• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.619-630
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• 2022

The concrete mix design and compressive strength evaluation are used as basic data for the durability of sustainable structures. However, the recent diversification of mixing factors has created difficulties in calculating the correct mixing factor or setting the reference value concrete mixing design. The purpose of this study is to design a predictive model of bidirectional analysis that calculates the mixing elements of ternary concrete using deep learning, one of the artificial intelligence techniques. For the DNN-based predictive model for calculating the concrete mixing factor, performance evaluation and comparison were performed using a total of 8 models with the number of layers and the number of hidden neurons as variables. The combination calculation result was output. As a result of the model's performance evaluation, an average error rate of about 1.423% for the concrete compressive strength factor was achieved. and an average MAPE error of 8.22% for the prediction of the ternary concrete mixing factor was satisfied. Through comparing the performance evaluation for each structure of the DNN model, the DNN5L-2048 model showed the highest performance for all compounding factors. Using the learned DNN model, the prediction of the ternary concrete formulation table with the required compressive strength of 30 and 50 MPa was carried out. The verification process through the expansion of the data set for learning and a comparison between the actual concrete mix table and the DNN model output concrete mix table is necessary.

• Journal of IKEEE
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• v.27 no.3
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• pp.345-349
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• 2023

In this paper, we propose a study on the development of deep learning structure for defective pixel detection of next-generation smart LED display board using imaging device. In this research, a technique utilizing imaging devices and deep learning is introduced to automatically detect defects in outdoor LED billboards. Through this approach, the effective management of LED billboards and the resolution of various errors and issues are aimed. The research process consists of three stages. Firstly, the planarized image data of the billboard is processed through calibration to completely remove the background and undergo necessary preprocessing to generate a training dataset. Secondly, the generated dataset is employed to train an object recognition network. This network is composed of a Backbone and a Head. The Backbone employs CSP-Darknet to extract feature maps, while the Head utilizes extracted feature maps as the basis for object detection. Throughout this process, the network is adjusted to align the Confidence score and Intersection over Union (IoU) error, sustaining continuous learning. In the third stage, the created model is employed to automatically detect defective pixels on actual outdoor LED billboards. The proposed method, applied in this paper, yielded results from accredited measurement experiments that achieved 100% detection of defective pixels on real LED billboards. This confirms the improved efficiency in managing and maintaining LED billboards. Such research findings are anticipated to bring about a revolutionary advancement in the management of LED billboards.

• Journal of the Korean Geotechnical Society
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• v.38 no.3
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• pp.35-42
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• 2022

The N-value from the Standard Penetration Test (SPT), which is one of the representative in-situ test, is an important index that provides basic geological information and the depth of the bearing layer for the design of geotechnical structures. In the aspect of time and cost-effectiveness, there is a need to carry out a representative sampling test. However, the various variability and uncertainty are existing in the soil layer, so it is difficult to grasp the characteristics of the entire field from the limited test results. Thus the spatial interpolation techniques such as Kriging and IDW (inverse distance weighted) have been used for predicting unknown point from existing data. Recently, in order to increase the accuracy of interpolation results, studies that combine the geotechnics and deep learning method have been conducted. In this study, based on the SPT results of about 22,000 holes of ground survey, a comparative study was conducted to predict the depth of the bearing layer using deep learning methods and IDW. The average error among the prediction results of the bearing layer of each analysis model was 3.01 m for IDW, 3.22 m and 2.46 m for fully connected network and PointNet, respectively. The standard deviation was 3.99 for IDW, 3.95 and 3.54 for fully connected network and PointNet. As a result, the point net deep learing algorithm showed improved results compared to IDW and other deep learning method.

