• Korean Journal of Construction Engineering and Management
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• v.7 no.4 s.32
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• pp.177-183
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• 2006

Risk-based estimation has been successfully introduced into the construction industry. By incorporating historical data associated with probability analysis, risk-based estimate is an effective decision support aid in considering whether to launch a particular project. The industry challenges, however, especially related with management issues, such as labor shortage, wage growth, and supply chain complexity, have often resulted in poor cost performance. The insufficient assessing the project characteristics (i.e., resource availability, project complexity, and project delivery method) can be the main reasons in the poor cost performance. Because the accuracy level of cost performance prediction can be enhanced by extensive evaluation of the subject project characteristics, a new approach for predicting cost performance in an earlier stage of a project can improve the Industry substantiality, in other words, value maximization. The purpose of this paper is to develop a new methodology in developing a risk-based estimate tool by incorporating extensive project characteristics. To do this, an extensive industry survey was conducted from both private and public sectors in building industry in Korea. In addition, significant project characteristics were identified in terms of cost performance indicator. Although the data collection is limited to Korean industry the suggested approach provides the industry with a straightforward methodology in risk management. As many researchers maintained that front-end planning efforts are crucial in achieving the successful outcome in building projects, the new method for risk-based estimation can Improve the cost performance as well as enhance the fulfillment in terms of business sustainability.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.9
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• pp.4284-4293
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• 2012

At the early construction project stage, the most important task is to estimate planned construction costs analyzed with detailed information. Therefore, in this study, Apartment Housing Projects at the Early Stage of Civil Construction Cost of the reasonable and accurate predictions of the Regression analysis to 170 of actual Construction Cost, and dependent variable regression to Civil Construction Cost, location based national land area based on a combination of private land, union land, public land to the use of predictive models by various analyses of the ease and accuracy. As a result, Civil Construction Cost of Apartment Housing Projects by the regression formula for the error rate estimates in national land predictive model 15.59%, private land predictive model 17.53%, union land predictive model 21.86%, public land predictive model 13.08%.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.05a
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• pp.122-123
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• 2018

Recently, CM at Risk delivery system(CM@R) that could solve the problems of Design Bid Build delivery(DBB) system has been emerging. In the CM@R delivery system, the contractor negotiates for a maximum guaranteed price(GMP) with the client at the design stage, and the contractor carries out the construction within the GMP. In CM @ R, the construction company with expertise in construction participates from the design stage to reflects the construction know-how in the design. On the other hand, the modification design frequently occurs due to the change of the construction cost when negotiating the GMP. In addition, uncertainties are inherent in the GMP calculation because the calculation is based on unfinished drawings and documents. This study proposes a probabilistic GMP estimation method applying MCS to the BIM - based cost prediction model, in order to extract the accurate quantity information when estimating the GMP and to cope with the change of the construction cost inherent in uncertainty.

• Korean Journal of Construction Engineering and Management
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• v.10 no.4
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• pp.87-100
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• 2009

The quality of early cost estimates is critical to the feasibility analysis and budget allocation decisions for public capital projects. Various researches have been attempted to develop cost prediction models in the early stage of a construction project. However, existing studies are limited on its applicability to actual projects because they focus primarily on a specific phase as well as utilize restricted information while the amount of information collectable differs from one another along with the project stages. This research aims to develop two-staged cost estimation model for the schematic planning and preliminary design process of a construction projects, considering the available information of each phase. In the schematic planning stage where outlined information of a project is only available, the Case-Based Reasoning model is used for easy and rapid elicitation of a project cost based on the extensive database of more than 90 actual highway construction projects. Then, the representing quantity-based model is proposed for the preliminary design stage where more information on the quantities and unit costs are collectable based on the alternative routes and cross-sections of a highway project. Real case studies are used to demonstrate and validate the benefits of the proposed approach. Through the two-stage cost estimation system, users are able to hold a timely prospect to presume the final cost within the budge such that feasibility study as well as budget allocation decisions are made on effectively and competitively.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09a
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• pp.675-676
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• 2005

• Journal of the Korea Institute of Building Construction
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• v.11 no.5
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• pp.459-467
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• 2011

The work of constructing apartment housing involves various fields of industry that are linked to each other, and is based on a design document prepared by multiple technicians and architects. Consequently, design errors, material flaws or faulty construction works can cause defects, which sometimes overlap with each other. Construction companies should repair any defects found in a completed building within a specified period of time, and to do this, should establish a business plan by efficiently predicting the cost of defect repair. As it is very difficult for companies to accurately predict the occurrence of defects, historical performance data is used as a base. For domestic apartment housing units, data on the cost of defect repair is insufficient, so there are hardly any methods that can be used to make precise predictions. Therefore, the intent of this study is to develop a model that can predict the cost of defect repair by supply type and area, based on historical performance data with ten years worth of post-completion.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2004.11a
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• pp.290-295
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• 2004

