A new model for bridge management: Part B: decision support system for remediation planning. Issue 1 (1st January 2016)

• Record Type: Journal Article
• Authors: Rashidi, Maria; Samali, Bijan; Sharafi, Pezhman
• Is Part Of: Australian journal of civil engineering. Volume 14:Issue 1(2016)
• Page Start: 46
• Page End: 53
• Publication Date: 2016-01-01
• Subjects: Remediation -- decision support system (DSS) -- simplified analytical hierarchy process (S-AHP); Civil engineering -- Periodicals; Civil engineering -- Australia -- Periodicals; Periodicals; 624.05
• DOI: 10.1080/14488353.2015.1092642
• Languages: English
• ISSNs: 1448-8353

Abstract

Abstract: Bridges are fundamental elements in any road network and characterize a major capital asset of community resources. Because of their strategic location over obstacles, any failure may limit and restrict road traffic and cause inconvenience and economic loss. It is therefore important to manage bridge assets, to ensure that all the bridges are maintained in a safe condition, with the most cost-efficient use of resources. Most of the existing bridge management systems are to some extent subjective and based on cost optimization which does not address the indirect impact of the remedial actions. The subjective nature of decision-making in bridge remediation could be replaced by the application of a multi-criteria decision support system that supports decision-makers through balanced consideration of multiple criteria. The main goal of this paper is to present a requirements-driven decision support methodology for remediation of concrete bridges with the aim of maintaining bridge assets within acceptable limits of safety, serviceability and sustainability. The simplified analytical hierarchy process is used as a multi-criteria decision-making technique. A method for selection of the best remediation plan in terms of fund allocation for top-ranked bridges of the network is also proposed using the outputs of the previous procedures considering the budget as the main constraint.