A framework for evaluating the impact of structural health monitoring on bridge management

M. Pozzi*, D. Zonta, W. Wang, G. Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

33 Citations (Scopus)

Abstract

Although structural monitoring has been recognized as a powerful information tool, bridge managers often make decisions based on their experience or on common sense, somehow regardless of the action suggested by instrumental damage detection algorithms. In fact, managers weight differently the outcomes of the detection based on his/her príor perception of the state of the structure and decide keeping in mind the possible effects of the action he/she can undertake. In this paper we propose a rational framework to include the impact of the mentioned issues on decision making. The methodology is applied to the Bill Emerson Memorial Bridge, a new 1206 meter long cable-stayed structure across the Mississippi River, instrumented with an 84-channel seismic instrumentation system. Using a finite element model, we estimate the probability distributions of the response of the bridge for a possible damage scenarío involving formation of plastic hinges at the intersection of tower columns and cap beams. The example shows how it is possible to estimate the economic benefit of a monitoring system for any event which requires a damage assessment. 

Original languageEnglish
Title of host publicationBridge Maintenance, Safety, Management and Life-Cycle Optimization - Proceedings of the 5th International Conference on Bridge Maintenance, Safety and Management
EditorsDan Frangopol, Richard Sause, Chad Kusko
Pages161-162
Number of pages2
Publication statusPublished - 7 Jul 2010
Event5th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2010 - Philadelphia, PA, United States
Duration: 11 Jul 201015 Jul 2010

Conference

Conference5th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2010
Country/TerritoryUnited States
CityPhiladelphia, PA
Period11/07/1015/07/10

Keywords

  • artificial neural network
  • bridge management
  • condition assessment
  • decision-making

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