• Steel and Composite Structures
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• v.50 no.5
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• pp.515-529
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• 2024

Cold-formed steel (CFS) is a popular choice for construction due to its low cost, durability, sustainability, resistance to high environmental and seismic pressures, and ease of installation. The beam-column connections in residential and medium-rise structures are formed using self-drilling screws that connect two CFS channel sections and a gusset plate. In order to increase the moment capacity of these CFS screwed beam-column connections, stiffeners are often placed on the web area of each single channel. However, there is limited literature on studying the effects of stiffeners on the moment capacity of CFS screwed beam-column connections. Hence, this paper proposes a new test approach for determining the moment capacity of CFS screwed beam-column couplings. This study describes an experimental test programme consisting of eight novel experimental tests. The effect of stiffeners, beam thickness, and gusset plate thickness on the structural behaviour of CFS screwed beam-column connections is investigated. Besides, nonlinear elasto-plastic finite element (FE) models were developed and validated against experimental test data. It found that there was reasonable agreement in terms of moment capacity and failure mode prediction. From the experimental and numerical investigation, it found that the increase in gusset plate or beam thickness and the use of stiffeners have no significant effect on the structural behaviour, moment capacity, or rotational capacity of joints exhibiting the same collapse behaviour; however, the capacity or energy absorption capacities have increased in joints whose failure behaviour varies with increasing thickness or using stiffeners. Besides, the thickness change has little impact on the initial stiffness.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.56-63
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• 2015

Several fields of science have demanded large-scale workflow support, which requires thousands of CPU cores or more. In order to support such large-scale scientific workflows, high capacity parallel systems such as supercomputers are widely used. In order to increase the utilization of these systems, most schedulers use backfilling policy: Small jobs are moved ahead to fill in holes in the schedule when large jobs do not delay. Since an estimate of the runtime is necessary for backfilling, most parallel systems use user's estimated runtime. However, it is found to be extremely inaccurate because users overestimate their jobs. Therefore, in this paper, we propose a novel system for the runtime prediction based on workload-aware clustering with the goal of improving prediction performance. The proposed method for runtime prediction of parallel applications consists of three main phases. First, a feature selection based on factor analysis is performed to identify important input features. Then, it performs a clustering analysis of history data based on self-organizing map which is followed by hierarchical clustering for finding the clustering boundaries from the weight vectors. Finally, prediction models are constructed using support vector regression with the clustered workload data. Multiple prediction models for each clustered data pattern can reduce the error rate compared with a single model for the whole data pattern. In the experiments, we use workload logs on parallel systems (i.e., iPSC, LANL-CM5, SDSC-Par95, SDSC-Par96, and CTC-SP2) to evaluate the effectiveness of our approach. Comparing with other techniques, experimental results show that the proposed method improves the accuracy up to 69.08%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• Journal of the Earthquake Engineering Society of Korea
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• v.10 no.2 s.48
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• pp.1-10
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• 2006

Reinforced concrete bridge columns with relatively small aspect ratio show flexure-shear behavior, which is flexural behavior at initial and medium displacement stages and shear failure at final stage. Since the columns with flexure-shear failure have lower ductility than those with flexural failure, shear capacity curve models shall be applied as well as flexural capacity curve in order to determine ultimate displacement for seismic design or performance evaluation. In this paper, a modified shear capacity curve model is proposed and compared with the other models such as the CALTRANS model, Aschheim et al.'s model, and Priestley et al.'s model. Four shear capacity curve models are applied to the 4 full scale circular bridge column test results and the accuracy of each model is discussed. It may not be fully adequate to drive a final decision from the application to the limited number of test results, however the proposed model provides the better prediction of failure mode and ultimate displacement than the other models for the selected column test results.

• Journal of the Korean Geotechnical Society
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• v.29 no.1
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• pp.121-133
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• 2013

This paper presents the results of an investigation into the pull-out capacity characteristics of screw anchor piles. Theoretical background of screw anchor pile (SAP) was first discussed. A series of reduced-scale model tests were performed on a number of cases with different SAP geometries such as pitch and diameter of screw as well as relative density of the model ground. The applicability of the pull-out capacity prediction equations were also examined based on the test results. It was shown that the pitch of screw has negligible effect on the pull-out capacity, while the diameter of screw has relatively large effect on pull-out capacity under a given condition. Practical implications of the findings from this study are discussed in great detail.