• Journal of the Korean Recycled Construction Resources Institute
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• v.11 no.4
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• pp.355-363
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• 2023

In safety assessment of a aged bridge, dynamic characteristics and displacement are directly related to the rigidity of the structural system, especially displacement is the most important factor as the physical quantity that the bridge user can directly detect. However, in order to measure the displacement of the bridge, it is difficult to install displacement sensors at the bottom of the bridge and conduct traffic blocking and loading tests, resulting in increased costs or impossible measurements depending on the bridge's environment. In this study, a method of measuring the displacement of a bridge using only accelerometers without installing displacement sensors and ambient vibration without a loading test was proposed. For the analysis of bridge dynamic characteristics and displacement using ambient vibration, the mode shape and natural frequency of the bridge were extracted using a TDD technique known to enable quick analysis with simple calculations, and the unit load displacement of the bridge was analyzed through flexibility analysis to calculate static displacement. To verify this proposed technology, an on-site test was conducted on C Bridge, and the results were compared with the measured values of the loading test and the structural analysis data. As a result, it was confirmed that the mode shape and natural frequency were 0.42 to 1.13 % error ratio, and the maximum displacement at the main span was 3.58 % error ratio. Therefore, the proposed technology can be used as a basis data for indirectly determine the safety of the bridge by comparing the amount of displacement compared to the design and analysis values by estimating the displacement of the bridge that could not be measured due to the difficulty of installing displacement sensors.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.6
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• pp.391-398
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• 2023

In this paper, a penalized maximum likelihood estimation (PMLE) method that applies a penalty to increase the accuracy of a basis-screening-based Kriging model (BSKM) is introduced. The maximum order and set of basis functions used in the BSKM are determined according to their importance. In this regard, the cross-validation error (CVE) for the basis functions is employed as an indicator of importance. When constructing the Kriging model (KM), the maximum order of basis functions is determined, the importance of each basis function is evaluated according to the corresponding maximum order, and finally the optimal set of basis functions is determined. This optimal set is created by adding basis functions one by one in order of importance until the CVE of the KM is minimized. In this process, the KM must be generated repeatedly. Simultaneously, hyper-parameters representing correlations between datasets must be calculated through the maximum likelihood evaluation method. Given that the optimal set of basis functions depends on such hyper-parameters, it has a significant impact on the accuracy of the KM. The PMLE method is applied to accurately calculate hyper-parameters. It was confirmed that the accuracy of a BSKM can be improved by applying it to Branin-Hoo problem.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.120-129
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• 2023

Hanger cable tension is a major response that can determine the integrity and safety of suspension bridges. In general, the vibration method is used to estimate hanger cable tension on operational suspension bridges. It measures natural frequencies from hanger cables and indirectly estimates tension using the geometry conditions of the hanger cables. This study estimated the hanger cable tension of the Palyeong Bridge using a vision-based system. The vision-based system used digital camcorders and tripods considering the convenience and economic efficiency of measurement. Measuring the natural frequencies for high-order modes required for the vibration method is difficult because the hanger cable response measured using the vision-based system is displacement-based. Therefore, this study proposed a back analysis technique for estimating tension using the natural frequencies of low-order modes. Optimization for the back analysis technique was performed by defining the difference between the natural frequencies of hanger cables measured in the field and those calculated using finite element analysis as the objective function. The direct search method that does not require the partial derivatives of the objective function was applied as the optimization method. The reliability and accuracy of the back analysis technique were verified by comparing the tension calculated using the method with that estimated using the vibration method. Tension was accurately estimated using the natural frequencies of low-order modes by applying the back analysis technique.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.97-115
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• 2023

In recent years, the structure of the population has been changing rapidly, with a declining birthrate and aging population, and the inequality of population distribution is expanding. At this point, changes in population estimation methods are required, and more accurate estimates are needed at the subregional level. This study aims to estimate the population in 2040 at the 500m grid level by applying an explainable machine learning to Busan in order to respond to this need for a change in population estimation method. Comparing the results of population estimation by applying the explainable machine learning and the cohort component method, we found that the machine learning produces lower errors and is more applicable to estimating areas with large population changes. This is because machine learning can account for a combination of variables that are likely to affect demographic change. Overestimated population values in a declining population period are likely to cause problems in urban planning, such as inefficiency of investment and overinvestment in certain sectors, resulting in a decrease in quality in other sectors. Underestimated population values can also accelerate the shrinkage of cities and reduce the quality of life, so there is a need to develop appropriate population estimation methods and alternatives.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.1
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• pp.32-38
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• 2024

