Using genetic algorithms for long-term planning of network of bridges

Document Type : Article

Authors

Department of Civil Engineering, Sharif University of Technology, Tehran, Iran

Abstract

Bridge maintenance activities are often budgeted, scheduled and conducted for networks of bridges with different ages, types and conditions, which can make bridge network maintenance management challenging. In this study, we propose an improved maintenance planning model based on genetic algorithm for a network of bridges to bring a long-term perspective to the lifespan of bridges. To test the applicability and efficiency of the model, it is applied to a network of 100 bridges in one of the south-western provinces of Iran. The results of the model implementation show considerable potential for improvement over the currently adopted model for bridge maintenance planning.

Keywords

Main Subjects


References:
1. IAM (the Institute of Asset Management) "Asset management - an anatomy", The Institute of Asset Management, 1.1 (2012).
2. Ranjbaran, J. "Unforeseen photos of Kan bridge collapse", KhabarOnline News, News number 4547, (Dec. 24, 2012), (in Persian), Available: http://www.khabaronline.ir/detail/259497/society/urban.
3. Tabnak "One casualty on bridge collapse", Tabnak News Agency, News number 123379, (Nov. 3, 2015), (in Persian), Available: http://ostanha. tabnak. ir/fa/news/123379.
4. ASCE (American Society of Civil Engineers) "Report card for America's infrastructure", Report American Society of Civil Engineers, Available: http://www.infrastructurereportcard.org/bridges (2015).
5. Gholami, M., Sam, A.R.B.M., and Yatim, J.M. "Assessment of bridge management system in Iran", Procedia Engineering, 54, pp. 573-583 (2015).
6. Hicks, R.G., Dun, K., and Moulthrop, J.S. "Framework for selecting effective preventive maintenance treatments for  flexible pavement", Transportation Research, Record 1597, pp. 1-10 (1997).
7. Al-Barqawi, H. and Zayed, T. "Assessment model of water main conditions", Pipeline Division Specialty Conference, Chicago, USA (2006).
8. Goldberg, D. "Genetic algorithms in search, optimization, and machine learning", Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA (1989).
9. Elhakeem, A. and Hegazy, T. "Building asset management with deficiency tracking and integrated life cycle optimisation", Journal of Structure and Infrastructure Engineering, 8(8), pp. 729-738 (2012).
10. Rashedi, R. "Large-scale asset renewal optimization: GAs+ segmentation versus advanced mathematical tools", M.Sc. Thesis, University of Waterloo, Ontario, Canada (2011).
11. Rashedi, R. and Hegazy, T. "Capital renewal optimization for large-scale infrastructure networks: genetic algorithms versus advanced mathematical tools", Structure and Infrastructure Engineering, 11(3), pp. 253-262 (2014).
12. Albattaineh, H. "Infrastructure intermediate-level modeling and optimization of budget allocation", Ph.D. Thesis, University Of Alberta, Alberta, Canada (2007).
13. Elbehairy, H. "Bridge management system with integrated life cycle cost optimization", Ph.D. Thesis, University of Waterloo, Ontario, Canada (2007).
14. NDR (Nebraska Department of Roads), "Developing deterioration models for Nebraska bridges", Report Project number: SPR-P1(11) M302 (2011).
15. Morcous, G., Rivard, H., and Hanna, A.M. "Modeling bridge deterioration using case based reasoning", Journal of Infrastructure Systems, 8(3), pp. 86-95 (2002).
16. Huang, R.Y., Mao, I.S., and Lee, H.K. "Exploring the deterioration factors of RC bridge decks: A rough set approach", Computer-Aided Civil and Infrastructure Engineering, 25(7), pp. 517-529 (2010).
17. SCI (Statistical Center of Iran) "The census result of 2016-2017", Statistical center of Iran, Available: https://www.amar.org.ir/Portals/0/census/ 1395/results/tables/jamiat/kolli/1-koli-jamiat.xls (2017).
18. USCB (U.S. Census Bureau), Annual Estimates of the Resident Population for the United States, Regions, States, and Puerto Rico: April 1, 2010 to July 1, 2015. U.S. Census Bureau, Available: http://www.census.gov/popest/data/state/totals/ 2015/tables/NST-EST2015-01.csv (2015).
19. IPRCI (Islamic Parliament Research Center of the Islamic Republic of Iran), "Vehicle per capital of Iran", Khordadnews, newcode: 44790 (Mar. 4, 2014), Available: http://khordadnews.ir/fa/news/44790.
20. OHPI (Office of Highway Policy Information) "State motor-vehicle registrations", Office of Highway Policy Information, U.S. Department of Transportation Federal Highway Administration, (January 2017), Available: https://www.fhwa.dot.gov/policyinformation/ statistics/2015/mv1.cfm.
21. Razali, N.M. and Geraghty, J. "Genetic algorithm performance with different selection strategies in solving TSP", The World Congress on Engineering, 2, pp. 1134-1139 (2011).
22. IRIMO (Islamic Republic of Iran Meteorological Organization), Access to historical climate data-Yasuj, Available: http://www.irimo.ir/, retrieved March 14 (2016).
23. UCD (US Climate Data), Report Climate Lincoln -Nebraska, Available: http://www.usclimatedata .com/, retrieved March 14 2016.
Volume 26, Issue 5
Transactions on Civil Engineering (A)
September and October 2019
Pages 2653-2664
  • Receive Date: 26 November 2016
  • Revise Date: 22 September 2017
  • Accept Date: 02 December 2017