This study presents a sanitary sewer management decision-making framework incorporating demand forecasting and life cycle cost analysis. The framework provides the asset managers with an alternative approach in sewer management. It is designed to allow asset managers to better allocate limited funds for maintenance and rehabilitation by identifying possible problematic sewers and devising a maintenance plan to prevent costly sewer failures. Sewer demand forecasting model is developed using an artificial neural network. The forecasted sewer demand is then used to identify “critical” areas, where the current hydraulic capacity is less than the forecasted sewer demand. In such areas, an optimal maintenance and rehabilitation strategy is developed through the application of probabilistic dynamic programming in conjunction with Markov chain deterioration modeling.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
ACE (1998).William Creek basin 18 sanitary sewer evaluation survey report for the City of Indianapolis. American Consulting Engineers, Inc., Indianapolis, IN.
ADS (2000).City of Indianapolis wet weather report January–December 1999. ADS Environmental Services Inc., Indianapolis, IN.
Arditi, D.A. and Messiha, M.H. (1996). “Life-cycle costing in municipal construction projects.”Journal of Infrastructure Systems, ASCE, Vol. 1, No. 1, pp. 33–43.
ASCE (2003).2003 Progress Report for America's Infrastructure. Available from http://www.asce.org/reportcard.
ASCE (1982).Gravity sanitary sewer design and construction. Manuals and reports 60, ASCE and Water Pollution Control Federation, New York, NY.
Bond Market Association (2001).The Bond Market Association Municipal Swap Index, New York, NY.
Butt, A.A., Shahin, M.Y., Carpenter, S.H., and Carnahan, J.V. (1994). “Application of Markov process to pavement management systems at network level.” Third International Conference on Managing Pavements,Transportation Research Board, Vol. 2, pp. 159–172.
Czepiel, E. (1995). Bridge management systems: literature reviewTechnical Report 11, Infrastructure Technology Institute, Northwestern University, Chicago, IL.
Chouinard, L.E., Andersen, G.R., and Torrey III, V.H. (1995). “Ranking models used for condition assessment of civil infrastructure systems.”Journal of Infrastructure Systems, ASCE, Vol. 2, No. 1, pp. 23–29.
Chung, S. and Lee, S. (2004). “Demand forecasting based on infrastructure asset management.”Journal of Civil Engineering, Korean Society of Civil Engineers, Vol 8, No. 2 pp. 165–172.
ENR (2001).Construction Cost Index History, Engineering News-Record, McGraw-Hill, Inc. New York, NY.
Hua, G.B. (1996). Residential construction demand forecasting using economic indicators: a comparative study of artificial neural networks and multiple regressions,Construction Management and Economics, E&FN. Spon, London, UK, Vol. 14, No. 1, pp. 25–34.
Khotanzad, A., Afkhami-Rohani, R., Lu, T., Abaye, A., Davis, M., and Maratukulam, D.J. (1997). “ANNSTLF—A neural-network-based electric load forecasting system.”IEEE Transactions on Neural Networks, Vol. 8, No. 4, pp. 835–845.
Koo, S. (2002). Sinking 50 billion won into the ground, Daily Chosun, Seoul, Korea.
Lee, S. and Chung, S. (2003). “Infrastructure Asset Management—Methodologies for Infrastructure Asset Management System in U.S”,Proceedings of 2003 KICEM, Korea, pp. 66–72.
Liong, S., Lim, W., and Paudyal, G.N. (2000). “River stage forecasting in Bangladesh: neural network approach.”Journal of Computing in Civil Engineering, ASCE, Vol. 14, No. 1, pp. 1–8.
Wirahadikusumah, R. (1999). “Optimization Modeling for Management of Large Combined Sewer Networks.” Ph. D. Dissertation, School of Civil Engineering, Purdue University, West Lafayette, IN.
About this article
Cite this article
Chung, SH., Hong, TH., Han, SW. et al. Life cycle cost analysis based optimal maintenance and rehabilitation for underground infrastructure management. KSCE J Civ Eng 10, 243–253 (2006). https://doi.org/10.1007/BF02830778
- sewer management
- demand forecasting
- life cycle cost analysis