Document Detail


A multi-objective optimisation model for sewer rehabilitation considering critical risk of failure.
MedLine Citation:
PMID:  23032772     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
A unique methodology for the optimal specification of sewer rehabilitation investment is presented in this paper. By accounting for the critical risk of asset failure, this methodology builds on previously successful work which explored the application of multi-objective optimisation tools to assist engineers with the specification of optimal rehabilitation strategies. The conventional sewerage rehabilitation specification process relies on the expertise of professional engineers to manually evaluate CCTV inspection information when determining the nature and extent of the rehabilitation solution. This process is not only tedious and subjective but it has no quantifiable means of identifying optimal solutions or possible combinations of optimal solutions in the delivery of catchment wide rehabilitation programmes. Therefore, the purely manual process of sewer rehabilitation design leaves a number of unanswered questions, such as: (1) Does the solution offer the greatest structural benefit to the network? (2) Is the solution the most cost-effective solution available? (3) Does the solution most greatly reduce the risk of critical asset failure? The application of a multi-objective genetic algorithm optimisation model, coupled with an enhanced critical risk methodology, has successfully answered these questions when applied to a case study data set provided by South West Water (UK).
Authors:
Ben Ward; Dragan A Savić
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Water science and technology : a journal of the International Association on Water Pollution Research     Volume:  66     ISSN:  0273-1223     ISO Abbreviation:  Water Sci. Technol.     Publication Date:  2012  
Date Detail:
Created Date:  2012-10-03     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9879497     Medline TA:  Water Sci Technol     Country:  England    
Other Details:
Languages:  eng     Pagination:  2410-7     Citation Subset:  IM    
Affiliation:
AECOM, Belverdere House, Pynes Hill, Exeter EX2 5WS, UK E-mail: ben.ward@aecom.com; Centre for Water Systems, University of Exeter, North Park Road, Exeter EX4 4QF, UK.
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