• Journal of Korean Society of Steel Construction
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• v.14 no.3
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• pp.395-402
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• 2002

This study proposed a muti-objective optimum design method for rational optimizing of orthotropic steel deck bridges. This multi-objective optimum design method was found to be effective in optimizing multi-objective problems, considering cost and deflection functions. It may ve difficult to optimize orthotropic steel deck bridges using a conventional optimization, since the bridges have several parts and show complex structural behaviors. Therefore, the Pareto curve can be obtained by performing the multi-objective optimization for real orthotropic steel deck bridges, using the multi-level technique with excellent efficiency. A reasonable and economical design can be attained using the Parato curve in the cost and deflection functions of the bridge. Thus, more reasonable design values can be determined based on a comparison with those using a conventional design procedure.

• Steel and Composite Structures
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• v.46 no.6
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• pp.731-744
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• 2023

Vehicle load information is an important role in operating and ensuring the structural health of cable-stayed bridges. In this regard, an efficient and economic method is proposed for vehicle load detection based on the observed cable tension and vehicle position using a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), a robust program for modeling and considering both geometric and material nonlinearities of bridge structures subjected to vehicle load with low computational costs. With the superiority of GNN, the proposed model is demonstrated to precisely capture complex nonlinear correlations between the input features and vehicle load in the output. Four popular machine learning methods including artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machines (SVM) are refereed in a comparison. A case study of a cable-stayed bridge with the typical truck is considered to evaluate the model's performance. The results demonstrate that the GNN-based model provides high accuracy and efficiency in prediction with satisfactory correlation coefficients, efficient determination values, and very small errors; and is a novel approach for vehicle load detection with the input data of the existing monitoring system.

• Steel and Composite Structures
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• v.35 no.5
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• pp.641-657
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• 2020

A unique lightweight string truss deployable bridge assembled by thin-walled fiber reinforced polymer (FRP) and metal profiles was designed for emergency applications. As a new structure, investigations into the static structural performance under the serviceability limit state are desired for examining the structural integrity of the developed bridge when subjected to unsymmetrical loadings characterized by combined torsion and bending. In this study, a full-scale experimental inspection was conducted on a fabricated bridge, and the combined flexural-torsional behavior was examined in terms of displacement and strains. The experimental structure showed favorable strength and rigidity performances to function as deployable bridge under unsymmetrical loading conditions and should be designed in accordance with the stiffness criterion, the same as that under symmetrical loads. In addition, a finite element model (FEM) with a simple modeling process, which considered the multi segments of the FRP members and realistic nodal stiffness of the complex unique hybrid nodal joints, was constructed and compared against experiments, demonstrating good agreement. A FEM-based numerical analysis was thereafter performed to explore the effect of the change in elastic modulus of different FRP elements on the static deformation of the bridge. The results confirmed that the change in elastic modulus of different types of FRP element members caused remarkable differences on the bending and torsional stiffness of the hybrid bridge. The global stiffness of such a unique bridge can be significantly enhanced by redesigning the critical lower string pull bars using designable FRP profiles with high elastic modulus.

• Smart Structures and Systems
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• v.10 no.2
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• pp.131-154
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• 2012

In this paper, it is aimed to determine the seismic behaviour of highway bridges by nondestructive testing using ambient vibration measurements. Eynel Highway Bridge which has arch type structural system with a total length of 216 m and located in the Ayvaclk county of Samsun, Turkey is selected as an application. The bridge connects the villages which are separated with Suat U$\breve{g}$urlu Dam Lake. A three dimensional finite element model is first established for a highway bridge using project drawings and an analytical modal analysis is then performed to generate natural frequencies and mode shapes in the three orthogonal directions. The ambient vibration measurements are carried out on the bridge deck under natural excitation such as traffic, human walking and wind loads using Operational Modal Analysis. Sensitive seismic accelerometers are used to collect signals obtained from the experimental tests. To obtain experimental dynamic characteristics, two output-only system identification techniques are employed namely, Enhanced Frequency Domain Decomposition technique in the frequency domain and Stochastic Subspace Identification technique in time domain. Analytical and experimental dynamic characteristic are compared with each other and finite element model of the bridge is updated by changing of boundary conditions to reduce the differences between the results. It is demonstrated that the ambient vibration measurements are enough to identify the most significant modes of highway bridges. After finite element model updating, maximum differences between the natural frequencies are reduced averagely from 23% to 3%. The updated finite element model reflects the dynamic characteristics of the bridge better, and it can be used to predict the dynamic response under complex external forces. It is also helpful for further damage identification and health condition monitoring. Analytical model of the bridge before and after model updating is analyzed using 1992 Erzincan earthquake record to determine the seismic behaviour. It can be seen from the analysis results that displacements increase by the height of bridge columns and along to middle point of the deck and main arches. Bending moments have an increasing trend along to first and last 50 m and have a decreasing trend long to the middle of the main arches.

