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Development of a Decision Making Model for Efficient Rehabilitation of Sewer System

효율적인 하수관거 개량을 위한 의사결정모형의 개발

  • Lee, Jung-Ho (Dept. of Civil, Environmental and Architectural Engrg., Korea Univ.) ;
  • Jun, Hwan-Don (Dept. of Civil Engineering, Hanbat National University) ;
  • Joo, Jin-Gul (Dept. of Civil, Environmental and Architectural Engrg., Korea Univ.) ;
  • Kim, Joong-Hoon (Dept. of Civil, Environmental and Architectural Engrg., Korea Univ.)
  • 이정호 (고려대학교 공과대학 건축.사회환경공학과) ;
  • 전환돈 (한밭대학교 토목공학과) ;
  • 주진걸 (고려대학교 공과대학 건축.사회환경공학과) ;
  • 김중훈 (고려대학교 공과대학 건축.사회환경공학과)
  • Published : 2008.02.29

Abstract

The objective of sewer rehabilitation is to improve its function while eliminating inflow/infiltration (I/I) and insufficient carrying capacity (ICC). Such rehabilitation efforts, however, have not been particularly successful due to a lack of sewer data and unsystematic field practices. The present study aimed to solve these problems by developing a decision making model consisting of two models: the rehabilitation weighting model (RWM) and the rehabilitation priority model (RPM). In RWM, the I/I of each pipe in a drainage district is estimated according to various defects, with each defect given an individual weighting factor using an analytic hierarchy process (AHP). RPM determines the optimal rehabilitation priority (ORP) using a genetic algorithm (GA). The developed models can be used to overcome the problems associated with unsystematic practices and, in practice, as a decision making tool for urban sewer system rehabilitation.

하수관거 개량사업의 주된 목적은 Inflow/Infiltration (I/I)를 제거 및 통수능력 확보이다. 최근 노후 하수관거의 개 보수 및 신설 사업이 활발히 이루어지고 있으나 현재의 사업들은 관거 데이터의 부족, 유량 및 수질 자료의 장기적인 측정 미비 등으로 인하여 효율적인 사업을 진행시키기에 무리가 있다. 본 연구에서는 하수관거 개량사업을 보다 효율적으로 진행시키기 위하여 Rehabilitation Weighting Model (RWM)과 Rehabilitation Priority Model (RPM)로 구성된 의사결정모형을 개발하였다. RWM은 시간 및 예산상의 제약으로 인하여 주요 지점에서만 관측되는 I/I를 상류의 각 관거별로 I/I를 산정하며, 관거별 I/I는 Analytic Hierarchy Process (AHP)를 통하여 산정된 8개 결함항목별 가중치에 따라서 결정된다. RPM은 Genetic Algorithm (GA)를 이용하여 소유역별 최적개량우선순위를 산정한다. 이것은 공사 기간 중 발생하는 I/I를 최소화시키기 위한 소유역별 공사 순서를 설정함으로써 하수처리장의 처리비용을 절감시킴으로써 하수관거 개량사업의 효율적인 시행을 위한 판단 기준을 제시해준다.

