• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2022년도 봄 학술논문 발표대회
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• pp.69-70
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• 2022

The domestic process planning generally relies on calculations based on the construction-standard-production-rate. This method requires trial and error practice during the construction phase to deduce the optimized equipment combination for time and cost. Trial and error during construction can lead to cost overruns and schedule delays. Accordingly, this paper suggests an advanced method for establishing a productivity database based on combinations of equipment and also considering site conditions in order to reduce the timely effort for deducing the optimized equipment combination. For this purpose DES (Discrete Event Simulation) model was developed based on the information provided in the construction-standard-production-rate.

• KDI Journal of Economic Policy
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• 제14권1호
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• pp.121-145
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• 1992

본고(本稿)에서는 현재의 경제상황을 잘 반영하는 건설투자활동(建設投資活動)의 단기예측모형(短期豫測模型)을 정립하고자 먼저 관련 시계열자료의 안정성(安定性) 여부(與否)와 순환성(循環性), 계절성(季節性)의 특성을 살펴본 후 여러 단기모형의 예측력(豫測力), 정합성(整合性), 설명력(說明力)을 비교 검토했다. 단위근(單位根) 검정(檢定)과 자기상관계수(自己相關係數) 스펙트랄 밀도함수 분석의 결과, 건설관련 시계열자료들이 대체로 단위근(單位根)을 갖지 않음으로써 안정적이고 주기적인 순환변동을 하고 있으며, 시차변수의 설명력이 높은 특성을 나타내었다. 또한 건설투자자료의 특성이 선행지표(先行指標)인 건축허가연면적(建築許可延面積) 및 건설수주액(建設受注額)과 아주 유사하여 건설투자 단기예측에 있어서 두 지표 사이의 시차관계(時差關係) 파악이 중요함을 알 수 있었다. 제(第)III장(章)에서는 단변량(單變量) 시계열모형(時系列模型)으로 ARIMA모형(模型)과 승법선형추세예측모형(乘法線型趨勢豫測模型)을, 다변량(多變量) 시계열모형(時系列模型)으로는 첫째, 선행지표(先行指標)를 이용한 1차자기회귀모형(次自己回歸模型), VAR모형(模型), 둘째 GNP자료를 이용한 거시경제모형의 단순한 축약형모형(縮約型模型)과 VAR모형(模型)을 제시하고 이들을 비교 평가하였다. 이에 따르면 단변량 시계열모형보다는 다변량 시계열모형이 시간이 경과할수록 예측오차(豫測誤差)가 커지지 않는다는 점에서 우수한 것으로 나타났으며, 다변량모형 중에서도 벡터자기회귀모형이 여타 모형보다 절대예측오차평균(絶對豫測誤差平均), 평균자승근(平均自乘根) 퍼센트 오차(誤差), 결정계수(決定係數) 등 모든 면에서 우수한 것으로 평가되었다. 이는 최근 건설투자가 추세에서 벗어난 급증세를 지속하고 있음을 고려할 때 타당한 결론이라 생각된다.

• 제어로봇시스템학회논문지
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• 제20권7호
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• pp.774-781
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• 2014

This paper presents a fingerprint database construction method for WLAN RSSI (Received Signal Strength Indicator)-based indoor positioning. When RSSI is used for indoor positioning, the fingerprint method can achieve more accurate positioning than trilateration and centroid methods. However, a FD (Fingerprint Database) must be constructed before positioning. This step is a very laborious process. To reduce the drawbacks of the fingerprint method, a radio propagation model-based FD construction method is presented. In this method, an FD can be constructed by a simulator. Experimental results show that the constructed FD-based positioning has a 3.17m (CEP) error. In this paper, a spatial correlation method is presented to estimate the NLOS(Non-Line of Sight) error included in the FD constructed by a simulator. As a result, the NLOS error of the FD is reduced and the performance of the error compensated FD-based positioning is improved. The experimental results show that the enhanced FD-based positioning has a 2.58m (CEP) error that is a reasonable performance for indoor LBS (Location Based Service).

• 한국산업융합학회 논문집
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• 제24권6_2호
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• pp.753-762
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• 2021

Google provides mapping services using satellite imagery, this is widely used for the study. Since about 20 years ago, research and business using drones have been expanding. Pix4D is widely used to create 3D information models using drones. This study compared the distance error by comparing the result of the road construction site with the DSM data of Google Earth and Pix4 D. Through this, we tried to understand the reliability of the result of distance measurement in Google Earth. A DTM result of 3.08 cm/pixel was obtained as a result of matching with 49666 key points for each image. The length and altitude of Pix4D and Google Earth were measured and compared using the obtained PCD. As a result, the average error of the distance based on the data of Pix4D was measured to be 0.68 m, confirming that the error was relatively small. As a result of measuring the altitude of Google Earth and Pix4D and comparing them, it was confirmed that the maximum error was 83.214m, which was measured using satellite images, but the error was quite large and there was inaccuracy. Through this, it was confirmed that there are difficulties in analyzing and acquiring data at road construction sites using Google Earth, and the result was obtained that point cloud data using drones is necessary.

