• Journal of Wetlands Research
• /
• v.13 no.1
• /
• pp.13-23
• /
• 2011

In this study, Mike11 will be used as the numerical model where a data assimilation method will be applied to it. This paper aims to gain an insight and understanding of data assimilation in flood forecasting models. It will start with a general discussion of data assimilation, followed by a description of the methodology and discussion of the statistical error forecast model used, which in this case is the linear regression. This error forecast model is applied to the water level forecast simulated by MIKE11 to produced improved forecast and validated against real measurements. It is found that there exists a phase error in the improved forecasts. Hence, 2 general formula are used to account for this phase error and they have shown improvement to the accuracy of the forecasts, where one improved the immediate forecast of up to 5 hours while the other improved the estimation of the peak discharge.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2012.05a
• /
• pp.25-27
• /
• 2012

The domestic construction industry recognize that review of design and construct-ability has the devastating effects. The way to take advantage of BIM for increase the possibility of discovered the design errors in advance are being discussed a lot recently. Since 2012, Public procurement published that the Turn-key project which is the more than 50 billion won in construction have to let the BIM-based design be mandatory. However, people expected the advent of BIM technology can resolve the existing design errors. But the situation is the connection of design stage and construction stage is very insufficient in domestic. Therefore, in this study understanding the types of design errors in construction phase. according the review of design errors before construction. Then can acceleration the application of BIM to improve the workability in a construction site.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2013.05a
• /
• pp.213-214
• /
• 2013

Recently, modular construction has been begun to introduce actively for improvment of productivity in the construction industry. By the way, the construction technology is being IT through BIM. This is why modular construction needs BIM technology. In this study, we analyze the problems in the current modular construction at factory production stage. The way to extract 3D shop drawings using BIM is suggested to solve this problem.

• Korean Journal of Construction Engineering and Management
• /
• v.21 no.3
• /
• pp.48-56
• /
• 2020

Prediction of construction cost in the planning stage that provides basic information for feasibility study, budgeting, and planning is an important factor for successful project execution. In this study, a prediction model was developed for the purpose of improving the accuracy of estimating the construction cost of Hanok in the planning stage. The cost of this model is estimated by two methods. First, the cost of wood work, which accounts for the largest portion of the total construction cost, is estimated by calculating the approximate quantity under various conditions (structure type, roof type, plane type, etc.). Second, the cost of the rest work sections except the wood work is estimated by using the unit cost model. The predictive model was verified by two case projects, and the error rate of total construction cost was -4%(case 1) and -6%(case 2). These results showed an error rate in the range that can be applied to practice in the planning stage.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.823-830
• /
• 2022

The risk of project execution increases due to the enlargement and complexity of Engineering, Procurement, and Construction (EPC) plant projects. In the fourth industrial revolution era, there is an increasing need to utilize a large amount of data generated during project execution. The design is a key element for the success of the EPC plant project. Although the design cost is about 5% of the total EPC project cost, it is a critical process that affects the entire subsequent process, such as construction, installation, and operation & maintenance (O&M). This study aims to develop a system using machine-learning (ML) techniques to predict risks and support decision-making based on big data generated in an EPC project's design and construction stages. As a result, three main modules were developed: (M1) the design cost estimation module, (M2) the design error check module, and (M3) the change order forecasting module. M1 estimated design cost based on project data such as contract amount, construction period, total design cost, and man-hour (M/H). M2 and M3 are applications for predicting the severity of schedule delay and cost over-run due to design errors and change orders through unstructured text data extracted from engineering documents. A validation test was performed through a case study to verify the model applied to each module. It is expected to improve the risk response capability of EPC contractors in the design and construction stage through this study.

• Journal of the Korea Institute of Building Construction
• /
• v.17 no.6
• /
• pp.535-544
• /
• 2017

This paper analyzes the relations between design errors, prevented by building information modeling (BIM)-based design validation, and their impacts in order to identify critical consideration factors for implementing BIM-based design validation in 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 their causes (illogical error, discrepancy, and missing item) and work types (structure, architecture, and mechanical, electrical, and plumbing (MEP)). The probabilistic relations among the independent variables, including the causes and work types of design errors, and the dependent variables, including the project delays, cost overruns, low quality, and rework generation that can be caused by these errors, are analyzed using logistic regression. The characteristics of each design error are analyzed by means of face-to-face interviews with practitioners. According to the results, the impacts of design error causes in predicting the probability values of project delays, cost overruns, low quality, and rework generation were statistically meaningful.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2005.05a
• /
• pp.107-112
• /
• 2005

Renovation of existing sports facilities or construction of temporary ones for international complex games requires a wide variety of hundreds of sub construction projects to be awarded at the same time but there are few precedents required regarding the study of tendering process required in such cases. As award of construction contracts to build sports facilities for various international complex games is a critical determinant of fate of international complex games in the initial phase, it is very critical that adequate construction tendering process bedetermined. This research is aimed to study tendering cases for renovation or construction of critical international sports facilities to enhance efficiency of facility construction and define most reasonable tendering approach to satisfy schedule constraints often associated with international complex games and contribute to improvement of international competitiveness of their host cities and propose a direction for further development. This study also provides inputs to subsequent sports events of comparable scale to minimize trial and error.

• Journal of KIBIM
• /
• v.12 no.2
• /
• pp.40-48
• /
• 2022

It is very important to accurately predict construction costs in the early stages of the construction project. However, it is difficult to accurately predict construction costs with limited information from the initial stage. In recent years, with the development of machine learning technology, it has become possible to predict construction costs more accurately than before only with schematic construction characteristics. Based on machine learning technology, this study aims to analyze plans to more accurately predict construction costs by using only the factors influencing construction costs. To the end of this study, the effect of the error rate according to the activation function and the node configuration of the hidden layer was analyzed.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.1194-1201
• /
• 2022

The existing literature has witnessed the importance of productivity assessment and deducing factors affecting it. However, yet many models have shown limitations in practical applications in actual construction sites for process planning due to uncertainty and lack of data. This research presents a productivity assessment and database generation framework using simulation and compares the results with RSMeans to derive appropriate equipment combinations alternatives for earthwork operations. Data of 15 different conditions was collected from 5 different construction sites. Prediction accuracy above 90% were achieved for the simulation models with average error rate of 7.4%. The construction productivity assessment framework presented in this study is expected to be highly applicable to operation planning for earthwork operations.

• Journal of the Korean Society of Industry Convergence
• /
• v.26 no.6_3
• /
• pp.1325-1332
• /
• 2023

Recently, much research is being conducted based on point cloud data for the growth of innovations such as construction automation in the transportation field and virtual national space. This data is often measured through remote control in terrain that is difficult for humans to access using devices such as UAVs and UGVs. Drones, one of the UAVs, are mainly used to acquire point cloud data, but photogrammetry using a vision camera, which takes a lot of time to create a point cloud map, is difficult to apply in construction sites where the terrain changes periodically and surveying is difficult. In this paper, we developed a point cloud mapping system by adopting non-repetitive scanning LiDAR and attempted to confirm improvements through field application. For accuracy analysis, a point cloud map was created through a 2 minute 40 second flight and about 30 seconds of software post-processing on a terrain measuring 144.5 × 138.8 m. As a result of comparing the actual measured distance for structures with an average of 4 m, an average error of 4.3 cm was recorded, confirming that the performance was within the error range applicable to the field.