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Dual-optimization method for improving accuracy in GA-CBR cost estimating model

김수영 2017년

논문상세정보
    • 저자 김수영
    • 기타서명 유전알고리즘-사례기반추론 공사비 예측 모델의 정확도 향상을 위한 듀얼 옵티마이제이션 방법
    • 형태사항 26 cm: xi, 198 p.: 삽화
    • 일반주기 참고문헌 수록
    • 학위논문사항 2017. 2, 서울대학교 대학원, 건축학과, 학위논문(박사)-
    • DDC 690, 22
    • 발행지 서울
    • 언어 eng
    • 출판년 2017
    • 발행사항 서울대학교 대학원
    유사주제 논문( 1,188)
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  • Yau, N. J., & Yang, J. B. (1998). “Case-based reasoning in construction management.” Computer-Aided Civil and Infrastructure Engineering, 13(2), 143-150.
  • Watson, I., & Marir, F. (1994). “Case-based reasoning: A review.” Knowledge Engineering Review, 9(4), 327-354.
  • Watson, I. (1999). “Case-based reasoning is a methodology not a technology.” Knowledge-based systems, 12(5), 303-308.
  • Turban, E., & Frenzel, L. E. (1992). Expert systems and applied artificial intelligence. Prentice Hall Professional Technical Reference.
  • Trost, S. M., & Oberlender, G. D. (2003). “Predicting accuracy of early cost estimates using factor analysis and multivariate regression.” Journal of Construction Engineering and Management, 129(2), 198-204.
  • Shiu, S. C., & Pal, S. K. (2004). “Case-based reasoning: concepts, features and soft computing.” Applied Intelligence, 21(3), 233-238.
  • Sharifi, M., Naghibzadeh, M., & Rouhani, M. (2013). “Adaptive case-based reasoning using support vector regression.” Advance Computing Conference (IACC), 2013 IEEE 3rd International, 1006-1010.
  • Seni, G., & Elder, J. F. (2010). “Ensemble methods in data mining: improving accuracy through combining predictions.” Synthesis Lectures on Data Mining and Knowledge Discovery, 2(1), 1-126.
  • Ryu, H. G., Lee, H. S., & Park, M. (2007). “Construction planning method using case-based reasoning (CONPLA-CBR).” Journal of Computing in Civil Engineering, 21(6), 410-422.
  • Riesbeck, C. K., & Schank, R. C. (1989). Inside case-based reasoning. Lawrence Earlbaum, Hillsdale, NJ.
  • Richter, M. M., & Weber, R. O. (2013). “Complex Similarity Topics.” Case- Based Reasoning, 149-165.
  • Qi, J., Hu, J., & Peng, Y. (2012). “A new adaptation method based on adaptability under k-nearest neighbors for case adaptation in case-based design.” Expert Systems with Applications, 39(7), 6485-6502.
  • Policastro, C. A., Carvalho, A. C., & Delbem, A. C. (2008). “A hybrid case adaptation approach for case-based reasoning.” Applied Intelligence, 28(2), 101-119.
  • Policastro, C. A., Carvalho, A. C., & Delbem, A. C. (2003). “Hybrid approaches for case retrieval and adaptation.” Annual Conference on Artificial Intelligence, 297-311.
  • Paulson Jr, B. C. (1976). Designing to reduce construction costs. Journal of the construction division, 102(C04).
  • Patterson, D. W., Rooney, N., & Galushka, M. (2003). “Efficient Retrieval for Case-Based Reasoning.” In FLAIRS Conference, 144-149.
  • Park, C. S. & Han, I. (2002). A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction. Expert Systems with Applications, 23(3), 255-264.
  • Montazemi, A. R., & Gupta, K. M. (1997). “A framework for retrieval in case-based reasoning systems.” Annals of operations research, 72, 51-73.
  • McLachlan, G., Do, K. A., & Ambroise, C. (2005). Analyzing microarray gene expression data. John Wiley & Sons.
