[논문]인공신경망 기반의 공공청사 공사비 예산 예측모델 개발 연구

김현진; 김한수

doi:10.6106/kjcem.2023.24.5.022

[국내논문] 인공신경망 기반의 공공청사 공사비 예산 예측모델 개발 연구
A Study on the Development of Construction Budget Estimating Model for Public Office Buildings based on Artificial Neural Network 원문보기

한국건설관리학회논문집 = Korean journal of construction engineering and management, v.24 no.5, 2023년, pp.22 - 34

초록
AI-Helper

건설사업의 사업초기단계에 산정되는 공사비 예산을 적절히 예측하는 것은 발주자의 올바른 의사결정을 지원하고 건설사업의 목표를 달성하기 위해 매우 중요한 현안이다. 이는 공공 건설사업의 경우에서도 마찬가지이다. 그러나 현재 공공 건설사업의 사업초기단계에서 수행되는 공사비 예산의 예측방식은 정확성 및 신뢰성 관점에서 정교하지 못해 이에 대한 개선의 필요성이 제기되고 있다. 본 연구의 목적은 인공신경망을 활용하여 공공청사 프로젝트 사업초기단계에서 활용할 수 있는 공사비 예산 예측모델을 개발하는데 있다. 본 연구에서는 조달청에서 제공하는 데이터와 SPSS Statistics 프로그램을 활용하여 인공신경망 모델을 구축하였으며, 공사비 예산 예측의 수준을 분석하고 추가 검증을 통해 모델의 정확성을 검증하였다. 검증 결과, 개발된 인공신경망 모델은 사업초기 단계에서 활용할 수 있는 견적의 오차범위를 보여주었으며 이를 통해 다양한 프로젝트 조건(변수)을 활용하여 보다 정교하게 공사비 예산을 예측할 수 있는 가능성을 시사하였다.

Abstract ▼ AI-Helper

Predicting accurately the construction cost budget in the early stages of construction projects is crucial to support the client's decision-making and achieve the objectives of the construction project. This holds true for public construction projects as well. However, the current methods for predicting construction cost budgets in the early stages of public construction projects are not sophisticated enough in terms of accuracy and reliability, indicating a need for improvement. The objective of this study is to develop a construction cost budget prediction model that can be utilized in the early stages of public building projects using an artificial neural network (ANN). In this study, an artificial neural network model was developed using the SPSS Statistics program and the data provided by the Public Procurement Service. The level of construction cost budget prediction was analyzed, and the accuracy of the model was validated through additional testing. The validation results demonstrated that the developed artificial neural network model exhibited an error range for estimates that can be utilized in the early stages of projects, indicating the potential to predict construction cost budgets more accurately by incorporating various project conditions.

주제어

표/그림 (11)

그림 Fig. 1. Structure of artificial neural network
그림 Fig. 2. Structure of multi layer perceptron
표 Table 1. Variables of the model
표 Table 2. Summary of model information
표 Table 3. Summary of case processing
표 Table 4. Summary of sum of squared errors
그림 Fig. 3. Graphs comparing predicted and observed values
그림 Fig. 4. Graphs comparing residual and observed values
표 Table 5. Importance of independent variables
표 Table 6. Project stage's estimate error tolerance of AACEI
표 Table 7. Rsults of validation data predict

참고문헌 (25)

