• 한국건축시공학회지
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• 제13권5호
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• pp.506-512
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• 2013

Since the amendment of the law on the private sector investment in social infrastructure in January of 2005, the government has been actively promoting Build-Transfer-Lease (BTL) projects. Notably, most new educational facilities have been built as BTL projects. For these facilities, the unit cost per unit area has been applied to predict construction costs. However, since construction costs are mostly managed after the detailed design phase, the costs can be estimated incorrectly. For this reason, cost management is needed in the planning phase, with a sound approximate estimate to prevent the wasteful use of funds. To address this shortcoming, this study aims to develop a quantity prediction model for education facilities using regression analysis in the planning phase. The developed model is focused on the required quantities of reinforced concrete and bricks. In order to achieve the objective, the data of 44 educational facility projects collected from Gyeonggi-do was used in the regression model. This study can be utilized by major stakeholders to accurately predict construction costs by estimating the appropriate quantities of reinforced concrete and bricks in the planning design phase.

• 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.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2021년도 가을 학술논문 발표대회
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• pp.232-233
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• 2021

As construction projects become larger and more complex in the construction environment, and as the Building Information Model(BIM) is technically introduced, the demand for construction costs in units of space is increasing. Cost estimating of spacial element can reduce the error in cost prediction method based on cost of work type and to utilize the construction cost data for each space in the design phase. The purpose of this study is to extract spatial statements by utilizing spacial information of quantitative statements based on items that are common elements of the Quantity Take-Off and Bill of Quantity.

• 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.58-65
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• 2011

Construction Productivity is one of the most important elements in construction management. It is used in construction process scheduling and cost management, which are significant sector in construction management. It is important to make appropriate schedule and monitor how works are done within schedule. But construction project contains uncertainty and inexactitude, modifying construction schedule is being an issue to manage construction works well. Even though prediction and monitoring of productivity can be principal activity, it is hard to predict productivity with manager's experience and a standard of estimate. A large number of factors influencing productivity, such as drawing, construction method, weather, labor, material, equipment, etc. But current calculation of productivity depends on empirical probability, not consider difference of each influencing factor. In this research, the aim is to present a productivity predicting regression model of form work, which includes effectiveness of influences factors. 5 variables existed inside form work are selected by interview and site research based on literature review of existed various productivity influencing factors. The effectiveness and correlation of productivity influencing factors are analyzed by statistical approach, and it is used to make productivity regression model. The finding of this research will improves monitoring and controlling of project schedule in construction phase.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2013년도 추계 학술논문 발표대회
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• pp.114-115
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• 2013

The maintenance and repair period consists the largest part of a construction project life cycle cost. However, it has been analyzed that the repairing plan relies on regulations and the officers' experience mostly that sometimes lead to performing unnecessary work. Moreover, the data occurred during repairing have not been stored in a system that can be used in future plans. Therefore, the purpose of this study is to suggest a repairing cost and time predicting model by applying the properties of the building.

• Geomechanics and Engineering
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• 제30권5호
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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.

• 국제학술발표논문집
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• The 1th International Conference on Construction Engineering and Project Management
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• pp.913-916
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• 2005

This paper presents the development of Artificial Neural Network models for forecasting the cost and contract duration of school reconstruction projects to assist the planners' decision-making in the early stage of the projects. 132 schools reconstruction projects in central Taiwan, which received the most serious damage from the Chi-Chi Earthquake, were collected. The developed Artificial Neural Network prediction models demonstrate good prediction abilities with average error rates under 10% for school reconstruction projects. The analytical results indicate that the Artificial Neural Network model with back-propagation learning is a feasible method to produce accurate prediction results to assist planners' decision-making process.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.349-352
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• 2015

Size of building has a direct relationship with building cost, energy use and space maintenance cost. Therefore, minimizing building size during a project development is of paramount importance against such wastes. However, incautious reduction of building size may result in crowded space, and therefore harms the functionality despite the fact that building is supposed to satisfactorily support users' activity. A well-balanced design solution is, therefore, needed at an optimum level that minimizes building size in tandem with providing sufficient space to maintain functionality. For such design, architects and engineers need to be informed accurate and reliable space-use information. We present in this paper a conceptual framework of an agent-based space-use prediction simulation system that provides individual level space-use information over time in a building in consideration of project specific user information and activity schedules, space preference, ad beavioural rules. The information will accordingly assist architects and engineers to optimize space of the building as appropriate.

• Architectural research
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• 제16권2호
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• pp.75-80
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• 2014

Risks, uncertainties, and associated cost overruns are critical problems for construction projects. Cost contingency is an important funding source for these unforeseen events and is included in the base estimate to help perform financially successful projects. In order to predict more accurate contingency, many empirical models using regression analysis and artificial neural network method have been proposed and showed its viability to minimize prediction errors. However, categorical factors on contingency cannot have been treated and thus considered in these empirical models since those models are able to treat only numerical factors. This paper identified potential factors on contingency in transportation construction projects and evaluated categorical factors using the one-way ANOVA statistical method. Among factors including project work type, delivery method type, contract agreement type, bid award type, letting type, and geographical location, two factors of project work type and contract agreement type were found to be statistically important on allocating cost contingency.

• 한국산학기술학회논문지
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• 제13권9호
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• pp.4284-4293
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• 2012

건설사업 수행의 초기단계에서 가장 중요한 과제는 적정 예정공사비를 산정하는 일이다. 따라서 본 연구에서는 공동주택 건설사업 초기단계에서 합리적이고 정확한 토목공사비의 예측을 위하여 170개의 공사비자료를 활용한 회귀분석을 실시하였고, 종속변수인 토목공사비를 지역위치에 따른 전국, 부지조건에 따른 사유지, 조합부지, 공공부지로 구분하여 다양한 분석을 함으로써 예측모델의 이용의 편리성과 정확성을 높였다. 회귀식을 이용한 공동주택 토목공사비의 예측 결과 오차율은 전국 적용 예측모델 15.59%, 사유지 적용 예측모델 17.53%, 조합부지 적용 예측모델 21.86%, 공공부지 적용 예측모델 13.08%로 나타났다.