Keywords

References

  1. 구리시 (2001). 구리시 하수도정비 기본계획보고서
  2. 구리시 (2002). 한강수계 하수관거정비 시범사업 타당성조사보고서
  3. 서울특별시 (1998). 하수관거조사 및 정비 기본설계보고서(불광배수구역)
  4. 이정호, 김중훈, 김형수, 김응석, 조덕준 (2004). '최적 도시유출시스템의 개발 : I. 도시유출시스템에서의 AHP를 고려한 불명수량 산정에 대한 연구.' 한국수자원학회논문집, 한국수자원학회, 제 37권, 제3호, pp. 195-206
  5. 이정호, 김중훈, 김형수, 조덕준, 김응석 (2004). '최적도시유출시스템의 개발 : II. 도시유역의 최적 유출시스템 제어를 위한 의사결정모형의 개발.' 한국수자원학회논문집, 한국수자원학회, 제 7권, 제3호, pp. 207-217
  6. Abraham, D.M., Gillani, S.A. (1999).'Innovations in Material for Sewer System Rehabilitation.' Trenchless Technology Research, Vol 14, No. 1, pp. 43-56 https://doi.org/10.1016/S0886-7798(99)00003-6
  7. Brousseau, E (1997). 'Trenchless Sewer Rehabilitation vs. Reconstruction. DM Robichaud Associates' Ltd. http://www. dmrobichaud.com/rehabchoices.htm
  8. Belhadj, N., Joannis, C. and Raimbault, G. (1995). 'Modelling of Rainfall Induced Infiltration into Separate Sewerage.' Water Science and Technology, Vol. 32, No. 1. pp. 161-168 https://doi.org/10.1016/0273-1223(95)00551-W
  9. Chae, M.J. and Abraham, D.M. (2001) 'Neuro-Fuzzy Approaches for Sanitary Sewer Pipeline Condition Assessment.' Journal of Computing in Civil Engineering, Vol. 15, No. 1, pp. 4-14 https://doi.org/10.1061/(ASCE)0887-3801(2001)15:1(4)
  10. deMonsabert, S. and Thornton, P. (1997) 'A benders decomposition model for sewer rehabilitation planning for infiltration and inflow planning.' Water Environment Research, Vol. 69, No. 2, pp. 162-167 https://doi.org/10.2175/106143097X125317
  11. deMonsabert, S., Ong, C. and Thornton, P. (1999). 'An integer programming for optimizing sanitary sewer rehabilitation over a planning horizon.' Water Environment Research, Vol. 74, No. 7, pp. 1292-1297 https://doi.org/10.2175/106143096X122429
  12. Fenner, R.A. and Sweeting, L. (1999). 'A Decision Support Model for the Rehabilitation of Non-Critical Sewer.' Water Science Tech- nology, Vol. 39, No. 9, pp. 193-200 https://doi.org/10.1016/S0273-1223(99)00233-4
  13. Goldberg, D.E. (1990). 'Genetic Algorithms in Search, Optimization and Machine Learning.' Addison-Wesley, Reading,MA
  14. Holland, J.H. (1975). 'Adaptation in Natural and Artificial Systems.' Univ. MI Press
  15. Hoffman, M. (2000). 'New Developments in Pipeline Rehabilitation Technology.' Environ- mental and Pipeline Engineering 2000, pp. 348-357
  16. Lin, Z.C. and Yang, C.B. (1996). 'Evaluation of machine selection by the AHP method.' Journal of materials Processing Technology, Vol. 57, No. 3, pp. 253-258 https://doi.org/10.1016/0924-0136(95)02076-4
  17. Moselhi, O. and Tariq, S.E. (2000). 'Classification of Defects in Sewer Pipes using Neural Networks.' Journal of Infrastructure System, Vol. 6, No. 3, pp. 97-104 https://doi.org/10.1061/(ASCE)1076-0342(2000)6:3(97)
  18. Reyna, S.M., Vanegas, J.A. and Khan, A.H. (1994). 'Construction Technologies for Sewer Rehabilitation.' Journal of Construction Engineering and management, Vol. 120, No. 3, pp. 467-487 https://doi.org/10.1061/(ASCE)0733-9364(1994)120:3(467)
  19. Thomas, L.S. (1990). 'How to make a decision : The Analytic Hierarchy Process.' European Journal of Operational Research, Vol. 48, No. 1, pp. 9-16 https://doi.org/10.1016/0377-2217(90)90057-I
  20. Thomas, L.S. (1994). 'Highlights and critical points in the theory and application of the analytical hierarchy process.' European Journal of Operational Research, Vol. 74, No. 3, pp. 426-447 https://doi.org/10.1016/0377-2217(94)90222-4
  21. Vargas, L.G. (1990). 'An overview of the analytic hierarchy process and its applications.' European Journal of Operational Research, Vol. 48, No. 1, pp. 2-8 https://doi.org/10.1016/0377-2217(90)90056-H
  22. Wirahadikusumah, R., Abraham, D.M., Iseley, T. and Prasanth, R.K. (1998). 'Assessment Technologies for Sewer System Rehabilitation.' Automation in Construction, Vol. 7, No. 4, pp. 259-270 https://doi.org/10.1016/S0926-5805(97)00071-X