Baccarini, D. (2005). Estimating Project Cost Contingency？- Beyond the 10% Syndrome. Conference Paper of？Australian Institute of Project Management National？Conference, Nov 9 2005. Victoria: Australian Institute？of Project Management.
Chandgude, S.E., and Sarode G.C. (2020). "To Study Cost？Prediction Analysis of Construction Project Using ANN？Model(Artificial Neural Network) SVM by Matlab."？Journal of Emerging Technologies and Innovative？Research (JETIR), JEITIR, 7(7), pp. 291-306.
Gunaydin, H.M., and Dogan, S.Z. (2004). "A Neural Network？Approach for Early Cost Estimation of Structural？Systems of Buildings." Journal of Elsevier, Elsevier,？22(7), pp. 595-602.
Han, H.D., Kim, J.H., Yoon, J.H., and Seo, J.W. (2011). "Road？Construction Cost Estimation Model in the Planning？Phase Using Artificial Neural Network." Journal of？Korean Society of Civil Engineering, Korean Society of？Civil Engineering, 31(6), pp. 829-837.
Hashemi, S.T., Ebadati, O.M., and Kaur, H. (2020). Cost？Estimation and Prediction in Construction Projects: a？Systematic Review on Machine Learning Techniques.？SN Applied Science Research Report, Review Paper.
Heo, J.Y. (2014). "Prediction of Road-Construction Cost？Using an Artificial Neural Network Method." Master？Thesis, Journal of Kang-Won University, Kang-Won？University, Kangwondo, Korea.
Hong, S.I. (2021). Recent Research Trends and？Revitalization of Machine Learning in Building？Operation and Maintenance. KICEM Research Report,？22(2).
Ji, S.H., Park, M.S., Lee, H.S., and Yoon, Y.S. (2008).？"Direction for Improving Cost Estimation and？Management of Construction Projects: Comparing to？Australian System." Korean Journal of Construction？Engineering and Management, KICEM, 9(2), pp. 170-181.
Jo, Y.H., Choi, H.J., Kim, J.W., and Yun, S.H. (2020).？"Conceotual Cost Estimate Method of Public Office？Building Structural Frame Work by Regression？Analysis." Journal of the Korea Institute of Building？Construction, KIC, 20(2), pp. 147-153.
Jung, E.H., Yeom, D.J., Kim, J.S., and Kim, Y.S. (2021).？"Development of Construction Cost Estimation Model？for Elementary, Middle and High School Facilities."？Korean Journal of Construction Engineering and？Management, KICEM, 22(3), pp. 69-78.
Kim, C.Y., and Son, J.H. (2006). "A Study on the Model？of Artificial Neural Networkfor Construction Cost？Estimation of Educational Facilities at Conceptual？Stage." Korean Journal of Construction Engineering and？Management, KICEM, 7(4), pp. 91-99.
Kim, I.K., and Park, J. (2022). "A Study on Improvement of？Construction Cost Estimation Method in the Feasibility？Study Stage of Health Facilities in ODA Project." Journal？of Korea Association of International Development and？Cooperation, KAIDEC, 14(1), pp. 99-118.
Kim, J.H., Jun, S.M., Hwang, S.H., Kim, H.K., Heo, J.M., and？Kang, M.S. (2021). "Impact of Activation Functions on？Flood Forecasting Model Based on Artificial Neural Networks." Journal of the Korean Society of Agricultural？Engineers, KSAE, 63(1), pp. 11-25.
Kim, Y.C., Yoo, W.S., and Shin, Y.S. (2017). "Application of？Artificial Neural Networks to Prediction of Construction？Safety Accidents." Journal of Korean Society of Hazard？Mitigation, KOSHAM, 17(1), pp. 7-14.

상세보기
Korea Institute of Procurement. (2021). 「2022 Construction？Order Guidebook.」 Korea Institute of Procurement？(2021), pp. 1-51.
Kulkarni, P.S., Londhe, S.N., and Deo, S.N. (2017). "Artificial？Neural Networks for Construction Management:？A Review." Journal of Soft Computing in Civil？Engineering, SCCE, 1(2), pp. 70-88.
Lee, D.H. (2018). Artificial Neural Network to Improve？Learning Effectiveness of Artificial Intelligence.？Telecommunications Technology Association (TTA)？Research Report.
Lee, H.S., Jung, M.J., Park, M.S., and Son, B.S. (2010). "Cost？Estimating for Public Facilities at Early Stage Using？Functional Area Cost -Focusing on Army Barracks-"？Korean Journal of Construction Engineering and？Management, KICEM, 6(1), pp. 3-13.
Lee, J.G., Lee, H.S., Park, M.S., and Seo J.O. (2021). "Early-Stage Cost Estimation Model for Power Generation？Project with Limited Historical Data." Journal of？Emarald, Emerald, ISSN 0969-9988.
Lee, J.Y., and Lee, B.J. (2021). "Application of Artificial？Neural Network to Predict Aerodynamic Coefficients of？the Nose Section of the Missiles." Journal of The Korean？Society for Aeronautical & Space Science, KSAS, 49(11),？pp. 901-907.
Oh, S.B., and Lim, H.I. (2019). "A Study on Hidden Layer？for Neural Network Model." Proceedings of the Korea？Information Processing Society Conference, KIPS, 26(1),？pp. 467-769.
Park, H.J., Choi, S.B., and Kang, C.W. (2020). "Determination？of Input Nodes for Neural Network Model in Time？Series Data." Journal of the Korean Data Analysis？Society, KDAS, 22(3), pp. 1055-1065.

상세보기
Park, M.S., Seong, K.H., Lee, H.S., Ji, S.H., and Kim, S.Y.？(2010). "Schematic Cost Estimation Method using Case-Based Reasoning: Focusing on Determining Attribute？Weight." Korean Journal of Construction Engineering？and Management, KICEM, 11(4), pp. 22-31.
Van, S.G., Le-Hoai, L., and Dang, C.N. (2019). "Predicting？Implementation Cost Contingencies for Residential？Construction Projects in Flood-Prone Areas." Journal of？Emerald, Emerald, 12(10), pp. 1097-1119.
Yoon, J.M. (2019). "Effectiveness Analysis of Credit Card？Default Risk with Deep Learning Neural Network."？Journal of Korea Money and Finance Association,？KMFA, 33(1), pp. 151-183.

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