• Smart Structures and Systems
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• v.32 no.6
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• pp.371-382
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• 2023

Box girder bridges are now widely used in bridge construction, and it is necessary to perform load rating regularly to evaluate the load capacity of box girder bridges. Load testing is a common measure for load rating. However, the bridge must be loaded by many trucks under different loading conditions, which is time-consuming and laborious. To solve this problem, this paper proposes a load rating method for box girder bridges based on rapid moving loads testing. The method includes three steps. First, the quasi-influence factors of the bridge are obtained by crossing the bridge with rapidly moving loads, and the structural modal parameters are simultaneously obtained from the dynamic data to supplement. Second, an objective function is constructed, consisting of the quasi-influence factors at several measurement points and structural modal parameters. The finite element model for load rating is then updated based on the Rosenbrock method. Third, on this basis, a load rating method is proposed using the updated model. The load rating method proposed in this paper can considerably reduce the time duration of traditional static load testing and effectively utilize the dynamic and static properties of box girder bridges to obtain an accurate finite element model. The load capacity obtained based on the updated model can avoid the inconsistency of the evaluation results for the different structural members using the adjustment factors specified in codes.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.11a
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• pp.681-688
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• 2000

This study presented the new method for evaluating the coefficient of consolidation by using inflection point method which was based on the fact that time factor, T corresponding to the inflection point of a semilogarithmic plot of a time curve is fixed and equals to T ＝ 0.405 at 70% consolidation. In the proposed method, the next load increment is applied as soon as the necessary time required to identify the inflection point. Thus, the coefficient of consolidation may be easily evaluated. The time required to complete the testing using this rapid consolidation method could be as low as 1.5-3 hours compared with 1 or 2 weeks in the case of the conventional consolidation test.

• Journal of the Korean Society for Advanced Composite Structures
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• v.1 no.1
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• pp.34-45
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• 2010

This paper describes the design, manufacturing process, testing, application, and assessment of capacity-ratings of the first all advanced composites bridge on a public highway system. In order to verify the bridge design prior to the field application, a sub-scale bridge superstructure was built and tested in the laboratory. The field load test results were compared with those of the finite element analysis for the verification of validity. To investigate its in-service performance, field load testing and visual inspections were conducted under an actual service environment. The paper includes the presentation and discussion for advanced composites bridge capacity rating based on the stress modification coefficients obtained from the test results. The test result indicates that the advanced composites bridge has no structural problems and is structurally performing well in-service as expected. Since these composite materials are new to bridge applications, reliable data is not available for their in-service performance. The results may provide a baseline data for future field advanced composites bridge capacity rating assessments and also serve as part of a long-term performance of advanced composites bridge.

• Journal of Welding and Joining
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• v.3 no.2
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• pp.42-51
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• 1985

The dynamic fracture toughness, fracture characteristics, impact tension and tensile properties of Al-Mg-Si T5 alloy and Al-Zn-Mg T6 alloy respectively welded with filler metal of Alcan 4043 were investigated. The dynamic fracture toughness values were obtained rapidly and simply for the specimen of small size by using instrumented Chirpy impact testing machine. the testing temperatures of the specimen were a range of room temperature and-196.deg. C. The results obtained in this experiment are summarized as follows. With decreasing the testing temperatures, dynamic tensile stress and fracture load were increased, on the other hand the deflection and impact value showed decreasing tendency in order of base metal>HAZ>weld. Changes of total absorbed energy were more influenced by the crack propagation energy than the crack initiation energy. At the low temperatures, the unstable rapid fracture representing the crack propagation appeared for the specimens of Charpy press side notched in Al-Zn-Mg alloy, but it was difficult to obtain the unstable rapid fracture in Al-Mg-Si alloy. Because of the development of plastic zone at the notch root, it was difficult to obtain thevalid $K_{1d}$ value in Al-Mg-Si alloy. Therefore the fatigue cracked specimens were effective in both Al-Mg-Si and Al-Zn-Mg alloys. With decreasing the impact testing temperatures, specimens underwent a transition from dimple-type transgranular fracture to lamella surface-type intergranular fracture because of the precipitate at the grain boundaries, impurities and crystal structure of the precipitates.s.

• Computers and Concrete
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• v.18 no.6
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• pp.1113-1134
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• 2016

A methodology using neural networks has been proposed for rapid prediction of inelastic bending moments in reinforced concrete continuous beams subjected to service load. The closed form expressions obtained from the trained neural networks take into account cracking in concrete at in-span and at near the internal supports and tension stiffening effect. The expressions predict the inelastic moments (considering the concrete cracking) from the elastic moments (neglecting the concrete cracking) at supports. Three separate neural networks are trained since these have been postulated to represent all the beams having any number of spans. The training, validating, and testing data sets for the neural networks are generated using an analytical-numerical procedure of analysis. The proposed expressions are verified for example beams of different number of spans and cross-section properties and the errors are found to be small. The proposed expressions, at minimal input data and computation effort, yield results that are close to FEM results. The expressions can be used in preliminary every day design as they enable a rapid prediction of inelastic moments and require a computational effort that is a fraction of that required for the available methods in literature.

