• 한국건설관리학회논문집
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• 제10권3호
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• pp.130-138
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• 2009

Bromilow's time-cost (BTC) relationship was examined for building projects in Vietnam using actual construction time and total construction cost. Data set was collected from 77 historical building construction projects completed between 1999 and 2005 which were adjusted by consumer price index (CPI) to 2000 price. Time-cost equations were specified respected to two sectors, public and private, in Vietnamese construction industry and all cases. It is shown that a public funded building project has the longer construction duration than a similar budget private funded project. The resulting models are statistically significant. The adjusted R-square coefficients of all cases, public and private projects models are respectively 0.403, 0.436 and 0.377 mean that the BTC regression lines moderately fit the data set.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2018년도 춘계 학술논문 발표대회
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• pp.294-295
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• 2018

Nowadays, the construction industry utilizes 3D models in the designing process, on which research is being conducted to establish an automated system for project cost estimation in connection with information related to construction such as material unit costs and wages, beyond the level of design interference review and construction quantity estimation. In this process, the project cost is estimated in connection with unit price data after takeoff the quantity based on the 3D model attributes and data types. A way to reduce cost and risk would be first developing prototypes of some of essential buildings and works, comparing and validating the outcomes, and then extending to the whole scope, because estimates differ on the basis of the scope and level of 3D design models as well as the data accuracy. This study analyzes case studies of project cost estimation by computing the quantity on the basis of 3D model in the construction industry and explores methodologies and management measures applicable for estimating nuclear power plant construction project costs.

• 한국산학기술학회논문지
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• 제14권3호
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• pp.1474-1480
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• 2013

본 연구는 TBM 공법의 구경별 자원기반 적산(원가 계산) 방식에 의한 공사비 적산을 수행하여 직접공사비와 총공사비를 분석하고, 이를 근거로 회귀분석을 수행하여 TBM 공법 구경별 직접공사비 및 총공사비를 추정할 수 있는 개산 공사비(개략 공사비) 예측 식 모델을 제시하였다. 본 연구에서 제시한 TBM 공법의 구경별 개산 공사비 예측 식 모델은 향후 TBM 공법 적용 현장의 사업기획, 예비조사, 타당성조사, 기본설계 단계에서 개산 공사비를 추정하는데 효과적으로 적용할 수 있을 것이다.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2019년도 춘계 학술논문 발표대회
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• pp.226-227
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• 2019

The basic type construction cost which is the base of the building cost estimation is being adjusted according to the price changes by utilizing the apartment construction cost index in order to flexibly operate it. In this study, we analyzed the change trends and characteristics of the housing cost index for the basic type building cost model project operated from September, 2012 to March, 2018. As a result, the increase in material costs is slight while the share of the labor cost increased in the construction cost due to the rise of labor unit price, leading to a perceived increase in sensitivity of labor costs. We should be careful to keep the sensitivity of the material cost and the labor cost to an appropriate level so that the index may not be distorted.

• 한국건설관리학회논문집
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• 제14권3호
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• pp.42-52
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• 2013

본 연구는 기존의 선형적인 공사비 예측방법의 한계를 극복하고 사례기반추론 (Case Based Reasoning, CBR)기법을 통해 기획단계의 실적정보를 활용하여 신뢰도 높은 공사비 예측 모델을 제시하는 것이 목적이다. 이를 위하여 사례기반추론 기법과 유전자알고리즘 (Genetic Algorithm, GA)의 선택연산을 복합적으로 활용한 스프레드시트 기반의 교량공사비 추론모델을 제시하였다. 추론모델의 검증을 위하여 국내 교량공사 시공사례 4건을 적용하였으며, 적용 결과 평균 8.69%의 오차율로 나타나 교량공사비의 예측 정확도가 타 분석방법과 비교하여 상대적으로 높은 것으로 파악하였다. 연구에서 제시된 교량공사비 예측모델은 초기 설계단계에서 상세제원에 대한 정보를 획득할 수 없을 경우에, 교량의 대표적 제원정보 만으로 공사비 선택범위를 최소화된 오차율로 예측할 수 있으므로, 개선된 보정 방법으로서 교량공사의 합리적인 개략공사비 산정에 활용될 수 있을 것으로 판단된다.

• 한국콘텐츠학회논문지
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• 제12권4호
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• pp.468-475
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• 2012

도로건설공사의 주요 구조물인 터널의 경우 건설지역, 지반 환경, 공법 등 다양한 변수에 의해 공사비가 결정된다. 그러나 NATM 터널 초기 공사비를 결정하기 위한 모델에 대한 연구는 부족한 실정이다. 따라서 본 논문은 시스템 사고를 활용하여 터널공사비 예측을 위한 모델을 제시하였다. 기존 문헌 조사를 통해 터널공사비의 영향요인을 파악하고 영향 요인간 상관관계를 고려하여 터널공사비 인과지도를 작성하였다. 그리고 터널공사의 내역서를 활용하여 터널 길이와 굴착량, 암질 등에 의한 터널공사비 변화정도를 분석하였다. 본 연구를 통해 다양한 영향요인을 고려한 NATM 터널공사비 예측에 대한 대안을 제시할 수 있을 것으로 기대한다.

