Welcome to the InfoSci Platform
Reference Hub3
Neuro-Fuzzy Assessment of Building Damage and Safety After an Earthquake

Neuro-Fuzzy Assessment of Building Damage and Safety After an Earthquake

Martha Carreño, Omar Cardona, Alex Barbat
Copyright: © 2007 |Pages: 35
ISBN13: 9781599040998|ISBN10: 1599040999|ISBN13 Softcover: 9781599041001|EISBN13: 9781599041018
DOI: 10.4018/978-1-59904-099-8.ch007
Cite Chapter Cite Chapter

MLA

Carreño, Martha, et al. "Neuro-Fuzzy Assessment of Building Damage and Safety After an Earthquake." Intelligent Computational Paradigms in Earthquake Engineering, edited by Nikos Lagaros and Yiannis Tsompanakis, IGI Global, 2007, pp. 123-157. https://doi.org/10.4018/978-1-59904-099-8.ch007

APA

Carreño, M., Cardona, O., & Barbat, A. (2007). Neuro-Fuzzy Assessment of Building Damage and Safety After an Earthquake. In N. Lagaros & Y. Tsompanakis (Eds.), Intelligent Computational Paradigms in Earthquake Engineering (pp. 123-157). IGI Global. https://doi.org/10.4018/978-1-59904-099-8.ch007

Chicago

Carreño, Martha, Omar Cardona, and Alex Barbat. "Neuro-Fuzzy Assessment of Building Damage and Safety After an Earthquake." In Intelligent Computational Paradigms in Earthquake Engineering, edited by Nikos Lagaros and Yiannis Tsompanakis, 123-157. Hershey, PA: IGI Global, 2007. https://doi.org/10.4018/978-1-59904-099-8.ch007

Export Reference

Mendeley
Favorite

Abstract

This chapter describes the algorithmic basis of a computational intelligence technique, based on a neuro-fuzzy system, developed with the objective of assisting nonexpert professionals of building construction to evaluate the damage and safety of buildings after strong earthquakes, facilitating decision-making during the emergency response phase on their habitability and reparability. A hybrid neuro-fuzzy system is proposed, based on a special three-layer feedforward artificial neural network and fuzzy rule bases. The inputs to the system are fuzzy sets, taking into account that the damage levels of the structural components are linguistic variables, defined by means of qualifications such as slight, moderate or severe, which are very appropriate to handle subjective and incomplete information. The chapter is a contribution to the understanding of how soft computing applications, such as artificial neural networks and fuzzy sets, can be used to complex and urgent processes of engineering decision-making, like the building occupancy after a seismic disaster.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.