• 대한기계학회논문집
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• 제18권11호
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• pp.2932-2943
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• 1994

An efficient approach to the automatic construction of effective quadrilateral finite element meshes for two-dimensional analysis is presented. The procedure is composed of, firstly, an initial mesh generation and, secondly, an h-version of adaptive refinement based on error analysis. As for an initial mesh generation scheme, a modified looping algorithm has been employed. For the adaptive refinement process, an error indicator obtained by computing the residual error of the equilibrium equations in the energy norm with a relaxation factor has been employed. Examples of mesh generation and self-adaptive mesh improvements are given. These example solutions demonstrate that an effective mesh for a given error tolerance can be obtained in a few steps of the analysis processes.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2021년도 봄 학술논문 발표대회
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• pp.223-224
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• 2021

Recently, the demand for high fluidity concrete has been increased due to skyscrapers. However, it has its own limits. First of all, high fluidity concrete has large variation and through trial & error it costs lots of money and time. Neural network model has repetitive learning process which can solve the problem while training the data. Therefore, the purpose of this study is to predict optimum mix design of 40MPa, 60MPa high fluidity concrete by using neural network model and verifying compressive strength by applying real data. As a result, comparing collective data and predicted compressive strength data using MATLAB, 40MPa mix design error rate was 1.2%~1.6% and 60MPa mix design error rate was 2%~3%. Overall 40MPa mix design error rate was less than 60MPa mix design error rate.

• Kyungpook Mathematical Journal
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• 제26권2호
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• pp.173-186
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• 1986

• 교육시설 논문지
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• 제25권6호
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• pp.35-44
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• 2018

The purpose of this study is to predict more accurate construction costs and to support efficient decision making in the planning and design stages of smart education facilities. The higher the error in the projected cost, the more risk a project manager takes. If the manager can predict a more accurate construction cost in the early stages of a project, he/she can secure a decision period and support a more rational decision. During the planning and design stages, there is a limited amount of variables that can be selected for the estimating model. Moreover, since the number of completed smart schools is limited, there is little data. In this study, various artificial intelligence models were used to accurately predict the construction cost in the planning and design phase with limited variables and lack of performance data. A theoretical study on an artificial neural network and deep learning was carried out. As the artificial neural network has frequent problems of overfitting, it is found that there is a problem in practical application. In order to overcome the problem, this study suggests that the improved models of Deep Neural Network and Deep Belief Network are more effective in making accurate predictions. Deep Neural Network (DNN) and Deep Belief Network (DBN) models were constructed for the prediction of construction cost. Average Error Rate and Root Mean Square Error (RMSE) were calculated to compare the error and accuracy of those models. This study proposes a cost prediction model that can be used practically in the planning and design stages.

• 한국디지털건축인테리어학회논문집
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• 제12권1호
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• pp.61-70
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• 2012

A construction project is very costly and takes a long time to make investment and yield profit. For this reason, financial institutions are cautious about financing construction projects. Meanwhile, a construction company needs financing from financial institutions to cover a large expense of a construction project. Thus, there is likely to be a close correlation between financing conditions and business operation of a construction company. To examine the relationship, variables were identified that are related to insolvency of a construction company and changes in financing conditions. The analysis period is between the second quarter of 2001 and the fourth quarter of 2010. Data was retrieved from TS2000 established by Korea Listed Companies Association (KLCA), Statistics Office, and Construction Economy Research Institute of Korea (CERIK). In terms of methodology, VECM (Vector Error Correction Model) was used to analyze dynamic relationship between changes in financing conditions and insolvency of a construction company based on the identified variables. The hypothesis was that changes in financing conditions would significantly affect business of a construction company, but, the analysis did not find a close relation between the two factors. However, it was shown that poor business of a construction company affects financing conditions adversely.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2017년도 춘계 학술논문 발표대회
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• pp.264-265
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• 2017

This paper aims to analyze relations between design errors prevented by building information modeling (BIM)-based design validation and their impacts in order to identify critical consideration factors for successfully implementing BIM-based design validation in the architecture, engineering, and construction (AEC) projects. More than 800 design errors detected by BIM-based design validation in two BIM-based projects in South Korea are categorized according to its causes and work types. The relations between causes and work types of design errors and project delay, cost overrun, low quality, and rework generation that can be caused by the errors are analyzed through conducting logistic regression. Characteristics of each design error are analyzed by conducting face-to-face interviews with practitioners in the two BIM-based projects. As the results, the impacts of design error causes on predicting project delay, cost overrun, low quality, and rework generation were the highest.