  • Luu, D. T., Ng, S. T., & Chen, S. E. (2005). “Formulating procurement selection criteria through case-based reasoning approach.” Journal of computing in civil engineering, 19(3), 269-276.
  • Lotfy, E. A., & Mohamed, A. S. (2002). “Applying neural networks in casebased reasoning adaptation for cost assessment of steel buildings.” International journal of computers and applications, 24(1), 28-38.
  • Liao, Z., Mao, X., Hannam, P. M., & Zhao, T. (2012). “Adaptation methodology of CBR for environmental emergency preparedness system based on an Improved Genetic Algorithm.” Expert Systems with Applications, 39(8), 7029-7040.
  • Korea Institute of Construction Technology, Construction Cost Index, 2016. Leake, D. B. (1996). Case-Based Reasoning: Experiences, lessons and future directions. MIT press.
  • Koo, C., Hong, T., & Hyun, C. (2011). “The development of a construction cost prediction model with improved prediction capacity using the advanced CBR approach.” Expert Systems with Applications, 38(7), 8597-8606.
  • Kolodner, J. (1993). Case-based reasoning, Morgan Kaufmann Publishers Inc., New York, USA.
  • Kim, K. J., & Kim, K. (2010). “Preliminary cost estimation model using casebased reasoning and genetic algorithms.” Journal of Computing in Civil Engineering, 24(6), 499-505.
  • Kim, G., & Kang, K. (2004). “A study on predicting construction cost of apartment housing projects based on case based reasoning technique at the early project stage.” Journal of Architectural Institute of Korea, 20(5), 83-92.
  • Ji, S. H., Park, M., Lee, H. S., Ahn, J., Kim, N., & Son, B. (2010). “Military facility cost estimation system using case-based reasoning in Korea.” Journal of Computing in Civil Engineering, 25(3), 218-231.
  • Ji, S. H., Park, M., & Lee, H. S. (2011). “Cost estimation model for building projects using case-based reasoning.” Canadian Journal of Civil Engineering, 38(5), 570-581.
  • Ji, S. H. (2011). “Improvement of retrieval and adaptation methods in casebased reasoning for construction cost estimation.” Ph. D. dissertation, Seoul National Univ., Seoul, South Korea.
  • Ji, C., Hong, T., & Hyun, C. (2010). “CBR revision model for improving cost prediction accuracy in multifamily housing projects.” Journal of Management in Engineering, 26(4), 229-236.
  • Hu, J., Qi, J., & Peng, Y. (2015). “New CBR adaptation method combining with problem–solution relational analysis for mechanical design.” Computers in Industry, 66, 41-51.
  • Hong, T., Hyun, C., & Moon, H. (2011). “CBR-based cost prediction model- II of the design phase for multi-family housing projects.” Expert Systems with Applications, 38(3), 2797-2808.
  • Holland, J. H. (1975). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. U Michigan Press.
  • Hegazy, T., & Ayed, A. (1998). “Neural network model for parametric cost estimation of highway projects.” Journal of Construction Engineering and Management, 124(3), 210-218.
  • Golberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addion wesley, 1989, 102.
  • Goh, Y. M., & Chua, D. K. H. (2009). “Case-based reasoning approach to construction safety hazard identification: adaptation and utilization.” Journal of Construction Engineering and Management, 136(2), 170-178.
  • Gen, M., & Cheng, R. (2000). Genetic algorithms and engineering optimization. John Wiley & Sons.
  • Garza, A. D. G. S., & Maher, M. L. (1999). “An evolutionary approach to case adaptation.” International Conference on Case-Based Reasoning 1999, 162-173.
  • G naydın, H. M., & Doğan, S. Z. (2004). “A neural network approach for early cost estimation of structural systems of buildings.” International Journal of Project Management, 22(7), 595-602.
  • Fushiki, T. (2011). “Estimation of prediction error by using K-fold crossvalidation.” Statistics and Computing, 21(2), 137-146.
  • Elhag, T. M. S., & Boussabaine, A. H. (1998). “An artificial neural system for cost estimation of construction projects.” 14th ARCOM Annual Conference, 219-226.