• Steel and Composite Structures
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• v.9 no.3
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• pp.223-237
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• 2009

The present research aims to develop a methodology to rapidly assess bridges with damage to the superstructure, caused by overheight trucks or lower-than-average overhead clearance. Terrestrial laser scanning and image processing techniques are combined with the finite element method to arrive at an analytical model which is more accurate, with respect to the complex geometrical aspects of the bridge in its damaged configuration. ""Virtual load testing"" may subsequently be carried out on this analytical model to determine the reserve capacity of the structure in an objective manner.

• Computers and Concrete
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• v.19 no.3
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• pp.293-303
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• 2017

Maximum deflection in a beam is a serviceability design criterion and occurs generally at or close to the mid-span. This paper presents a methodology using neural networks for rapid prediction of mid-span deflections in reinforced concrete beams subjected to service load. The closed form expressions are further obtained from the trained neural networks. The closed form expressions take into account cracking in concrete at in-span and at near the interior supports and tension stiffening effect. The expressions predict the inelastic deflections (incorporating the concrete cracking) from the elastic moments and the elastic deflections (neglecting the concrete cracking). Five separate neural networks are trained since these have been postulated to represent all beams having any number of spans. The training, validating, and testing data sets for the neural networks are generated using an analytical-numerical procedure of analysis. The proposed expressions have been verified by comparison with the experimental results reported elsewhere and also by comparison with the finite element method (FEM). The proposed expressions, at minimal input data and minimal computation effort, yield results that are close to FEM results. The expressions can be used in every day design since the errors are found to be small.

• Journal of Biosystems Engineering
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• v.16 no.3
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• pp.239-247
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• 1991

This study was conducted to investigate the feasbility of continuously variable V-belt transmission(CVVT) as automatic power transmission system of combine harvesters. An experimental set-up for testing the performance of CVVT and the automatic transmission system was designed and used to analyze the power transmission characteristics of CVVT. The transmission efficiency of CVVT was increased logarithmically with increase of the load of driven shaft, but was not affected by the speed ratios of transmission. More than 80% of transmission efficiency was obtained in the 25N-m load and more of driven-shaft, and the maximum efficiency was 88~91%. When rapid speed change of the CVVT was attempted, the speed of driven shaft was stabilized within about 0.4 seconds after shift operation in both cases of increasing and decreasing of the speed.

• Steel and Composite Structures
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• v.18 no.3
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• pp.547-563
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• 2015

Deflection in a beam of a composite frame is a serviceability design criterion. This paper presents a methodology for rapid prediction of long-term mid-span deflections of beams in composite frames subjected to service load. Neural networks have been developed to predict the inelastic mid-span deflections in beams of frames (typically for 20 years, considering cracking, and time effects, i.e., creep and shrinkage in concrete) from the elastic moments and elastic mid-span deflections (neglecting cracking, and time effects). These models can be used for frames with any number of bays and stories. The training, validating, and testing data sets for the neural networks are generated using a hybrid analytical-numerical procedure of analysis. Multilayered feed-forward networks have been developed using sigmoid function as an activation function and the back propagation-learning algorithm for training. The proposed neural networks are validated for an example frame of different number of spans and stories and the errors are shown to be small. Sensitivity studies are carried out using the developed neural networks. These studies show the influence of variations of input parameters on the output parameter. The neural networks can be used in every day design as they enable rapid prediction of inelastic mid-span deflections with reasonable accuracy for practical purposes and require computational effort which is a fraction of that required for the available methods.

• International Journal of Fluid Machinery and Systems
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• v.3 no.4
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• pp.352-359
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• 2010

In recent years, the market has shown increasing interest in pump-turbines. The prompt availability of pumped storage plants and the benefits to the power system achieved by peak lopping, providing reserve capacity, and rapid response in frequency control are providing a growing advantage. In this context, there is a need to develop pumpturbines that can reliably withstand dynamic operation modes, fast changes of discharge rate by adjusting the variable diffuser vanes, as well as fast changes from pumping to turbine operation. In the first part of the present study, various flow patterns linked to operation of a pump-turbine system are discussed. In this context, pump and turbine modes are presented separately and different load cases are shown in each operating mode. In order to create modern, competitive pump-turbine designs, this study further explains what design challenges should be considered in defining the geometry of a pump-turbine impeller. The second part of the paper describes an innovative, staggered approach to impeller development, applied to a low head pump-turbine project. The first level of the process consists of optimization strategies based on evolutionary algorithms together with 3D in-viscid flow analysis. In the next stage, the hydraulic behavior of both pump mode and turbine mode is evaluated by solving the full 3D Navier-Stokes equations in combination with a robust turbulence model. Finally, the progress in hydraulic design is demonstrated by model test results that show a significant improvement in hydraulic performance compared to an existing reference design.