• Journal of the Korean Geotechnical Society
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• v.34 no.6
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• pp.37-47
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• 2018

In this study, 34 laboratory load test data were collected, and analyzed to propose the equations for predicting ultimate bearing capacity of sand compaction pile (SCP) and gravel compaction pile (GCP) reinforced clay. The collected data were compared with the ultimate bearing capacity estimated by existing theoretical equations, and the prediction accuracy of the existing theoretical equations was identified. Also, multiple regression analysis was performed to predict the ultimate bearing capacity, and the most efficient number and type of input variables were selected through error evaluation by leave-one-out cross validation. Finally, the multiple regression equations for estimating the ultimate bearing capacity of laboratory load test for SCP and GCP were proposed, and their performance was evaluated.

• Computers and Concrete
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• v.2 no.1
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• pp.79-96
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• 2005

The use of high-strength concrete (HSC) has significantly increased over the last decade, especially in offshore structures, long-span bridges, and tall buildings. The behavior of such concrete is noticeably different from that of normal-strength concrete (NSC) due to its different microstructure and mode of failure. In particular, the shear capacity of structural members made of HSC is a concern and must be carefully evaluated. The shear fracture surface in HSC members is usually trans-granular (propagates across coarse aggregates) and is therefore smoother than that in NSC members, which reduces the effect of shear transfer mechanisms through aggregate interlock across cracks, thus reducing the ultimate shear strength. Current code provisions for shear design are mainly based on experimental results obtained on NSC members having compressive strength of up to 50MPa. The validity of such methods to calculate the shear strength of HSC members is still questionable. In this study, a new approach based on artificial neural networks (ANNs) was used to predict the shear capacity of NSC and HSC beams without shear reinforcement. Shear capacities predicted by the ANN model were compared to those of five other methods commonly used in shear investigations: the ACI method, the CSA simplified method, Response 2000, Eurocode-2, and Zsutty's method. A sensitivity analysis was conducted to evaluate the ability of ANNs to capture the effect of main shear design parameters (concrete compressive strength, amount of longitudinal reinforcement, beam size, and shear span to depth ratio) on the shear capacity of reinforced NSC and HSC beams. It was found that the ANN model outperformed all other considered methods, providing more accurate results of shear capacity, and better capturing the effect of basic shear design parameters. Therefore, it offers an efficient alternative to evaluate the shear capacity of NSC and HSC members without stirrups.

• Computers and Concrete
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• v.24 no.2
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• pp.159-172
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• 2019

This paper presents an investigation into the flexural behavior of reinforced concrete (RC) beams retrofitted by ultra-high performance fiber-reinforced concrete (UHPFRC) layers. The experimental study has been conducted in two parts. In the first part, four methods of retrofitting with UHPFRC layers in both the up and down sides of the beams have been proposed and their efficiency in the bonding of the normal concrete and ultra-high performance fiber-reinforced concrete has been discussed. The results showed that using the grooving method and the pre-casted UHPFRC layers in comparison with the sandblasting method and the cast-in-place UHPFRC layers leads to increase the load carrying capacity and the energy absorption capacity and causes high bond strength between two concretes. In the second part of the experimental study, the tests have been conducted on the beams with single UHPFRC layer in the down side and in the up side, using the effective retrofitting method chosen from the first part. The results are compared with those of non-retrofitted beam and the results of the first part of experimental study. The results showed that the retrofitted beam with two UHPFRC layers in the up and down sides has the highest energy absorption and load carrying capacity. A finite element analysis was applied to prediction the flexural behavior of the composite beams. A good agreement was achieved between the finite element and experimental results. Finally, a parametric study was carried out on full-scale retrofitted beams. The results indicated that in all retrofitted beams with UHPFRC in single and two sides, increasing of the UHPFRC layer thickness causes the load carrying capacity to be increased. Also, increases of the normal concrete compressive strength improved the cracking load of the beams.

• The KSFM Journal of Fluid Machinery
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• v.15 no.4
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• pp.61-66
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• 2012

Nowadays, precise prediction of the pump performance becomes more important than ever before in high-value industries such as power plants and large ships. The power consumed in such pumps of large head and capacity definitely affects the efficiency of the entire system. In this study, we report the theoretical and CFD results used in prediction of the performance change caused by the reduction of impeller diameter. We have found that the theoretical calculation is somehow useful at least in estimating the very beginning condition for the CFD main calculation.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.2
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• pp.185-190
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• 2013

The previous steganographic methods by using the interpolation were difficult to estimate the distortion because the size of cover image is extended by interpolation algorithms. In this paper, to solve the problems of previous methods proposed the improved steganographic method based on the pixel replacement algorithms. In our method, we cannot extend a cover image, but also can estimate exactly the distortion of the stego-images. In the experimental results, the estimated distortion and embedding capacity of stego-image are shown on three pixel replacement methods.