[논문]공동주택 리모델링 자동견적을 위한 DL-MCS Hybrid Expert System 개발

김준; 차희성

doi:10.6106/kjcem.2020.21.6.113

공동주택 리모델링 자동견적을 위한 DL-MCS Hybrid Expert System 개발
Development of DL-MCS Hybrid Expert System for Automatic Estimation of Apartment Remodeling 원문보기

한국건설관리학회논문집 = Korean journal of construction engineering and management, v.21 no.6, 2020년, pp.113 - 124

초록
AI-Helper

노후화된 공동주택을 리모델링을 통하여 성능개선을 하고자 하는 사회적 움직임이 포착되고 있다. 이를 위해 리모델링의 공사비 분석, 구조 분석, 정치제도적 검토등이 진행되어 리모델링을 활성화방안이 제시되고 있다. 그러나 현재 공동주택 리모델링의 공사비 분석방법이 연구상으로 제시되고 있으나, 실무적 활용가능성에서 한계점이 존재하는 상황이다. 구체적으로, 실무적으로 활용되기 위해서는 현재 진행되었거나 진행 중인 사례에는 적용가능하지만 향후 발생할 사례 또한 공사비분석에 활용되기에 분석방법의 지속 가능성이 결여되어 있다. 따라서 본 연구에서는 공동주택 리모델링 공사비견적의 지속가능성을 위해 자동화된 견적방법을 제시하고자 한다. 공사비견적의 지속가능성을 위해서 Deep-Learning을 견적절차에 도입하였다. 구체적으로, 공동주택 리모델링에서 발생할 수 있는 설계요소,공종, 공사비 상승계수의 관계를 자동적으로 찾는 방안을 제시하였다. 추가적으로 Artificial Neural Network기반 견적의 태생적인 한계점인 불확실성 미반영을 보완하고자 Monte Carlo Simulation을 견적절차에 산입하였다. 사례가 누적될수록 더 높은 정확도를 제시하기 위하여 견적결과를 기존의 누적데이터와 비교를 하여 더 높은 정확도를 산출하는 방안 또한 제시되었다. 본 연구에서 제안한 자동화된 개산견적의 지속가능성을 검토하고자 13개 사례의 학습절차와 추가 2개 사례의 누적절차를 거쳤다. 결과적으로 추가된 2개 프로젝트의 특성이 반영된 새로운 공사비견적절차가 자동적으로 제시되었다. 본 연구에서는 15개의 사례를 활용하여 개산견적의 방안을 활용하였으나, 사례가 누적되어 반영될 경우 본 연구의 기대효과는 더욱 높아질 것으로 사료된다.

Abstract ▼ AI-Helper

Social movements to improve the performance of buildings through remodeling of aging apartment houses are being captured. To this end, the remodeling construction cost analysis, structural analysis, and political institutional review have been conducted to suggest ways to activate the remodeling. However, although the method of analyzing construction cost for remodeling apartment houses is currently being proposed for research purposes, there are limitations in practical application possibilities. Specifically, In order to be used practically, it is applicable to cases that have already been completed or in progress, but cases that will occur in the future are also used for construction cost analysis, so the sustainability of the analysis method is lacking. For the purpose of this, we would like to suggest an automated estimating method. For the sustainability of construction cost estimates, Deep-Learning was introduced in the estimating procedure. Specifically, a method for automatically finding the relationship between design elements, work types, and cost increase factors that can occur in apartment remodeling was presented. In addition, Monte Carlo Simulation was included in the estimation procedure to compensate for the lack of uncertainty, which is the inherent limitation of the Deep Learning-based estimation. In order to present higher accuracy as cases are accumulated, a method of calculating higher accuracy by comparing the estimate result with the existing accumulated data was also suggested. In order to validate the sustainability of the automated estimates proposed in this study, 13 cases of learning procedures and an additional 2 cases of cumulative procedures were performed. As a result, a new construction cost estimating procedure was automatically presented that reflects the characteristics of the two additional projects. In this study, the method of estimate estimate was used using 15 cases, If the cases are accumulated and reflected, the effect of this study is expected to increase.

주제어

표/그림 (11)

그림 Fig. 1. Research procedure and content
그림 Fig. 2. Kim's (2018) Research procedure and content
그림 Fig. 3. Manual based Expert system vs Deep learning based Expert system
그림 Fig. 4. Concept of DL-MCS Estimation process
표 Table 2. Design Element
표 Table 1. Unit Area Cost
그림 Fig. 6. Design Element-Work-Probability Table
그림 Fig. 5. Type of Probability Distribution
그림 Fig. 7. Design Element-Work-Probability Table and MCS Table
그림 Fig. 8. Information from Excel data to DL
그림 Fig. 9. Coding

참고문헌 (27)

Ahn, S. (2006). "Development of Case-based Reasoning Model for Evaluating Reliability of Conceptual Cost Estimates in Canstruction Projects." Ph.D. dissertation, Korea University, Korea.
Alex, D.P., Al Hussein, M., Bouferguene, A., and Fernando, S. (2010). "Artificial neural network model for cost estimation: City of Edmonton's water and sewer installation services." Journal of construction engineering and management, 136(7), pp. 745-756.