In the case of apartment building, it is difficult to conduct non-destructive testing due to the actual presence of people and the dust and noise generated during the core test, so inspections are performed each time in the common area and underground parking lot, and the tests are conducted on the finishing material rather than on the concrete surface due to low-cost orders. As the process progresses, poor inspection is inevitable. In addition, the proposed formulas for strength estimation have large fluctuations depending on the differences in test conditions and environments, and even if they show the same measured value, the deviation between each proposed formula is large, making it difficult to accurately estimate strength, making it difficult to use. Accordingly, we would like to select finishing materials mainly used in apartment complexes and compare and evaluate the compressive strength of concrete according to the type of finishing material by using non-destructive testing methods directly on the finishing materials without removing the finishing materials. The reliability evaluation results of the estimated compressive strength of concrete using the ultrasonic velocity method according to the type of finishing material are as follows. The error rate between the estimated compressive strength and compressive strength derived through the ultrasonic velocity method shows a wide range of variation, ranging from 21.83% to 58.89%. The effect of the presence or absence of finishing materials on the estimated compressive strength was found to be insignificant. Accordingly, it is necessary to select more types of finishing materials and study ultrasonic velocity methods according to the presence or absence of finishing materials, and to study estimation techniques that can increase reliability.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.1
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• pp.98-106
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• 2024

Due to advancements in construction technology and analytical tools, an increasing number of cable-stayed bridges have been designed and constructed in recent years. A cable is a structural element that primarily transmits the main load of a cable-stayed bridge and plays the most crucial role in reflecting the overall condition of the entire bridge system. In this study, a vision-based method was applied to estimate the tension of the stay cables located at a long distance. To measure the response of a cable using a vision-based method, it is necessary to install feature points or targets on the cable. However, depending on the location of the point to be measured, there may be no feature points in the cable, and there may also be limitations in installing the target on the cable. Hence, it is necessary to find a way to measure cable response that overcomes the limitations of existing vision-based methods. This study proposes a method for measuring cable responses by utilizing the characteristics of cable shape. The proposed method involved extracting the cable shape from the acquired image and determining the center of the extracted cable shape to measure the cable response. The extracted natural frequencies of the vibration mode were obtained using the measured responses, and the tension was estimated by applying them to the vibration method. To verify the reliability of the vision-based method, cable images were obtained from the Hwatae Bridge in service under ambient vibration conditions. The reliability of the method proposed in this study was confirmed by applying it to the vibration method using a vision-based approach, resulting in estimated tensions with an error of less than 1% compared to tensions estimated using an accelerometer.

• Journal of the Korean Society of Radiology
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• v.11 no.7
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• pp.547-553
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• 2017

In the current medical field, lead is widely used as a radiation shield. However, the lead weight is very heavy, so wearing protective clothing such as apron is difficult to wear for long periods of time and there is a problem with the danger of lethal toxicity in humans. Recently, many studies have been conducted to develop substitute materials of lead to resolve these problems. As a substitute materials for lead, barium(Ba) and iodine(I) have excellent shielding ability. But, It has characteristics emitting characteristic X-rays from the energy area near 30 keV. For patients or radiation workers, shielding materials is often made into contact with the human body. Therefore, the characteristic X-rays generated by the shielding material are directly exposured in the human body, which increases the risk of increasing radiation absorbed dose. In this study, we have developed the FLUKA transport code, one of the most suitable elements of radiation transport codes, to remove the characteristic X-rays generated by barium or iodine. We have verified the reliability of the shielding fraction of the structure of the structure shielding by comparing with the MCPDX simulations conducted as a prior study. Using the MCNPX and FLUKA, the double layer shielding structures with the various thickness combination consisting of barium sulphate ($BaSO_4$) and bismuth oxide($Bi_2O_3$) are designed. The accuracy of the type shown in IEC 61331-1 was geometrically identical to the simulation. In addition, the transmission spectrum and absorbed dose of the shielding material for the successive x-rays of 120 kVp spectra were compared with lead. In results, $0.3mm-BaSO_4/0.3mm-Bi_2O_3$ and $0.1mm-BaSO_4/0.5mm-Bi_2O_3$ structures have been absorbed in both 33 keV and 37 keV characteristic X-rays. In addition, for high-energy X-rays greater than 90 keV, the shielding efficiency was shown close to lead. Also, the transport code of the FLUKA's photon transport code was showed cut-off on low-energy X-rays(below 33keV) and is limited to computerized X-rays of the low-energy X-rays. But, In high-energy areas above 40 keV, the relative error with MCNPX was found to be highly reliable within 6 %.