• 응용통계연구
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• 제15권2호
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• pp.395-403
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• 2002

반응표면방법론에서의 세번째 단계에서는 일차모형이 가정되고, 반응표면의 곡선효과는 중앙점과 2수준 부분실시법에서의 실험을 통해서 검토된다. 참모형이 2차 모형인 경우를 가정하자. 최적실험계획을 선택하기 위해서 Box와 Draper(1959)는 관심영역에서 예측치 y(x)의 평균제곱오차를 적분한 값인 가중평균제곱오차(AMSE)를 최소화 시키는 최적실험계획 기준을 제안하였다. AMSE는 예측치의 가중분산과 가중제곱편의 량의 합으로 분할될 수 있다. AMSE는 실험계획 적률과 참모형의 회귀계수들의 값에 종속되어서 가중평균제곱오차를 최 소화하는 실험 계획을 찾기는 불가능하다. 실용적인 대안으로 Box와 Draper(1959)는 가중제곱편의 량을 최소화하는 실험계획을 제안했고, 이 실험계획의 상자점들이 중앙점을 향해서 축소됨을 보였다. 이 논문에서는 표준화된 회귀계수들의 값에 대해서 실험계획의 최소효율을 최대화하는 강건한 실험계획을 제안한다.

• Wind and Structures
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• 제36권4호
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• pp.237-247
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• 2023

The emergence of high-rise buildings has necessitated frequent structural health monitoring and maintenance for safety reasons. Wind causes damage and structural changes on tall structures; thus, safe structures should be designed. The pressure developed on tall buildings has been utilized in previous research studies to assess the impacts of wind on structures. The wind tunnel test is a primary research method commonly used to quantify the aerodynamic characteristics of high-rise buildings. Wind pressure is measured by placing pressure sensor taps at different locations on tall buildings, and the collected data are used for analysis. However, sensors may malfunction and produce erroneous data; these data losses make it difficult to analyze aerodynamic properties. Therefore, it is essential to generate missing data relative to the original data obtained from neighboring pressure sensor taps at various intervals. This study proposes a deep learning-based, deep convolutional generative adversarial network (DCGAN) to restore missing data associated with faulty pressure sensors installed on high-rise buildings. The performance of the proposed DCGAN is validated by using a standard imputation model known as the generative adversarial imputation network (GAIN). The average mean-square error (AMSE) and average R-squared (ARSE) are used as performance metrics. The calculated ARSE values by DCGAN on the building model's front, backside, left, and right sides are 0.970, 0.972, 0.984 and 0.978, respectively. The AMSE produced by DCGAN on four sides of the building model is 0.008, 0.010, 0.015 and 0.014. The average standard deviation of the actual measures of the pressure sensors on four sides of the model were 0.1738, 0.1758, 0.2234 and 0.2278. The average standard deviation of the pressure values generated by the proposed DCGAN imputation model was closer to that of the measured actual with values of 0.1736,0.1746,0.2191, and 0.2239 on four sides, respectively. In comparison, the standard deviation of the values predicted by GAIN are 0.1726,0.1735,0.2161, and 0.2209, which is far from actual values. The results demonstrate that DCGAN model fits better for data imputation than the GAIN model with improved accuracy and fewer error rates. Additionally, the DCGAN is utilized to estimate the wind pressure in regions of buildings where no pressure sensor taps are available; the model yielded greater prediction accuracy than GAIN.