Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Utilization of artificial neural networks in building damage prediction
Download
082593.pdf
Date
1999
Author
Erkuş, Barış
Metadata
Show full item record
Item Usage Stats
153
views
0
downloads
Cite This
URI
https://hdl.handle.net/11511/2315
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
Utilization of neural networks for simulation of vehicle induced flow in tunnel systems
Koç, Gencer; Albayrak, Kahraman; Sert, Cüneyt; Department of Mechanical Engineering (2012)
Air velocities induced by underground vehicles in complex metro systems are obtained using artificial neural networks. Complex tunnel shaft-systems with any number of tunnels and shafts and with most of the practically possible geometries encountered in underground structures can be simulated with the proposed method. A single neural network, of type feed-forward back propagation, with a single hidden layer is trained for modelling a single tunnel segment. Train and tunnel parameters that have influence on ...
Utilization of neural networks for simulating vehicle induced air velocity in underground tunnels
Koç, G.; Albayrak, Kahraman; Sert, Cüneyt (2012-12-01)
Air velocities induced by underground vehicles in metro tunnels equipped with ventilation shafts are obtained using artificial neural networks. Complex tunnel shaft-systems with any number of tunnels and shafts and with most of the practically possible geometries encountered in underground structures can be simulated with the proposed method. A single neural network, of type feed-forward back propagation, with a single hidden layer is trained for modeling a single tunnel segment. Train and tunnel parameters...
Utilization of automatic computation methods in the design and optimization of reinforced concrete buildings.
Ecer, Akin; Department of Civil Engineering (1967)
Utilization of feature modeling in axiomatic design
Üçtepe, Orhan; Doğru, Ali Hikmet; Department of Computer Engineering (2008)
This thesis provides an approach to use feature modeling with a set of guidelines for requirements definition and decomposition activities of the axiomatic design methodology. A tool that supports the development of feature models and modeling of the Axiomatic Design activities is implemented to be utilized for guiding the designer. Axiomatic Design suggested four domains of information in the transformation of the problem definition to the solution, and provided mechanisms for supporting the mapping among ...
Utilization of numerical weather prediction models for flood planning and operational studies in datascarce basins
Bozkurt, Okan Çağrı; Merzi, Nuri; Akyürek, Sevda Zuhal; Department of Civil Engineering (2020-9)
With the increasing rates of urbanization, floods have become a more critical problem day by day. Consequently, the perception arises that the flood control structures have become insufficient. Well-calibrated weather forecasting and hydrological models are important complementary tools to reduce the damage caused by floods. Such applications can yield results as useful as structural measures. In this study, the effects of the use of numerical weather forecasting and hydrological models on structural and no...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
B. Erkuş, “Utilization of artificial neural networks in building damage prediction,” Middle East Technical University, 1999.