• 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 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.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06e
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• pp.81-84
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• 1998

본 논문에서는 문장의 문법 구조로부터 운율 경계 강도를 효율적으로 예측하기 위해서, 문법 정보의 세밀함에 따라 품사셋을 3단계로 설정하였다. 그리고 운율 경계 강도를 예측하는데 있어서 어떠한 품사셋이 최적인가를 알아보기 위해 150문장의 코퍼스를 구축하였으며, 세 종류의 품사셋에 대해 코퍼스를 수작업으로 품사분석을 하였다. 청취실험으로 결정한 운율 경계 강도를 바탕으로 확률론적인 모델링 방법을 사용하여 예측하는 실험을 하였다. 이러한 예측결과를 평가 비교하여 최적의 품사셋을 정하였다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.251-258
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• 2000

인공신경망에 의해 부도예측을 하기 위해서는 여러 개의 재무비율을 입력변수 즉, 입력노드로 이용하는데, 이 가운데 적절한 입력노드를 선정하는 일은 예측력을 결정하는데 있어서 매우 중요하다. 본 연구에서는 새로운 입력노드 선정 휴리스틱을 제안하기 위하여 적절한 훈련이 끝난 인공신경망 모델에서 각 입력노드와 연결되는 가중치들의 합에 대한 절대값인 연결강도가 작은 경우 해당 노드는 출력값에 대한 설명력이 약할 것이다라는 연결강도판별 명제를 제시한다. 즉, 연결강도가 연결강도임계치보다 작은 입력노드는 제거 대상으로 분류할 수 있을 것이고, 이들 노드를 제외한 입력노드는 그렇지 않은 경우보다 더 나은 예측력을 보여 줄 수 있을 것이다. 연결강도판별 명제를 실증적으로 입증하기 위해 본 연구에서는 연결강도판별 선처리 과정에 대한 방법론을 제안하고 제안된 방법론에 의해 부도예측을 실시하여 아무런 선처리를 거치지 않은 모형과 비교하였고, 또 기존의 입력변수 선정방식 중에 하나인 의사결정트리 방식에 의한 입력변수 선정 모형과도 비교하여 더 나은 결과를 얻었다.

• 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.

• Composites Research
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• v.16 no.5
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• pp.15-20
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• 2003

In this paper, the fracture strength of the surface damaged laminates was predicted by applying the fracture strengths of the unflawed and flawed laminates. For prediction, the theoretical equation about the fracture strength of laminates was simplified applying classical laminate theory and was applied to the surface damaged laminates. Lagace's and Tsai's experimental data were used for verifying the theoretical equation. Moreover, to verify the theoretical prediction, an experiment was performed. Surface unflawed laminate and flawed laminates were fabricated and the experiments were made and these results were compared with theoretical predictions. The specimens' fiber direction was same to the tensile direction and the theoretical predictions and the experimental results were showed good agreement. Therefore, by this equation, the fracture strength of structures made of composites will be able to be predicted when the surface of the structures was damaged.

• 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.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.359-368
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• 2001

본 연구에서는 부도예측용 인공신경망의 입력노드 선정을 위한 휴리스틱으로 연결강도분석 접근법을 제안한다. 연결강도분석은 학습이 끝난 인공신경망에서 입력노드와 은닉노드와 연결된 가중치의 절대값 즉, 연결강도를 분석하여 입력변수를 선정하는 접근법으로, 본 연구에서는 약체연결뉴론제거법, 강체연결뉴론선택법 그리고 이 두 기법을 통합한 통합 연결강도 모형을 제안하여 각각 의사결정 트리 및 다변량판별분석에 의해 선정된 입력변수를 이용한 인공신경망 모형과 예측율을 비교한다. 실험 결과 본 연구에서 제안하고 있는 방법론이 의사결정트리나 다다변량판별분석 기법 보다 높은 예측율을 보여 주었다. 특히 두 기법의 통합연결강도 모형의 경우에는 다른 단일 기법보다 높은 예측율을 보이고 있다.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.5
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• pp.135-140
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• 2021

In this study, the concrete aggregate shape features were extracted from the cross-section of a normal concrete strength cylinder, and the compressive strength of the cylinder was predicted using artificial neural networks and image processing technology. The distance-angle features of aggregates, along with general aggregate shape features such as area, perimeter, major/minor axis lengths, etc., were numerically expressed and utilized for the compressive strength prediction. The results showed that compressive strength can be predicted using only the aggregate shape features of the cross-section without using major variables. The artificial neural network algorithm was able to predict concrete compressive strength within a range of 4.43% relative error between the predicted strength and test results. This experimental study indicates that various material properties such as rheology, and tensile strength of concrete can be predicted by utilizing aggregate shape features.

• 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.