• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2004년도 가을 학술발표회 논문집
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• pp.150-156
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• 2004

Though the concept of Life-Cycle Cost (LCC) itself is not new, its effectiveness for planning, design, rehabilitation and maintenance/management of civil infrastructures is becoming increasingly recognized. For the decision problems as in the case of the LCC of plant facilities, equipments, bridge decks, pavements, etc., the Life-Cycle Cost Analysis (LCCA) is relatively simple, and thus its practical implementation is rather straightforward. However, when it comes to major infrastructures such as bridge, tunnels, underground facilities, etc., the LCCA problem becomes extremely complex because lack of cost data associated with various direct and indirect losses, and the absence of uncertainty data available for the assessment as well. As a result, the LCC studies have been largely limited only to those relatively simple LCCA problems of planning or conceptual design for making decisions. Accordingly, in the recent years, the researchers have pursued extensive studies on the LCC effectiveness mostly related to LCC models and frameworks for civil infrastructures. Moreover, recently the demand on the practical application of LCC effective decisions in design and maintenance is rapidly growing unprecedently in civil engineering practice. Indirction cost is very important on LCC formulation. But that is very difficult and complicate the estimation every LCC. The objective of this paper is to suggest efficient regression model for the estimation of indirect cost approach to the practical application of LCC for the design and rehabilitation of civil. infrastructures considering traffic, traffic network, detour condition, and workzone condition. In this paper, it performed the sensitivity analysis and correlation analysis of parameter for development of regression model of inflection cost.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.1183-1188
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• 2009

Estimating project costs during the early stage of conceptual planning is very important when deciding whether to approve the project and allocate an appropriate budget. However, due to greater uncertainties involved in a project, it is challenging to estimate costs during this initial stage within a reasonable tolerance. This paper attempts to develop a cost-estimate model for public road projects under these circumstances and limitations. In the conceptual planning stage of a road project, there is only limited information for cost estimation, for example, such input data as total length of the route, origin and destination, number of lanes, general geographic characteristics of the route, and other basic attributes. This implies that the model should individuate suitable but restricted information without considering detailed features such as quantity of earthwork and a detailed route of a given condition. With these limited facts, this paper applies a case-based reasoning (CBR) method to solve a new problem by deriving similar past problems, which in turn is used to estimate the cost of a given project based on best-fitted previous cases. To develop a CBR cost-estimate model, the authors classified 8 representative variables, including project type, the number of lanes, total length, road design grades, etc. Then, we developed the CBR model, primarily by using 180 actual cases of public road projects, procured over the last decade. With the CBR model, it was found that the degree of error in estimation can be reasonably reduced, to below approximately 30% compared to the final costs estimated upon the completion of detailed design.

• 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.

• 한국건설관리학회논문집
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• 제9권3호
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• pp.65-74
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• 2008

국내 건설산업의 공공 발주자는 사업의 초기단계의 사업비관리에 대한 중요성을 충분히 인식하고 있음에도 불구하고 체계화된 공사비 산정시스템을 보유하고 있지 못한 실정이다. 이에 신규 공공 건설공사를 기획하고 사업비를 책정하는 단계에서는 예산을 수립하는 담당자가 기존의 실적데이터와 경험을 바탕으로 공사비를 추정하고 있으며, 기본설계단계와 실시설계단계 이후 산정된 공사비를 책정된 예산과 비교 후 사업의 추진여부를 검토하거나 설계내용을 예산에 맞도록 변경하고 있다. 이에 본 연구에서는 공공아파트의 사업초기단계에서 공사비를 산정할 수 있는 공공아파트 계획설계단계에서의 공사비 예측모델을 개발하고자 하였다. 본 연구의 결과를 바탕으로 계획설계단계에서 공사비를 예측하고 사업비 및 전반적인 설계를 관리한다면 지금보다 더 효율적인 방법으로 국가의 재원을 적절하게 책정하고 집행할 수 있을 것으로 판단된다.

• 한국BIM학회 논문집
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• 제5권2호
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• pp.34-45
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• 2015

As buildings become larger and more complicated, construction costs have increased with a considerable effect on buildings' Life Cycle Cost (LCC). However, there has been little consideration on economic aspects in the selection of construction materials due to limited information on the materials and dependency in architects' experience and inefficiency in cost estimation, causing design changes, increase in maintenance cost, difficulty in budgeting, and decrease in building performance. To solve these problems, this study proposed a BIM-based material selection model which reflects the comprehensive economic efficiency of building materials. Our cost prediction model can estimates the material-related cost during the entire building life cycle. Furthermore, we implemented the proposed model in connection with BIM, which can analyze and compare LCC by material. Through the validation of the model, we could confirm the necessity of LCC-based material selection in comparison with the conventional cost-centered material selection.

