Investigation of a static and a dynamic neighbourhood methodology to develop work programs for multiple close municipal infrastructure networks

References

• Ahmadi, M., Cherqui, F., de Massiac, J.-C., Werey, C., Lagoutte, S. A., & Le Gauffre, P. (2014). Condition grading for dysfunction indicators in sewer asset management. Structure and Infrastructure Engineering, 10, 346–358.
   Web of Science ®Google Scholar
• Allweyer, T. (2010). BPMN 2.0: Introduction to the standard for business process modeling. Norderstedt: BoD-Books on Demand.
   Google Scholar
• Arthur, S., Crow, H., Pedezert, L., & Karikas, N. (2009). The holistic prioritisation of proactive sewer maintenance. Water Science and Technology, 59, 1385–1396.
   PubMed Web of Science ®Google Scholar
• Birant, D., & Kut, A. (2007). St-dbscan: An algorithm for clustering spatial-temporal data. Data & Knowledge Engineering, 60, 208–221.
   Web of Science ®Google Scholar
• Bobylev, N. (2009). Mainstreaming sustainable development into a city’s master plan: A case of urban underground space use. Land Use Policy, 26, 1128–1137.
   Web of Science ®Google Scholar
• Cardoso, M., Coelho, S., Matos, R., & Alegre, H. (2004). Performance assessment of water supply and wastewater systems. Urban Water Journal, 1, 55–67.
   Google Scholar
• Coelho, S. T. (1997). Performance in water distribution: A systems approach. New York, NY: Research Studies Press.
   Google Scholar
• Dehghanian, P., Fotuhi-Firuzabad, M., Aminifar, F., & Billinton, R. (2013). A comprehensive scheme for reliability centered maintenance in power distribution systems -- Part I: Methodology. IEEE Transactions on Power Delivery, 28, 761–770.
   Web of Science ®Google Scholar
• Dueñas-Osorio, L., Craig, J. I., Goodno, B. J., & Bostrom, A. (2007). Interdependent response of networked systems. Journal of Infrastructure Systems, 13, 185–194.
   Web of Science ®Google Scholar
• Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of 2nd international conference on knowledge discovery and data mining (KDD-96). Portland, Oregon, USA.
   Google Scholar
• Fenner, R. A. (2000). Approaches to sewer maintenance: A review. Urban Water, 2, 343–356.
   Google Scholar
• Fenner, R. A., Sweeting, L., & Marriott, M. (2000). A new approach for directing proactive sewer maintenance. Proceedings of the ICE -- Water and Maritime Engineering, 142, 67–77.
   Google Scholar
• Frangopol, D. M., Kong, J. S., & Gharaibeh, E. S. (2001). Reliability-based life-cycle management of highway bridges. Journal of Computing in Civil Engineering, 15, 27–34.
   Web of Science ®Google Scholar
• Fung, G. (2001). A comprehensive overview of basic clustering algorithms. Google Code,
   Google Scholar
• Garland, A., Flintsch, G., Garvin, M. J., & Sotelino, E. (2009). Characterization of network topology to support infrastructure asset management. Public Works Management & Policy, 14, 81–101.
   Google Scholar
• Hajdin, R., & Adey, B. T. (2006). Optimal worksites on highway networks subject to constraints. In International forum on rngineering decision making. Lake Louise, Canada.
   Google Scholar
• Hajdin, R., & Lindenmann, H.-P. (2007). Algorithm for the planning of optimum highway work zones. Journal of Infrastructure Systems, 13, 202–214.
   Web of Science ®Google Scholar
• Halfawy, M. R., Dridi, L., & Baker, S. (2008). Integrated decision support system for optimal renewal planning of sewer networks. Journal of Computing in Civil Engineering, 22, 360–372.
   Web of Science ®Google Scholar
• Jeong, H. S., Abraham, D.M., & Baik, H.S. (2005). An ordered probit model approach for developing Markov Chain based deterioration model for wastewater infrastructure systems (pp. 649–661). Houston, TX: American Society of Civil Engineers.
   Google Scholar
• Jiang, B., & Claramunt, C. (2004). Topological analysis of urban street networks. Environment and Planning B: Planning and Design, 31, 151–162.
   Web of Science ®Google Scholar
• Johnson, D. B. (1977). Efficient algorithms for shortest paths in sparse networks. Journal of ACM, 24(1), 1–13.
   Web of Science ®Google Scholar
• Kirstein, S., Müller, K., Walecki-Mingers, M., & Deserno, T. M. (2012). Robust adaptive flow line detection in sewer pipes. Automation in Construction, 21, 24–31.
   Web of Science ®Google Scholar
• Kleiner, Y., Adams, B. J., & Rogers, J. S. (1998). Selection and scheduling of rehabilitation alternatives for water distribution systems. Water Resources Research, 34, 2053–2061.
   Web of Science ®Google Scholar
• Le Gauffre, P., Joannis, C., Vasconcelos, E., Breysse, D., Gibello, C. & Desmulliez, J. (2007). Performance indicators and multicriteria decision support for sewer asset management. Journal of Infrastructure Systems, 13, 105–114.
