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author:

Shao, Fei-Jiang (Shao, Fei-Jiang.) [1] (Scholars:姜绍飞) | Fu, Chun (Fu, Chun.) [2]

Indexed by:

CPCI-S

Abstract:

It is an interesting issue how to monitor and manage large-scale structures so that the alarm will be warned before various accidents occur, thus the disaster loss can be decreased to be the minimum. Numerous long-term on-line structural health monitoring systems have been developed and installed in china. However, another problem arises gradually how to effectively deal with huge and abundant measured information from a structural health monitoring system, thus to asses structural condition states. In view of this, intelligent information processing, which is a process of transforming the incomplete, imprecise, inconsistent and uncertain information into complete, precise, consistent and certain information, provides an approach and technique assurance to solve the above difficulties. This paper started from the structural health monitoring and its primary issues, and then presents a survey and overview of the intelligent information and its application to structural health monitoring and damage diagnosis, including modern signal processing, neural network, fuzzy theory, data/ information fusion, fractal theory, rough set and evolutionary computation. Further promising research in the future are discussed finally.

Keyword:

Damage identification Intelligent information processing State-of-the-art Structural health monitoring

Community:

  • [ 1 ] [Shao, Fei-Jiang]Fuzhou Univ, Coll Civil Engn, Fuzhou 350108, Peoples R China
  • [ 2 ] [Fu, Chun]Fuzhou Univ, Coll Civil Engn, Fuzhou 350108, Peoples R China

Reprint 's Address:

  • 姜绍飞

    [Shao, Fei-Jiang]Fuzhou Univ, Coll Civil Engn, Fuzhou 350108, Peoples R China

Email:

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Source :

PROCEEDINGS OF THE ELEVENTH INTERNATIONAL SYMPOSIUM ON STRUCTURAL ENGINEERING, VOL I AND II

Year: 2010

Page: 1116-1123

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 8

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