• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6A
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• pp.799-808
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• 2008

The analysis of vehicular loads reflecting the domestic traffic circumstances is necessary for the development of adequate design live load models in the analysis and design of cable-supported bridges or the development of fatigue load models to predict the remaining lifespan of the bridges. This study intends to develop an ANN(artificial neural network)-based Bridge WIM system and Influence line-based Bridge WIM system for obtaining information concerning the loads conditions of vehicles crossing bridge structures by exploiting the signals measured by strain gauges installed at the bottom surface of the bridge superstructure. This study relies on experimental data corresponding to the travelling of hundreds of random vehicles rather than on theoretical data generated through numerical simulations to secure data sets for the training and test of the ANN. In addition, data acquired from 3 types of vehicles weighed statically at measurement station and then crossing the bridge repeatedly are also exploited to examine the accuracy of the trained ANN. The results obtained through the proposed ANN-based analysis method, the influence line analysis method considering the local behavior of the bridge are compared for an example cable-stayed bridge. In view of the results related to the cable-stayed bridge, the cross beam ANN analysis method appears to provide more remarkable load analysis results than the cross beam influence line method.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.6
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• pp.434-449
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• 2019

In this study, we develop a new cross-shore sediment module which takes the effect of infra-gravity waves of bound mode, and boundary layer streaming on the sediment transport into account besides the well-known asymmetry and under-tow. In doing so, the effect of individual random waves occurring during the unit observation period of 1 hr on sediment transport is also fully taken into account. To demonstrate how the individual random waves would affect the sediment transport, we numerically simulate the non-linear shoaling process of random wavers over the beach of uniform slope. Numerical results show that with the consistent frequency Boussinesq Eq. the application of which is lately extended to surf zone, we could simulate the saw-tooth profile observed without exception over the surf zone, infra-gravity waves of bound mode, and boundary-layer streaming accurately enough. It is also shown that when yearly highest random waves are modeled by the equivalent nonlinear uniform waves, the maximum cross-shore transport rate well exceeds the one where the randomness is fully taken into account as much as three times. Besides, in order to optimize the free parameter K involved in the long-shore sediment module, we carry out the numerical simulation to trace the yearly shoreline change of Mang-Bang beach from 2017.4.26 to 2018.4.20 as well, and proceeds to optimize the K by comparing the traced shoreline change with the measured one. Numerical results show that the optimized K for Mang-Bang beach would be 0.17. With K = 0.17, via yearly grand circulation process comprising severe erosion by consecutively occurring yearly highest waves at the end of October, and gradual recovery over the winter and spring by swell, the advance of shore-line at the northern and southern ends of Mang-Bang beach by 18 m, and the retreat of shore-line by 2.4 m at the middle of Mang-Bang beach can be successfully duplicated in the numerical simulation.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.1
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• pp.67-76
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• 2024

In this paper, we present a DIP-MLS testing method that combines digital image processing with a rigid body-based MLS differencing approach to measure mechanical variables and analyze the impact of target location and image resolution. This method assesses the displacement of the target attached to the sample through digital image processing and allocates this displacement to the node displacement of the MLS differencing method, which solely employs nodes to calculate mechanical variables such as stress and strain of the studied object. We propose an effective method to measure the displacement of the target's center of gravity using digital image processing. The calculation of mechanical variables through the MLS differencing method, incorporating image-based target displacement, facilitates easy computation of mechanical variables at arbitrary positions without constraints from meshes or grids. This is achieved by acquiring the accurate displacement history of the test specimen and utilizing the displacement of tracking points with low rigidity. The developed testing method was validated by comparing the measurement results of the sensor with those of the DIP-MLS testing method in a three-point bending test of a rubber beam. Additionally, numerical analysis results simulated only by the MLS differencing method were compared, confirming that the developed method accurately reproduces the actual test and shows good agreement with numerical analysis results before significant deformation. Furthermore, we analyzed the effects of boundary points by applying 46 tracking points, including corner points, to the DIP-MLS testing method. This was compared with using only the internal points of the target, determining the optimal image resolution for this testing method. Through this, we demonstrated that the developed method efficiently addresses the limitations of direct experiments or existing mesh-based simulations. It also suggests that digitalization of the experimental-simulation process is achievable to a considerable extent.

• Journal of the Korea Concrete Institute
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• v.25 no.5
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• pp.485-496
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• 2013

In recent years, frequent terror or military attacks by explosion or impact accidents have occurred. Examplary case of these attacks were World Trade Center collapse and US Department of Defense Pentagon attack on Sept. 11 of 2001. These attacks of the civil infrastructure have induced numerous casualties and property damage, which raised public concerns and anxiety of potential terrorist attacks. However, a existing design procedure for civil infrastructures do not consider a protective design for extreme loading scenario. Also, the extreme loading researches of prestressed concrete (PSC) member, which widely used for nuclear containment vessel, gas tank, bridges, and tunnel, are insufficient due to experimental limitations of loading characteristics. To protect concrete structures against extreme loading such as explosion and impact with high strain rate, understanding of the effect, characteristic, and propagation mechanism of extreme loadings on structures is needed. Therefore, in this paper, to evaluate the impact resistance capacity and its protective performance of bi-directional unbonded prestressed concrete member, impact tests were carried out on $1400mm{\times}1000mm{\times}300mm$ for reinforced concrete (RC), prestressed concrete without rebar (PS), prestressed concrete with rebar (PSR, general PSC) specimens. According to test site conditions, impact tests were performed with 14 kN impactor with drop height of 10 m, 5 m, 4 m for preliminary tests and 3.5 m for main tests. Also, in this study, the procedure, layout, and measurement system of impact tests were established. The impact resistance capacity was measured using crack patterns, damage rates, measuring value such as displacement, acceleration, and residual structural strength. The results can be used as basic research references for related research areas, which include protective design and impact numerical simulation under impact loading.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.