   Web of Science ®Google Scholar
• Lee, J., & Madanat, S. (2015). A joint bottom-up solution methodology for system-level pavement rehabilitation and reconstruction. Transportation Research Part B: Methodological, 78, 106–122.
   Web of Science ®Google Scholar
• Lejeune Dirichlet, G. (1850). Über die Reduction der positiven quadratischen Formen mit drei unbestimmten ganzen Zahlen [About the reduction of positive quadratic forms with three unknown integers]. Journal für die reine und angewandte Mathematik, 40, 209–227.
   Google Scholar
• Lethanh, N., Adey, B. T., & Sigrist, M. (2014). A mixed-integer linear model for determining optimal work zones on a road network. In Proceeding of the international conference on engineering and applied sciences optimization, KOS Island.
   Google Scholar
• Liu, M., & Frangopol, D. M. (2004). Optimal bridge maintenance planning based on probabilistic performance prediction. Engineering Structures, 26, 991–1002.
   Web of Science ®Google Scholar
• Liu, P., Zhou, D., & Wu, N. (2007). Vdbscan: Varied density based spatial clustering of applications with noise. In 2007 International conference on service systems and service management (pp. 1–4). Chengdu, China.
   Google Scholar
• Louit, D., Pascual, R., & Banjevic, D. (2009). Optimal interval for major maintenance actions in electricity distribution networks. International Journal of Electrical Power & Energy Systems, 31, 396–401.
   Web of Science ®Google Scholar
• Mathew, B. S., & Isaac, K. P. (2013). Optimisation of maintenance strategy for rural road network using genetic algorithm. International Journal of Pavement Engineering, 15, 352–360.
   Web of Science ®Google Scholar
• Miyamoto, A., Kawamura, K., & Nakamura, H. (2000). Bridge management system and maintenance optimization for existing bridges. Computer-Aided Civil and Infrastructure Engineering, 15, 45–55.
   Web of Science ®Google Scholar
• Morcous, G., & Lounis, Z. (2005). Maintenance optimization of infrastructure networks using genetic algorithms. Automation in Construction, 14, 129–142.
   Web of Science ®Google Scholar
• Pandey, M. D. (1998). Probabilistic models for condition assessment of oil and gas pipelines. NDT & E International, 31, 349–358.
   Web of Science ®Google Scholar
• Ram, A., Jalal, S., Jalal, A. S., & Kumar, M. (2010). A density based algorithm for discovering density varied clusters in large spatial databases. International Journal of Computer Applications, 3(6), 1–4.
   Google Scholar
• Rinaldi, S. M., Peerenboom, J. P., & Kelly, T. K. (2001). Identifying, understanding, and analyzing critical infrastructure interdependencies. Control Systems, IEEE, 21, 11–25.
   Web of Science ®Google Scholar
• Saaty, T. L. (1996). Decision making with dependence and feedback: The analytic network process (Vol. 17). Pittsburgh, PA: RWS Publications.
   Google Scholar
• Sathaye, N., & Madanat, S. (2012). A bottom-up optimal pavement resurfacing solution approach for large-scale networks. Transportation Research Part B: Methodological, 46, 520–528.
   Web of Science ®Google Scholar
• Shamir, U., & Howard, C. D. D. (1979). An analytic approach to scheduling pipe replacement. Journal -- American Water Works Association, 71, 248–258.
   Web of Science ®Google Scholar
• Stillman, R. H. (2003). Power line maintenance with minimal repair and replacement. In Reliability and Maintainability Symposium, 2003. Annual (pp. 541–545). Tampa, Florida, USA.
   Google Scholar
• Tscheikner-Gratl, F., Sitzenfrei, R., Rauch, W., & Kleidorfer, M. (2016). Integrated rehabilitation planning of urban infrastructure systems using a street section priority model. Urban Water Journal, 13, 28–40.
   Web of Science ®Google Scholar
• Walski, T. M., & Pelliccia, A. (1982). Economic analysis of water main breaks. Journal -- American Water Works Association, 74, 140–147.
   Web of Science ®Google Scholar
• Wang, Y., Moselhi, O., & Zayed, T. (2009). Study of the suitability of existing deterioration models for water mains. Journal of Performance of Constructed Facilities, 23, 47–54.
   Web of Science ®Google Scholar
• Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques. San Francisco, CA: Morgan Kaufmann.
   Google Scholar
• Xu, X., Ester, M., Kriegel, H. P., & Sander, J. (1998). A distribution-based clustering algorithm for mining in large spatial databases. In 14th International conference on data engineering, 1998. Proceedings (pp. 324–331). Orlando, Florida, USA.
   Google Scholar
• Zayed, T., & Mohamed, E. (2013). Budget allocation and rehabilitation plans for water systems using simulation approach. Tunnelling and Underground Space Technology, 36, 34–45.
   Web of Science ®Google Scholar