• 대한기계학회논문집A
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• 제38권8호
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• pp.911-916
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• 2014

레저보트산업은 미래형 고부가가치 산업으로 최근 국내에서는 한국형 고속레저낚시보트에 대한 연구가 수행되었다. 본 논문에서는 고속레저낚시보트의 대량생산을 위한 기초연구로서 제품구성모델 기반의 건조공수 추정 연구를 수행하였다. 이를 위해 레저보트의 건조공정을 분석하고 범용 작업분류체계를 적용하였으며, 업체 설문조사를 통해 계층분석법 기반의 레저보트 건조공수 비례계수를 도출하였다. 또한 실적선의 공수를 기반으로 비례계수를 이용한 개발선 후보들의 정량적인 공수추정을 수행하였으며, 그 결과 실적선 공수 대비 17% 감소된 쌍동형 개발선을 최종 선정할 수 있었다. 이는 개발된 레저보트의 설계가 실제 건조단계에서 가격경쟁력을 갖출 수 있는 양산가능수준의 제품임을 의미한다.

• Journal of Construction Engineering and Project Management
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• 제4권4호
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• pp.9-16
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• 2014

The purpose of this paper is to develop a model for forecasting early design construction cost of building projects using Artificial Neural Network (ANN). Eighty questionnaires distributed among construction organizations were utilized to identify significant parameters for the building project costs. 169 case studies of building projects were collected from the construction industry in Gaza Strip. The case studies were used to develop ANN model. Eleven significant parameters were considered as independent input variables affected on "project cost". The neural network model reasonably succeeded in estimating building projects cost without the need for more detailed drawings. The average percentage error of tested dataset for the adapted model was largely acceptable (less than 6%). Sensitivity analysis showed that the area of typical floor and number of floors are the most influential parameters in building cost.

• 한국수자원학회논문집
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• 제35권2호
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• pp.187-194
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• 2002

하천유역 면적강우량 산정의 정확도를 개선하기 위하여 기존 강우관측자료의 통계적 특성을 이용한 강우관측망의 최적설계방법을 연구하였다. 최적설계를 위한 목적함수는 면적강우량의 추정오차 및 지점강우량 관측비용의 항으로 구성하고, 그 값이 최소인 관측망은 선정하였다. 통계f7파의 추정방법으로는 통계적 분산 산정방법인 크리깅 모형을 채택하였다. 비용은 강우관측소의 설치비와 연간운영 비론 적용하고, 오차항과 비용항의 통합에는 등치매개변수를 이용하였다. 연구된 최적설계방법을 댐 신설로 강우관측소 증설이 필요한 용담댐 유역에 적용하여, 대상유역의 최적 강우관측망을 제안하였다.

• 한국농공학회:학술대회논문집
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• 한국농공학회 1999년도 Proceedings of the 1999 Annual Conference The Korean Society of Agricutural Engineers
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• pp.359-364
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• 1999

Providing the reasonable construction cost at the initial stage of the rural development project, is a kety factor of the each step of project , such as propriety analysis , cost planning , design, and planning the progress of work. The explainable construction cost can be estimated at the early stage using the actual cost data by statistical analysis. In this study, the influence factors are extracted by factor analysis with the actual cost data of rural development project, object cost model is developed by multiple regression analysis, and verify the developed cost model by Monte-Carlo simulation .

• 한국항공우주학회지
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• 제40권4호
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• pp.364-374
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• 2012

본 연구에서는 발사체의 개념설계 단계 동안 성능 변수뿐만 아니라 신뢰성에 따른 개발비용 추정방안을 분석하고자 하였다. 과거에는 발사체의 개발비용을 예측하기 위해서 주로 발사체의 성능과 건조 질량 등을 독립변수로 하는 추정 방법이 많이 이용되었다. 이러한 접근 방법은 비교적 근사하였지만 신뢰성 수준에 따른 비용 변화를 반영할 수 없기 때문에 원하는 신뢰성을 가진 발사체의 개발비용을 근접하게 예측하기는 쉽지 않았다. 본 논문에서는 이러한 비용추정 방법의 한계를 개선하기 위해 성능과 질량을 기반으로 한 비용 모델인 TRANSCOST에 신뢰성 개념을 도입하여 발사체 비용을 추정할 수 있는 방법론을 제시하였다. 이를 기반으로 신뢰성에 따른 한국형발사체(KSLV- II)의 개발비용을 추정했다.