Paper
11 April 2006 Damage detection of wooden buildings using chaos analysis and system identification
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Abstract
Structural Health Monitoring (SHM), the technology to evaluate the health of the structure (such as earthquake resistance), is getting attention recently. The techniques for evaluating earthquake-proof performance in the building are key elements. However, existing techniques are not so practical as they require professional knowledge on structural engineering. The chaos analysis method, recently proposed by Prof. Okada is an attractive method to meet the need. The method evaluates earthquake-proof performance, using chaos analysis. It is clear that there is correlation between chaos theory and physical parameters. This paper proposes a modified version of chaos analysis to make the method stable and reliable. Findings in the course of this study are as follows. (1) The chaos degree rises when attenuation grows. (2) The chaos degree rises by widening the band of the frequency. (3) The ground motion with large power decides the chaos degree. (4) The chaos degree is not unique for the same structure depending on the inputs. These are due to the results of simulation and full-scale experiments. Using these findings, the computational method of the chaos degree without depending on the characteristics of inputs is established. It is a method using the system identification to estimate the parameter model of building from the result of the response due to any inputs. The influence of the input characteristic can be excluded by this method.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
K. Hijikata and A. Mita "Damage detection of wooden buildings using chaos analysis and system identification", Proc. SPIE 6174, Smart Structures and Materials 2006: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, 61741O (11 April 2006); https://doi.org/10.1117/12.658954
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KEYWORDS
Chaos

Buildings

System identification

Earthquakes

Structural health monitoring

Autoregressive models

Damage detection

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