상세보기
Al-Tabtabai, H. (1998). "A framework for developing an expert analysis and forecasting system for construction projects." Expert Systems with Applications, 14(3), pp. 259-273.

상세보기
Chae, S., Kim, K., Cha, H., Kim, K., and Han, J. (2010). "Constructability Analysis in Aged-Housing Remodeling Demolition Work for Maximizing Waste Recycling." Korean Journal of Construction Engineering and Management, KICEM, 11(3), pp. 13-23.

원문보기 상세보기
Chen, Z., and Suen, C.Y. (1994). Measuring the complexity of rule-based expert systems. Expert Systems with Applications, 7(4), pp. 467-481.

상세보기
Chen, J.X. (2016). "The evolution of computing: AlphaGo." Computing in Science & Engineering, 18(4), pp. 4-7.

상세보기
Choi, S., Son, J., Lee D., and Choe, M. (2010). "Management A Study on the Application of the Cost Data Guide in Remodeling Works." Journal of the Regional Association of Architectural Institute of Korea, 12(4), pp. 343-352.
Elmousalami, H.H. (2020). Artificial intelligence and parametric construction cost estimate modeling: stateof-the-art review. Journal of Construction Engineering and Management, 146(1), 03119008.

상세보기
Gu, H. Seo, J., and Choo, S. (2019). "Development of Facade Dataset Construction Technology Using Deep Learning-based Automatic Image Labeling." Journal of the Architectural Institute of Korea Planning & Design, 35(12), pp. 43-54.
Hasan, S.A. (2019). Optimal Alternatives by Controlling Time and Cost of a Multi-Level Car Park Using Excel-Solver Program, Journal of Southwest Jiaotong University, 54(6).
Hegazy, T., and Ayed, A. (1998). "Neural network model for parametric cost estimation of highway projects." Journal of construction engineering and management, 124(3), pp. 210-218.

상세보기
Higa, K., and Lee, H.G. (1998). "A graph-based approach for rule integrity and maintainability in expert system maintenance." Information & management, 33(6), pp. 273-285.

상세보기
Ji, S.H., Park, M., and Lee, H.S. (2010). "Data preprocessing-based parametric cost model for building projects: case studies of Korean construction projects." Journal of Construction Engineering and Management, 136(8), pp. 844-853.

상세보기
Karaca, I., Gransberg, D.D., and Jeong, H.D. (2020). "Improving the Accuracy of Early Cost Estimates on Transportation Infrastructure Projects." Journal of Management in Engineering, 36(5), 04020063.

상세보기
Kim, H., Kang, L., Lee, D., and Kim, C. (2014). "Development of a Computer System and Suggestion of Man-Hours for Demolition Cost Estimation." Journal of The Korean Society of Civil Engineers, 34(3), pp. 1007-1015.

원문보기 상세보기
Kim, J., and Cha, H. (2018). "Development of Estimation System for Housing Remodeling Cost through Influence Analysis by Design Elements." Korean Journal of Construction Engineering and Management, KICEM, 19(6), pp. 65-79.
Kim, K., and Baik, H. (2015). "Comparative Analysis between RSMeans and Standard Estimating System for Selective Finish Demolition in Remodeling Projects." Journal Of The Architectural Institute Of Korea Structure & Construction, 31(3), pp. 73-80.
Kim, G.H., Kim, G.H., An, S.H., and Kang, K.I. (2004). "Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning." Building and environment, 39(10), pp. 1235-1242.

상세보기
Lee, J., and Lee. S. (2017). "Deep Learning for Structural Analysis." Korean Association for Spatial Structures, 17(4), pp. 10-16.
Lu, M., AbouRizk, S.M., and Hermann, U.H. (2001). "Sensitivity analysis of neural networks in spool fabrication productivity studies." Journal of Computing in Civil Engineering, 15(4), pp. 299-308.

상세보기
Park, M., Han, K., Lee, H., and Leem, D. (2008). "Sustainable Cost Estimating Model for Construction Projects." Journal Of The Architectural Institute of Korea Structure & Construction, 24(10), pp. 167-179.
Rafiei, M.H., and Adeli, H. (2018). "Novel machinelearning model for estimating construction costs considering economic variables and indexes." Journal of construction engineering and management, 144(12), 04018106.

상세보기
Seo, J., Kim, S, Cho, J., and Chun, J. (2010). "Supporting Model of Cost Planning using Case-Based Reasoning in Pre-design Phase -Focused on the Case of Public Office Building-" Journal Of The Architectural Institute Of Korea Structure & Construction, 26(4), pp. 159-169.
Tijanic, K., Car-Pusic, D., and Sperac, M. (2019). Cost estimation in road construction using artificial neural network. Neural Computing and Applications, pp. 1-13.
Wang, W.C., Wang, S.H., Tsui, Y.K., and Hsu, C.H. (2012). "A factor-based probabilistic cost model to support bidprice estimation." Expert Systems with Applications, 39(5), pp. 5358-5366.

상세보기
Watson, I., Basden, A., and Brandon, P. (1992). "The client-centred approach: expert system development." Expert Systems, 9(4), pp. 181-188.

상세보기
Yau, N.J., and Yang, J.B. (1998). "Case-based reasoning in construction management." Computer-Aided Civil and Infrastructure Engineering, 13(2), pp. 143-150.

상세보기

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