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SOFT COMPUTING METHODS FOR CIVIL AND STRUCTURAL ENGINEERING
Edited by: Y. Tsompanakis and B.H.V. Topping
Structural Health Monitoring using Soft Computing
H. Furuta1, Y. Nomura2 and K. Nakatsu1
1Department of Informatics, Kansai University, Takatsuki, Osaka, Japan
H. Furuta, Y. Nomura, K. Nakatsu, "Structural Health Monitoring using Soft Computing", in Y. Tsompanakis and B.H.V. Topping, (Editor), "Soft Computing Methods for Civil and Structural Engineering", Saxe-Coburg Publications, Stirlingshire, UK, Chapter 6, pp 117-147, 2011. doi:10.4203/csets.29.6
Keywords: AdaBoost, chaos, damage detection, fuzzy ensemble, health monitoring.
Recently, great attention has been paid to soft computing technology, because of its applicability and ease of computation in engineering problems. This paper aims to introduce the soft computing technology into structural health monitoring. Structural health monitoring (SHM) has been used for evaluating structural integrity and detecting structural damage. The SHM, in general, involves the observation of a structure over time using dynamic response measurements periodically sampled from an array of sensors, the extraction of damage-sensitive features from these measurements, and the statistical analysis of these features to determine the current state of structural soundness. The SHM can monitor the change of structural soundness due to aging and deterioration in the long term. On the other hand, after such extreme events as earthquakes or typhoons, SHM can provide us with reliable information regarding the integrity of the structure through rapid condition screening obtained from the measured data.
In this study, a new damage evaluation system is described, which can evaluate the damage condition of existing structures by using the visual information given by digital photos. In particular, the damage to reinforced concrete (RC) bridge decks is evaluated with the aid of digital photos and pattern recognition. Apparently, it is necessary to evaluate the structural damage of existing bridges in a quantitative manner. However, it is difficult to avoid the subjectivity of inspectors when visual data are used for the evaluation of damage or deterioration. Here, an attempt is made to develop a damage assessment system by using a topographic attentive mapping (TAM) network. Next, the AdaBoost technique and fuzzy ensemble system are used to improve the evaluation accuracy. Finally, an attempt is made to develop a baseline-less structural health monitoring method that can detect damage locations and degrees without baseline data. Based upon chaos excitation, it is possible to perform the damage detection by concentrating on the chaotic characteristics of structural response.
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