• Smart Structures and Systems
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• v.30 no.1
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• pp.17-34
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• 2022

For steel structures, fatigue cracks are critical damage induced by long-term cycle loading and distortion effects. Vision-based crack detection can be a solution to ensure structural integrity and performance by continuous monitoring and non-destructive assessment. A critical issue is to distinguish cracks from other features in captured images which possibly consist of complex backgrounds such as handwritings and marks, which were made to record crack patterns and lengths during periodic visual inspections. This study presents a parametric study on image-based crack identification for orthotropic steel bridge decks using captured images with complicated backgrounds. Firstly, a framework for vision-based crack segmentation using the atrous convolution-based Deeplapv3+ network (ACDN) is designed. Secondly, features on crack images are labeled to build three databanks by consideration of objects in the backgrounds. Thirdly, evaluation metrics computed from the trained ACDN models are utilized to evaluate the effects of obstacles on crack detection results. Finally, various training parameters, including image sizes, hyper-parameters, and the number of training images, are optimized for the ACDN model of crack detection. The result demonstrated that fatigue cracks could be identified by the trained ACDN models, and the accuracy of the crack-detection result was improved by optimizing the training parameters. It enables the applicability of the vision-based technique for early detecting tiny fatigue cracks in steel structures.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.4
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• pp.1-12
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• 2024

In this study, a determination method of structural member section based on Nonlinear behaviors of steel cable-stayed bridges and the Harmony Search algorithm was presented. The Harmony Search algorithm determines the structural member section of cable-stayed bridges by repeating the process of setting the initial value, initializing the harmony memory, configuring the new harmony memory, and updating the harmony memory to search for the optimal value. The nonlinear initial shape analysis of a three-dimensional steel cable-stayed bridge was performed with the cross-section of the main member selected by the Harmony Search algorithm, and the optimal cross-section of the main members of the cable-stayed bridge, such as pylons, girders, cross-beams, and cables, reflecting the complex behavior characteristics and the nonlinearity of each member was determined in consideration of the initial tension and shape. The total weight was used as the objective function for determining the cross-section of the main member of the cable-stayed bridges, and the load resistance ability and serviceability based on the ultimate state design method were used as the restraint conditions. The width and height ratio of the girder and cross-section were considered additional restraint conditions. The optimal sections of the main members were made possible to be determined by considering the geometry and material nonlinearity of the pylons, girders, and cross-sections and the nonlinearity of the cable members. As a result of determining the optimal cross-section, it was confirmed that the proposed analysis method can determine the optimal cross-section according to the various constraint conditions of the cable-stayed bridge, and the structural member section of the cable-stayed bridge considering the nonlinearity can be determined through the Harmony Search algorithm.

• Journal of the Korea Concrete Institute
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• v.24 no.3
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• pp.249-258
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• 2012

Integral and rigid frame bridges have advantages in bridge maintenance and structural efficiency by eliminating expansion joints and bridge supports. However, the detail of typical girder-abutment connection is rather complex and increases construction cost depending on construction detail. For the purpose of compensating disadvantages such as complexity and additional cost, a new type of bridge is proposed in this study, which improves the efficiency of construction by simplifying the construction detail of girder-abutment connection. The proposed bridge has the connection detail of steel girder and abutment integrated by prestressed PS bar installed in the connection. In this study, finite element analysis and fatigue load test are conducted to evaluate the fatigue capacity of the proposed girder-abutment connection. The results of the finite element analysis revealed that the possibility of the fatigue damage in the girder-abutment connection is very low. The results of the fatigue load test verified that the integrity of the girder and abutment connection is maintained after 2,000,000 cycles of fatigue loading.

• Journal of Korean Society of Steel Construction
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• v.20 no.6
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• pp.691-700
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• 2008

Contemporary architectural structures have diverse and complex forms. Such structural variety demands requisite performance from the connections in the steel structure so that the latter could resist a horizontal force, such as an earthquake. The connections are the all-important components that create the discontinuous form and that support stress concentration, determining the stiffness and toughness of the entire steel frame. In this study, a real-scale column-to-beam connection was constructed in the 600MPa-grade high-strength and high-performance steel, to test its behavior. Its material and welding characteristics were examined in this study, and its structural performance was analyzed by conducting seismic-resistance tests on the full-scale, cross-shaped column-to-beam welded connections with non-scallop, ordinary-scallop, and reinforced-scallop details. The weld ability of the high-strength, high-performance steel was also evaluated, and data regarding the seismic design for practical application were provided.

• Steel and Composite Structures
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• v.44 no.2
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• pp.241-254
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• 2022

The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.

• Smart Structures and Systems
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• v.29 no.1
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• pp.153-166
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• 2022

Identifying fine cracks in steel bridge facilities is a challenging task of structural health monitoring (SHM). This study proposed an end-to-end crack image segmentation framework based on a one-step Convolutional Neural Network (CNN) for pixel-level object recognition with high accuracy. To particularly address the challenges arising from small object detection in complex background, efforts were made in loss function selection aiming at sample imbalance and module modification in order to improve the generalization ability on complicated images. Specifically, loss functions were compared among alternatives including the Binary Cross Entropy (BCE), Focal, Tversky and Dice loss, with the last three specialized for biased sample distribution. Structural modifications with dilated convolution, Spatial Pyramid Pooling (SPP) and Feature Pyramid Network (FPN) were also performed to form a new backbone termed CrackDet. Models of various loss functions and feature extraction modules were trained on crack images and tested on full-scale images collected on steel box girders. The CNN model incorporated the classic U-Net as its backbone, and Dice loss as its loss function achieved the highest mean Intersection-over-Union (mIoU) of 0.7571 on full-scale pictures. In contrast, the best performance on cropped crack images was achieved by integrating CrackDet with Dice loss at a mIoU of 0.7670.