An inversed greedy method for information-based optimal sensor placement on bridges

Bei Yang Zhang, Yi Qing Ni

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

An information-based optimal sensor placement (OSP) strategy is explored in the context of bridge health monitoring. An inversed greedy algorithm is proposed for optimizing the sensor placement since the conventional greedy algorithm can only achieve a near-optimal solution with large uncertainty. This study intends to reconstruct complete modal shape configuration based on the deployed sensors by employing Gaussian process regression (GPR) method, where the modal components from the deployed sensors are utilized for searching the optimal positions on the bridge. First of all, the GPR method is exploited to establish a probability model of the unsensed positions based on the data from the deployed sensors. Then, to maximize the information that the probability model contains, the mutual information is employed as the objective function and interpreted by the proposed inversed greedy algorithm. The performance of the proposed method is verified through a numerical case study, where the conventional greedy algorithm and the genetic algorithm are also implemented for the purpose of comparison with the proposed algorithm. The results show that the inversed greedy algorithm outperforms the greedy algorithm and the genetic algorithm since it can provide a solution which is closer to the optimum in terms of the prediction error.

Original languageEnglish
Title of host publicationStructural Health Monitoring 2019
Subtitle of host publicationEnabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT) - Proceedings of the 12th International Workshop on Structural Health Monitoring
EditorsFu-Kuo Chang, Alfredo Guemes, Fotis Kopsaftopoulos
PublisherDEStech Publications Inc.
Pages2825-2832
Number of pages8
ISBN (Electronic)9781605956015
Publication statusPublished - 1 Jan 2019
Event12th International Workshop on Structural Health Monitoring: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT), IWSHM 2019 - Stanford, United States
Duration: 10 Sep 201912 Sep 2019

Publication series

NameStructural Health Monitoring 2019: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT) - Proceedings of the 12th International Workshop on Structural Health Monitoring
Volume2

Conference

Conference12th International Workshop on Structural Health Monitoring: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT), IWSHM 2019
Country/TerritoryUnited States
CityStanford
Period10/09/1912/09/19

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Information Management

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