Topological surrogates for computationally efficient seismic robustness optimization of water pipe networks. (30th May 2020)
- Record Type:
- Journal Article
- Title:
- Topological surrogates for computationally efficient seismic robustness optimization of water pipe networks. (30th May 2020)
- Main Title:
- Topological surrogates for computationally efficient seismic robustness optimization of water pipe networks
- Authors:
- Pudasaini, Binaya
Shahandashti, Mohsen - Abstract:
- Abstract: The criticality of seismic robustness of the water pipe networks cannot be overstated. Current methodologies for optimizing seismic robustness of city‐scale water pipe networks are scarce. A very few studies that can be found are also prone to long optimization runtimes due to the requirement of repeated hydraulic analysis. Hence, there is a critical need for the identification of computationally efficient surrogate optimization methods for maximizing seismic robustness of water pipe networks. To address this need, this research was conducted to identify, for the first time, computationally efficient topological surrogates for hydraulic simulation‐based optimization. The computational efficiency of surrogate optimization was measured in terms of solution quality (i.e., post‐earthquake serviceability) and computational runtime. Ten different topological connectivity metrics were evaluated out of which five were considered computationally infeasible due to their prohibitive optimization runtime. Five remaining metrics were then used to formulate five surrogate objective functions for seismic robustness of water pipe networks. Each of these functions was optimized using a simulated annealing‐based algorithm. Application of the proposed approach to city‐level benchmark networks helped to identify two metrics out of ten that offered a substantial reduction in optimization runtime with a minimal loss in solution quality. These findings will be highly valuable to waterAbstract: The criticality of seismic robustness of the water pipe networks cannot be overstated. Current methodologies for optimizing seismic robustness of city‐scale water pipe networks are scarce. A very few studies that can be found are also prone to long optimization runtimes due to the requirement of repeated hydraulic analysis. Hence, there is a critical need for the identification of computationally efficient surrogate optimization methods for maximizing seismic robustness of water pipe networks. To address this need, this research was conducted to identify, for the first time, computationally efficient topological surrogates for hydraulic simulation‐based optimization. The computational efficiency of surrogate optimization was measured in terms of solution quality (i.e., post‐earthquake serviceability) and computational runtime. Ten different topological connectivity metrics were evaluated out of which five were considered computationally infeasible due to their prohibitive optimization runtime. Five remaining metrics were then used to formulate five surrogate objective functions for seismic robustness of water pipe networks. Each of these functions was optimized using a simulated annealing‐based algorithm. Application of the proposed approach to city‐level benchmark networks helped to identify two metrics out of ten that offered a substantial reduction in optimization runtime with a minimal loss in solution quality. These findings will be highly valuable to water distribution network managers for identifying economical rehabilitation policies for enhancing the seismic robustness at a city‐scale within a reasonable amount of time. … (more)
- Is Part Of:
- Computer-aided civil and infrastructure engineering. Volume 35:Number 10(2020:Oct.)
- Journal:
- Computer-aided civil and infrastructure engineering
- Issue:
- Volume 35:Number 10(2020:Oct.)
- Issue Display:
- Volume 35, Issue 10 (2020)
- Year:
- 2020
- Volume:
- 35
- Issue:
- 10
- Issue Sort Value:
- 2020-0035-0010-0000
- Page Start:
- 1101
- Page End:
- 1114
- Publication Date:
- 2020-05-30
- Subjects:
- Civil engineering -- Data processing -- Periodicals
Computer-aided engineering -- Periodicals
624.0285 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-8667 ↗
http://www.ingenta.com/journals/browse/bpl/mice ↗
http://www.intute.ac.uk/sciences/cgi-bin/fullrecord.pl?handle=p.curran.1032797039 ↗
http://www3.interscience.wiley.com/journal/118514357/home ↗
http://onlinelibrary.wiley.com/ ↗
http://firstsearch.oclc.org ↗ - DOI:
- 10.1111/mice.12566 ↗
- Languages:
- English
- ISSNs:
- 1093-9687
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3393.519350
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 14308.xml