Fuzzy Inference Systems for Automatic Classification of Earthquake Damages

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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [11 references]  Display  Hide  Download

https://hal.inria.fr/hal-01060638
Contributor : Hal Ifip <>
Submitted on : Friday, November 17, 2017 - 3:46:27 PM
Last modification on : Friday, August 9, 2019 - 4:12:12 PM
Long-term archiving on : Sunday, February 18, 2018 - 4:09:59 PM

File

AlvanitopoulosAE10.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Petros-Fotios Alvanitopoulos, Ioannis Andreadis, Anaxagoras Elenas. Fuzzy Inference Systems for Automatic Classification of Earthquake Damages. 6th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI), Oct 2010, Larnaca, Cyprus. pp.368-375, ⟨10.1007/978-3-642-16239-8_48⟩. ⟨hal-01060638⟩

Share

Metrics

Record views

231

Files downloads

123