• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6D
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• pp.599-605
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• 2012

It is important to decide traffic opening time for construction plan of epoxy asphalt pavement. For this purpose, strength prediction model of epoxy asphalt concrete is required. In this study, Marshall stability was measured according to temperature and time for making strength properties equation. Strength prediction model was developed using chemical kinetics considering temperature variation. The traffic opening time of epoxy asphalt pavement on bridge deck has been predicted using the developed model. The prediction and actual traffic opening times were different by 17-days, because weathers of year 2009-2011 used in prediction model were different from weather of year 2012. When the prediction model used the actually measured temperatures of pavement, the difference between real opening time and prediction opening time was two days. The correlation analysis result between measured strength and prediction strength revealed that the $R^2$ using accurate temperature of pavement was 0.95. An improved precise prediction result is to be obtained if the prediction model uses accurate temperature data of pavement.

• Journal of the Korea Concrete Institute
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• v.15 no.1
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• pp.87-94
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• 2003

Construction plan and strength control have limitations in construction production field because it is difficult to predict the form removal strength and development of specified concrete strength. However, we can have reasonable construction plan and strength control if prediction of concrete strength is available. In this study, firstly, the newly proposed strength prediction model with maturity method was compared with the logistic model to test the adaptability. Secondly, the determination of time of form removal was verified through the new strength prediction model. As the results, it is found that investigation of the activation energy that are used to calculate equivalent age is necessary, and new strength prediction model was proved to be more accurate in the strength prediction than logistic model in the early age. Moreover, the use of new model was more reasonable because it has low SSE and high decisive factor. If we adopt new strength prediction model at construction field, we can expect the reduced period of work through the reduced time of form removal.

• Journal of the Korea Concrete Institute
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• v.14 no.2
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• pp.137-147
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• 2002

Maturity models involving curing temperature and curing ages have been widely used to predict concrete strength, which can accurately estimate concrete strength. However, they may not consider physical quantities such as the characteristics of hydrates and the capillary porosity of microstructures associated with strength development. In order to find out the effects of both factors on a strength increment, the hydration model and the estimation method of the amount of capillary porosity were established, and the compressive strength test of concrete nth various water/cement ratios was carried out considering two test parameters, curing temperature and curing age. In this study, by analyzing the experimental results, a strength estimation model for early-age concrete that can consider the microstructural characteristics such as hydrates and capillary porosity was proposed. Measured compressive strengths were compared with estimated strengths and good agreements were obtained. Consequently, the proposed strength model can estimate compressive strength of concrete with curing age and curing temperature within an acceptable error.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.1338-1342
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• 2005

최근에 하천수질이 악화되고 물 수요량이 증대됨에 따라 사회적으로 하천의 유지관리문제가 중요시되고 있는 실정이다. 보다 효율적인 수질관리를 위해서 수질모델을 이용하여 장래 수질예측결과를 토대로 수질보전 대책 및 오염원 저감 계획을 수립하여야 하는데, 이러한 수질모델의 구성을 위해서는 장기간의 수질 및 유량측정자료의 구축이 선행되어져야하므로 시간적$\cdot$경제적인 어려움이 따르게 된다. 이에 본 연구에서는 영산강을 대상유역으로 선정하고, 영산강 유역 환경청과 영산강 홍수통제소에서 운영$\cdot$관리하는 관측망 자료인 수질자료와 수위자료를 이용하여 수질모델을 구축하고, 장래 수질예측을 실시하였다. 수질예측결과 영산강은 광주하수종말처리장 방류수의 수질농도가 지대한 영향을 미치는 것으로 판단되어 광주하수종말처리장 처리효율에 따른 수질변화를 모의하였다.

• Journal of the Korean earth science society
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• v.30 no.3
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• pp.294-304
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• 2009

A statistical prediction model for the typhoon intensity and track in the Northwestern Pacific area was developed based on the artificial neural network scheme. Specifically, this model is focused on the 5-day prediction after tropical cyclone genesis, and used the CLIPPER parameters (genesis location, intensity, and date), dynamic parameters (vertical wind shear between 200 and 850hPa, upper-level divergence, and lower-level relative vorticity), and thermal parameters (upper-level equivalent potential temperature, ENSO, 200-hPa air temperature, mid-level relative humidity). Based on the characteristics of predictors, a total of seven artificial neural network models were developed. The best one was the case that combined the CLIPPER parameters and thermal parameters. This case showed higher predictability during the summer season than the winter season, and the forecast error also depended on the location: The intensity error rate increases when the genesis location moves to Southeastern area and the track error increases when it moves to Northwestern area. Comparing the predictability with the multiple linear regression model, the artificial neural network model showed better performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.7-13
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• 2018

