Structural health monitoring approach based on dynamics of prediction-error for ARX and neural network models

Akira Mita, A. Take

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

Abstract

An acceleration-based structural health monitoring approach for building structures under earthquake using ARX model and neural network (NN) model is proposed in this paper. The ARX and NN models were constructed using the healthy structure with moderate input. Then the prediction-error for other inputs is obtained to estimate the damage existence and its extent. In previous studies the prediction error is usually used to measure the damage level by comparing the amplitude of the error. In this study, the analysis of dynamics of prediction-error is proposed to improve the accuracy of the damage identification. The approach is further improved by using the acceleration at later time steps. The delay of time was considered as a tunable band corresponding to different structures. To facilitate using possibly less sensors, the acceleration streams at the same location but at different time steps were utilized. The prediction accuracy could be raised by the increment of number of acceleration streams at different time steps to an appropriate value. In our proposed evaluation approach, damage occurrence alarm could be obtained practically and economically only using readily available acceleration time histories. The method was applied to a full-scale two-storey wooden building tested at the E-Defense. At the final step, the shake table was excited to reproduce the ground acceleration of large earthquake obtained at Kobe. The building was destructed finally by this earthquake input In the course of the shake table tests, many levels of inputs were applied to the building to see its degradation by the shake. The method proposed here was applied to see its effectiveness and applicability. We will show that the dynamic characteristics of the prediction error have many important information on the degree of damage and the modes of the damage that would not be obtained just by looking at the amplitude of the error.

Original languageEnglish
Title of host publicationStructural Health Monitoring 2009: From System Integration to Autonomous Systems - Proceedings of the 7th International Workshop on Structural Health Monitoring, IWSHM 2009
PublisherDEStech Publications
Pages1487-1494
Number of pages8
Volume2
ISBN (Print)9781605950075
Publication statusPublished - 2009
Event7th International Workshop on Structural Health Monitoring: From System Integration to Autonomous Systems, IWSHM 2009 - Stanford, United States
Duration: 2009 Sep 92009 Sep 11

Other

Other7th International Workshop on Structural Health Monitoring: From System Integration to Autonomous Systems, IWSHM 2009
CountryUnited States
CityStanford
Period09/9/909/9/11

ASJC Scopus subject areas

  • Health Information Management
  • Computer Science Applications

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  • Cite this

    Mita, A., & Take, A. (2009). Structural health monitoring approach based on dynamics of prediction-error for ARX and neural network models. In Structural Health Monitoring 2009: From System Integration to Autonomous Systems - Proceedings of the 7th International Workshop on Structural Health Monitoring, IWSHM 2009 (Vol. 2, pp. 1487-1494). DEStech Publications.