  • Duverlie, P., & Castelain, J. M. (1999). “Cost estimation during design step: parametric method versus case based reasoning method.” The international journal of advanced manufacturing technology, 15(12), 895-906.
  • Doğan, S. Z., Arditi, D., & G naydın, H. M. (2006). “Determining attribute weights in a CBR model for early cost prediction of structural systems.” Journal of Construction Engineering and Management, 132(10), 1092-1098.
  • Dikmen, I., Birgonul, M. T., & Gur, A. K. (2007). “A case-based decision support tool for bid mark-up estimation of international construction projects.” Automation in Construction, 17(1), 30-44.
  • De Mantaras, R. L., McSherry, D., Bridge, D., Leake, D., Smyth, B., Craw, S., Faltings, B., Maher, M. L., Cox, M. T., Forbus, K., Aamodt, A., & Watson, I. (2005). “Retrieval, reuse, revision and retention in case-based reasoning.” The Knowledge Engineering Review, 20(3), 215-240.
  • Chun, S. H., & Park, Y. J. (2006). “A new hybrid data mining technique using a regression case based reasoning: Application to financial forecasting.” Expert Systems with Applications, 31(2), 329-336.
  • Chua, D. K. H., Li, D. Z., & Chan, W. T. (2001). “Case-based reasoning approach in bid decision making.” Journal of construction engineering and management, 127(1), 35-45.
  • Christensen, P., & Dysert, L. R. (2005). Cost estimate classification system– as applied in engineering, procurement, and construction for the process industries. Morgantown, WV: AACE International.
  • Burkhard, H. D. (2001). “Similarity and distance in case based reasoning.” Fundamenta Informaticae, 47(3-4), 201-215.
  • Brown, C. E., & Gupta, U. G. (1994). “Applying Case-Based Reasoning to the Accounting Domain.” Intelligent Systems in Accounting, Finance and Management, 3(3), 205-221.
  • Begum, S., Ahmed, M. U., Funk, P., Xiong, N., & Von Sch ele, B. (2009). “A case-based decision support system for individual stress diagnosis using fuzzy similarity matching.” Computational Intelligence, 25(3), 180-195.
  • Barrie, D. S., & Paulson Jr, B. C. (2000). Professional construction management: including CM, design-construct, and general contracting. McGraw-Hill. 3rd edition.
  • Barletta, R. (1991). “An introduction to case-based reasoning.” AI expert, 6(8), 42-49.
  • Baker, J. E. (1985). Adaptive selection methods for genetic algorithms. International Conference on Genetic Algorithms and their applications, 101- 111.
  • Arditi, D., & Tokdemir, O. B. (1999). “Comparison of case-based reasoning and artificial neural networks.” Journal of computing in civil engineering, 13(3), 162-169.
  • Arafa, M., & Alqedra, M. (2011). “Early stage cost estimation of buildings construction projects using artificial neural networks.” Journal of Artificial Intelligence, 4(1), 63-75.
  • An, S. H., Kim, G. H., & Kang, K. I. (2007). “A case-based reasoning cost estimating model using experience by analytic hierarchy process.” Building and Environment, 42(7), 2573-2579.
  • Ahuja, H. N., Dozzi, S. P., & Abourizk, S. M. (1994). Project management: techniques in planning and controlling construction projects. John Wiley & Sons.
  • Ahn, J., Ji, S. H., Park, M., Lee, H. S., Kim, S., & Suh, S. W. (2014). “The attribute impact concept: Applications in case-based reasoning and parametric cost estimation.” Automation in Construction, 43, 195-203.
  • Ahn, J. (2016). “Front-end cost estimation by selective case-based reasoning for building construction projects.” Ph. D. dissertation, Seoul National Univ., Seoul, South Korea.
  • Aamodt A., Plaza E. 1994. "Case-based reasoning: foundational issues, methodological variations, and system approaches”. AI Communications, 7(1):39-59