• 국제학술발표논문집
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• The 4th International Conference on Construction Engineering and Project Management Organized by the University of New South Wales
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• pp.1-6
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• 2011

As large-scale building projects have recently increased for the residential, commercial and office facilities, construction costs for these projects have become a matter of great concern, due to their significant construction cost implications, as well as unpredictable market conditions and fluctuations in the rate of inflation during the projects' long-term construction periods. In particular, recent volatile fluctuations of construction material prices fueled such problems as cost forecasting. This research develops a time series model using the Box-Jenkins approach and material price time series data in Korea in order to forecast trends in the unit prices of required materials. Building information modeling (BIM) approaches are also used to analyze injection times of construction resources and to conduct quantity take-off so that total material prices can be forecast. To determine an optimal time series model for forecasting price trends, comparative analysis of predictability of tentative autoregressive integrated moving average (ARIMA) models is conducted. The proposed BIM-based time series forecasting model can help to deal with sudden changes in economic conditions by estimating material prices that correspond to resource injection times.

• 국제학술발표논문집
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• The 4th International Conference on Construction Engineering and Project Management Organized by the University of New South Wales
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• pp.534-541
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• 2011

Risk evaluation approaches for bidding on international construction projects are typically partitioned into three stages: country selection, project classification, and bid-cost evaluation. However, previous studies are frequently under attack in that they have several crucial limitations: 1) a dearth of studies about country selection risk tailored for the overseas construction market at a corporate level; 2) no consideration of uncertainties for input variable per se; 3) less probabilistic approaches in estimating a range of cost variance; and 4) less inclusion of covariance impacts. This study thus suggests a three-staged risk evaluation model to resolve these inherent problems. In the first stage, a country portfolio model that maximizes the expected construction market growth rate and profit rate while decreasing market uncertainty is formulated using multi-objective genetic analysis. Following this, probabilistic approaches for screening bad projects are suggested through applying various data mining methods such as discriminant logistic regression, neural network, C5.0, and support vector machine. For the last stage, the cost overrun prediction model is simulated for determining a reasonable bid cost, while considering non-parametric distribution, effects of systematic risks, and the firm's specific capability accrued in a given country. Through the three consecutive models, this study verifies that international construction risk can be allocated, reduced, and projected to some degree, thereby contributing to sustaining stable profits and revenues in both the short-term and the long-term perspective.

• 한국건설관리학회논문집
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• 제10권3호
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• pp.139-147
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• 2009

Accurate construction cost estimation in the initial stage of building project plays a key role for project success and for mitigation of disputes. Total construction cost(TCC) estimation of apartment projects in Vietnam has become more important because those projects increasingly rise in quantity with the urbanization and population growth. This paper presents the application of artificial neural networks(ANNs) in estimating TCC of apartment projects. Ninety-one questionnaires were collected to identify input variables. Fourteen data sets of completed apartment projects were obtained and processed for training and generalizing the neural network(NN). MATLAB software was used to train the NN. A program was constructed using Visual C++ in order to apply the neural network to realistic projects. The results suggest that this model is reasonable in predicting TCCs for apartment projects and reinforce the reliability of using neural networks to cost models. Although the proposed model is not validated in a rigorous way, the ANN-based model may be useful for both practitioners and researchers. It facilitates systematic predictions in early phases of construction projects. Practitioners are more proactive in estimating construction costs and making consistent decisions in initial phases of apartment projects. Researchers should benefit from exploring insights into its implementation in the real world. The findings are useful not only to researchers and practitioners in the Vietnam Construction Industry(VCI) but also to participants in other developing countries in South East Asia. Since Korea has emerged as the first largest foreign investor in Vietnam, the results of this study may be also useful to participants in Korea.

• 국제학술발표논문집
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• The 5th International Conference on Construction Engineering and Project Management
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• pp.243-251
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• 2013

Cash management is a major concern for all contractors in the construction industry. It is arguable that cash is the most critical resource of all. A contractor needs to secure sufficient funds to navigate the project to the end, while keeping an eye on maximizing profits along the way. Past research attempted to address such topic via developing models to tackle the time-cost tradeoff problem, cash flow forecasting, and cash flow management. Yet, little was done to integrate the three aspects of cash management together. This paper, as such, presents a comprehensive model that integrates the time-cost tradeoff problem, cash flow management, and cash flow forecasting. First, the model determines the project optimal completion time by considering the different alternative construction methods available for executing project activities. Second, it investigates different funding alternatives and proposes a project-level cash management plan. Two funding alternatives are considered; they are borrowing and company own financing. The model was built as a combinatorial optimization model that utilizes ant colony search capabilities. The model also utilizes Microsoft Project software and spreadsheets to maintain an environment that incorporates activities, their durations, and other project data, in order to estimate project completion time and cost. Ant Colony Optimization algorithm was coded as a Macro program using VBA. Finally, an example project was used to test the developed model, where it acted reliably in maximizing the contractor's profit in the test project.