Chapter
Apr 26, 2012
Realtime Damage Detection in Buildings Using Filter Based Radial Basis Function Network Mapping
Authors: Michael Contreras [email protected], Satish Nagarajaiah [email protected], and Sriram Narasimhan [email protected]Author Affiliations
Publication: Structures Congress 2009: Don't Mess with Structural Engineers: Expanding Our Role
Abstract
Radial Basis Function networks are extremely fast and require relatively small training data sets compared to other neural network methods such as back propagation. They are also less susceptible to problems with non-stationary inputs because of the behavior of the radial basis function hidden units. This makes RBF methods very attractive for realtime structural health monitoring (SHM) and damage detection. In this study, the aforementioned merits of the the RBF network will be exploited to perform realtime damage detection in buildings which are subjected to both sinusoidal and earthquake gound motion.
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Published In
Structures Congress 2009: Don't Mess with Structural Engineers: Expanding Our Role
Pages: 1 - 10
Copyright
© 2009 American Society of Civil Engineers.
History
Published online: Apr 26, 2012
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ASCE Technical Topics:
- Artificial intelligence and machine learning
- Building design
- Buildings
- Computer programming
- Computing in civil engineering
- Design (by type)
- Detection methods
- Education
- Engineering fundamentals
- Environmental engineering
- Filters
- Filtration
- Geomatics
- Information management
- Mapping
- Methodology (by type)
- Neural networks
- Practice and Profession
- Structural engineering
- Structures (by type)
- Surveying methods
- Training
- Water treatment
Authors
Affiliations
Depts. of Civil & Env. Engr., Rice University, Houston, TX 77005. E-mail: [email protected]
Depts. of Civil & Env. Engr., & Mech. Engr. & Mat. Sci., Rice Univ., Houston, TX 77005. E-mail: [email protected]
Dept. of Civil & Env. Engr., Univ. of Waterloo, ON, N2L 3G1 Canada. E-mail: [email protected]
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