The strength of concrete is affected significantly not only by the internal influence factors of cement, water, sand, aggregate, and admixture, but also by the external influence factors of concrete placement delay and curing temperature. The objective of this research was to predict the concrete strength considering both the internal and external influence factors when concrete is placed at the construction site. In this study, a concrete strength test was conducted on the 24 combinations of internal and external influence factors, and a neural network model was constructed using the test data. This neural network model can predict the concrete strength considering the external influence factors of the concrete placement delay and curing temperature when concrete is placed at the construction site. Contractors can use the concrete strength prediction neural network model to make concrete more robust to external influence factors during concrete placement at a construction site.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.238-245
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• 2021

In this study, we carried out case study to predict dissolved oxygen (DO) concentration of Nakdong river estuary with LSTM model. we aimed to figure out a optimal model condition and appropriate predictor for prediction in dissolved oxygen concentration with model parameter and predictor as cases. Model parameter case study results showed that Epoch = 300 and Sequence length = 1 showed higher accuracy than other conditions. In predictor case study, it was highest accuracy where DO and Temperature were used as a predictor, it was caused by high correlation between DO concentration and Temperature. From above results, we figured out an appropriate model condition and predictor for prediction in DO concentration of Nakdong river estuary.

• Journal of Korean Association for Spatial Structures
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• v.5 no.2 s.16
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• pp.89-97
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• 2005

As building structures are becoming high-rise, large-scale, and specialized, the use of high-strength materials increase. Therefore, an analytical model is necessary to appropriately predict the shear strength of reinforced concrete (RC) beams with high-strength materials. This study presents a truss model which is able to reasonably predict the shear strength of the RC beams having high-strength steel bars. Test results of 107 RC beams reported in the technical literatures were collected to check the validity of proposed model, TATM, for the shear strength of the RC beams with high-strength reinforcing bars. They were compared to theoretical results obtained from proposed model, TATM, and existing truss models. The experimental results were better predicted by TATM rather than other truss models, and the ratios of experimental results to theoretical results obtained from TATM were almost constant regardless of the yield strengths of tension and shear reinforcements.

• Journal of the Korean Geotechnical Society
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• v.19 no.4
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• pp.287-298
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• 2003

The feasibility of using neural networks to model the complex relationship between piezocone measurements and the undrained shear strength of clays has been investigated. A three layered back propagation neural network model was developed based on actual undrained shear strengths, which were obtained from the isotrpoically and anisotrpoically consolidated triaxial compression test(CIUC and CAUC), and piezocone measurements compiled from various locations around the world. It was validated by comparing model predictions with measured values about new piezocone data, which were not previously employed during development of model. Performance of the neural network model was compared with conventional empirical method, direct correlation method, and theoretical method. It was found that the neural network model is not only capable of inferring a complex relationship between piezocone measurements and the undrained shear strength of clays but also gives a more precise and reliable undrained shear strength than theoretical and empirical approaches. Furthermore, neural network model has a possibility to be a generalized relationship between piezocone measurements and undrained shear strength over the various places and countries, while the present empirical correlations present the site specific relationship.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06c
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• pp.99-102
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• 1998

본 연구에서는 연속음 인식과 합성을 위한 경계강도 예측 모델을 제안한다. 운율 경계 강도는 음성 합성에서는 운율구 사이의 휴지기의 길이 조절로 합성음의 자연도에 기여를 하고 연속음 인식에서는 인식과정에서 나타나는 후보문장의 선별 과정에 특징변수가 되어 인식률 향상에 큰 역할을 한다. 음성학적으로 발화된 문장은 큰 경계 단위로 볼 때 운율구 형태로 이루어졌다고 볼 수 있으며 구의 경계는 문장의 문법적인 특징과 관련을 지을 수 있게 된다. 본 논문에서는 운율 경계 강도 수준을 4로 하고 문법적인 특징으로는 트리구조 방법으로 결정된 오른쪽 가지의 수식의 깊이(rd)와 link grammar방법으로 결정된 음절수(syl), 연결거리(torig)를 bigram 모형과 결합하여 운율적 경계 강도를 예측한다. 예측 모형으로는 다중 회귀 모형과 Marcov 모형을 제안한다. 이들 모형으로 낭독체 200 문장에 대해 실험한 결과 76%로 경계 강도를 예측할 수 있었다.