Along with social changes, school buildings are getting complex and diversified unlike the past. However, objective data analysis on construction costs fall short. In particular, ordering agencies are in a great need of objective and practical construction cost management for on-budget construction and procurement of quality goods. This paper analyzes the design diagram for a newly built school with an order from the Daejeon Metropolitan Office of Education, and compares the analysis with those of other kinds of buildings. The results are: the total construction cost of one school unit is 8,017,596,000 won on average; the cost is in the order of building, machinery and equipment, electricity, communications and civil engineering; as to activity, RC construction takes account of $30.3\%$ of the total construction cost. 1'he cost of school construction per M2 is 838,000 won, which is 6th highest of 11 kinds of constructions and slightly lower than 950,000 won, the average price of comparative constructions. When it comes to the percentage, school building takes mote percentage of the total cost than comparative building while machinery and equipment, electricity and communications takes slightly less percentage. Through simple regression analysis of gross coverage, this paper suggests a model formula with which the total construction cost, construction cost in accordance with activity, how much main construction materials are to be used are predictable.

• Journal of Construction Engineering and Project Management
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• v.5 no.1
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• pp.11-19
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• 2015

This paper presents optimized artificial neural networks (ANNs) claims prediction and decision awareness framework that guides owner organizations in their pre-bid construction project decisions to minimize claims. The framework is composed of two genetic optimization ANNs models: a Claims Impact Prediction Model (CIPM), and a Decision Awareness Model (DAM). The CIPM is composed of three separate ANNs that predict the cost and time impacts of the possible claims that may arise in a project. The models also predict the expected types of relationship between the owner and the contractor based on their behavioral and technical decisions during the bidding phase of the project. The framework is implemented using actual data from international projects in the Middle East and Egypt (projects owned by either public or private local organizations who hired international prime contractors to deliver the projects). Literature review, interviews with pertinent experts in the Middle East, and lessons learned from several international construction projects in Egypt determined the input decision variables of the CIPM. The ANNs training, which has been implemented in a spreadsheet environment, was optimized using genetic algorithm (GA). Different weights were assigned as variables to the different layers of each ANN and the total square error was used as the objective function to be minimized. Data was collected from thirty-two international construction projects in order to train and test the ANNs of the CIPM, which predicted cost overruns, schedule delays, and relationships between contracting parties. A genetic optimization backward analysis technique was then applied to develop the Decision Awareness Model (DAM). The DAM combined the three artificial neural networks of the CIPM to assist project owners in setting optimum values for their behavioral and technical decision variables. It implements an intelligent user-friendly input interface which helps project owners in visualizing the impact of their decisions on the project's total cost, original duration, and expected owner-contractor relationship. The framework presents a unique and transparent hybrid genetic algorithm-ANNs training and testing method. It has been implemented in a spreadsheet environment using MS Excel$^{(R)}$ and EVOLVERTM V.5.5. It provides projects' owners of a decision-support tool that raises their awareness regarding their pre-bid decisions for a construction project.

• Geomechanics and Engineering
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• v.30 no.5
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• pp.413-422
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• 2022

TBM is widely used in the construction of various underground projects in the current world, and has the unique advantages that cannot be compared with traditional excavation methods. However, due to the high cost of TBM, the damage is even greater when geological disasters such as collapse occur during excavation. At present, there is still a shortage of research on various types of risk prediction of TBM tunnel, and accurate and reliable risk prediction model is an important theoretical basis for timely risk avoidance during construction. In this paper, a prediction model is proposed to evaluate the risk level of tunnel collapse by establishing a reasonable risk index system, using analytic hierarchy process to determine the index weight, and using the normal cloud model theory. At the same time, the traditional analytic hierarchy process is improved and optimized to ensure the objectivity of the weight values of the indicators in the prediction process, and the qualitative indicators are quantified so that they can directly participate in the process of risk prediction calculation. Through the practical engineering application, the feasibility and accuracy of the method are verified, and further optimization can be analyzed and discussed.

• Journal of Korean Tunnelling and Underground Space Association
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• v.5 no.2
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• pp.161-174
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• 2003

Applicability of the Decision Aids for Tunneling (DAT) technique is investigated in this study to better understand the efficiency of the decision making process during tunnel construction. For this, a traffic tunnel under construction is adopted and information on the construction procedure, i.e., overall geology, unit cost and construction time for each excavation process, is provided periodically. Various scattergrams in which cost-time simulation results are plotted are obtained according to the simulation methods and final prediction on the construction time/cost is made. It is found that the uncertainty in the cost distribution is greater than the uncertainty in the time distribution for each cycle simulation and the uncertainties in time and cost for the one time simulations are comparable. Future work will be concentrated on the updating scheme using the face mapping data and various parametric studies will also be performed.