• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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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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• v.14 no.1
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• pp.121-145
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• 1992

This paper examines characteristics of time series data related to the construction investment(stationarity and time series components such as secular trend, cyclical fluctuation, seasonal variation, and random change) and surveys predictibility, fitness, and explicability of independent variables of various models to build a short-term construction investment forecasting model suitable for current economic circumstances. Unit root test, autocorrelation coefficient and spectral density function analysis show that related time series data do not have unit roots, fluctuate cyclically, and are largely explicated by lagged variables. Moreover it is very important for the short-term construction investment forecasting to grasp time lag relation between construction investment series and leading indicators such as building construction permits and value of construction orders received. In chapter 3, we explicate 7 forecasting models; Univariate time series model (ARIMA and multiplicative linear trend model), multivariate time series model using leading indicators (1st order autoregressive model, vector autoregressive model and error correction model) and multivariate time series model using National Accounts data (simple reduced form model disconnected from simultaneous macroeconomic model and VAR model). These models are examined by 4 statistical tools that are average absolute error, root mean square error, adjusted coefficient of determination, and Durbin-Watson statistic. This analysis proves two facts. First, multivariate models are more suitable than univariate models in the point that forecasting error of multivariate models tend to decrease in contrast to the case of latter. Second, VAR model is superior than any other multivariate models; average absolute prediction error and root mean square error of VAR model are quitely low and adjusted coefficient of determination is higher. This conclusion is reasonable when we consider current construction investment has sustained overheating growth more than secular trend.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.7
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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).

• Journal of the Korean Society of Industry Convergence
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• v.24 no.6_2
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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.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.11
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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.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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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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• v.26 no.2
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• pp.173-186
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• 1986

• Journal of the Korean Institute of Educational Facilities
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• v.25 no.6
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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.

• Journal of The Korean Digital Architecture Interior Association
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• v.12 no.1
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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.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2017.05a
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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.