge.

• Smart Structures and Systems
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• v.31 no.1
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• pp.57-68
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• 2023

Bridge hangers, such as those in suspension and cable-stayed bridges, suffer from cumulative fatigue damage caused by dynamic loads (e.g., cyclic traffic and wind loads) in their service condition. Thus, the identification of damage to hangers is important in preserving the service life of the bridge structure. This study develops a new method for condition assessment of bridge hangers. The tension force of the bridge and the damages in the element level can be identified using the Bayesian optimization method. To improve the number of observed data, the additional mass method is combined the Bayesian optimization method. Numerical studies are presented to verify the accuracy and efficiency of the proposed method. The influence of different acquisition functions, which include expected improvement (EI), probability-of-improvement (PI), lower confidence bound (LCB), and expected improvement per second (EIPC), on the identification of damage to the bridge hanger is studied. Results show that the errors identified by the EI acquisition function are smaller than those identified by the other acquisition functions. The identification of the damage to the bridge hanger with various types of boundary conditions and different levels of measurement noise are also studied. Results show that both the severity of the damage and the tension force can be identified via the proposed method, thereby verifying the robustness of the proposed method. Compared to the genetic algorithm (GA), particle swarm optimization (PSO), and nonlinear least-square method (NLS), the Bayesian optimization (BO) performs best in identifying the structural damage and tension force.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2A
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• pp.293-300
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• 2006

There is a high degree of uncertainty in measurements of the thickness or the loss of thickness of corroded elements. Generally the thickness of corroded elements varies from one location of the element to another depending on the degree of corrosion, which makes the safety assessment difficult. Therefore, a procedure for safety assessment of corrosion- damaged steel structures using an imprecise reliability is proposed in this paper. The proposed safety assessment procedure using the imprecise reliability was also applied to a cable-stayed bridge in Korea to demonstrate its effectiveness and applicability. Since there is a large variation in measurements of the thickness of corroded elements, the thickness of corroded elements was considered as the imprecise element. This variation was found to be directly related to the degree of corrosion. Therefore, the variation increases as the degree of corrosion increases. Based on the comparative observations between the conventional reliability and the imprecise reliability, it is suggested that the imprecise reliability analysis derived based on the subjective or statistical judgment of conditional independence could be successfully utilized for the risk or safety assessment of corrosion-damaged structures.

• Journal of Korean Society of Disaster and Security
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• v.16 no.4
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• pp.129-136
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• 2023

A special offshore bridge with a high pylon has special structural features.Special offshore bridges have inspection blind spots that are difficult to visually inspect. To solve this problem, safety inspection methods using drones are being studied. In this study, image data of the pylon of a special offshore bridge was acquired using a drone. In addition, an artificial intelligence algorithm was developed to detect damage to the pylon surface. The AI algorithm utilized a deep learning network with different structures. The algorithm applied the stacking ensemble learning method to build a model that formed the ensemble and collect the results.