Abstract
This paper presents efficient models in the area of damage potential classification of seismic signals. After an earthquake, one of the most important actions that authorities must take is to inspect structures and estimate the degree of damages. The interest is obvious for several reasons such as public safety, economical recourses management and infrastructure. This approach provides a comparative study between the Mamdani-type and Sugeno-type fuzzy inference systems (FIS). The fuzzy models use a set of artificial accelerograms in order to classify structural damages in a specific structure. Previous studies propose a set of twenty well-known seismic parameters which are essential for description of the seismic excitation. The proposed fuzzy systems use an input vector of twenty seismic parameters instead of the earthquake accelerogram and produce classification rates up to 90%. Experimental results indicate that these systems are able to classify the structural damages in structures accurately. Both of them produce the same level of correct classification rates but the Mamdani-type has a slight superiority.
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Elenas, A., Meskouris, K.: Correlation Study Between Seismic Acceleration Parameters and Damage Indices of Structures. J. Engineering Structures 23, 698–704 (2001)
Rodriguez-Gomez, S., Cakmak, A.S.: Evaluation of Seismic Damage Indices for Reinforced Concrete Structures. Technical Report NCEER-90-0022, State University of New York, Buffalo (1990)
Park, Y.J., Ang, A.H.S.: Mechanistic Seismic Damage Model for Reinforced Concrete. J. Structural Engineering 111, 722–739 (1985)
Andreadis, I., Tsiftzis, Y., Elenas, A.: Intelligent Seismic Acceleration Signal Processing for Damage Classification in Buildings. J. IEEE Transactions on Instrumentation and Measurement 56, 1555–1564 (2007)
Alvanitopoulos, P., Andreadis, I., Elenas, A.: A New Algorithm for the Classification of Earthquake Damages in Structures. In: 5th IASTED International Conference on Signal Processing, Pattern Recognition and Applications, Innsbruck, Austria, pp. 151–156 (February 2008)
Alvanitopoulos, P., Andreadis, I., Elenas, A.: A Genetic Algorithm for the Classification of Earthquake Damages in Buildings. In: 5th IFIP Conference on Artificial Intelligence Applications & Innovations, Thessaloniki, Greece, pp. 341–346 (April 2009)
Angelov, P., Zhou, X.: Evolving Fuzzy-Rule-Based Classifiers from Data Streams. J. IEEE Transactions on Fuzzy Systems 16, 1462–1475 (2008)
Angelov, P., Lughofer, E., Zhou, X.: Evolving Fuzzy Classifiers Using Different Model Architectures. J. Fuzzy Sets and Systems 159, 3160–3182 (2008)
Mamdani, E.H., Assilian, S.: An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller. J. Man-Machine Studies 7, 1–13 (1975)
Kulkarni, A.D.: Computer Vision and Fuzzy-Neural Systems. Prentice Hall PTR, Upper Saddle River (2001)
Sugeno, M.: Industrial Applications of Fuzzy Control. Elsevier Science, New York (1985)
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Alvanitopoulos, PF., Andreadis, I., Elenas, A. (2010). Fuzzy Inference Systems for Automatic Classification of Earthquake Damages. In: Papadopoulos, H., Andreou, A.S., Bramer, M. (eds) Artificial Intelligence Applications and Innovations. AIAI 2010. IFIP Advances in Information and Communication Technology, vol 339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16239-8_48
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DOI: https://doi.org/10.1007/978-3-642-16239-8_48
Publisher Name: Springer, Berlin, Heidelberg
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