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DC Field | Value | Language |
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dc.contributor.author | Zayan, Hend Saad | - |
dc.date.accessioned | 2022-10-24T12:00:01Z | - |
dc.date.available | 2022-10-24T12:00:01Z | - |
dc.date.issued | 2016 | - |
dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/6028 | - |
dc.description | Master Thesis | en_US |
dc.description.abstract | This research numerically investigats the behavior of reinforced concrete deep beams exposed to elevated temperature. Deep beam is a structural member which has a larger depth with respect to span and has slender width. It has been defined in different forms into codes. It is a significant part in building, bridges, and other structures. The specimens were analyzed by finite element analysis in ANSYS package using nonlinear material model within including the thermal analysis. Good agreements have been noticed between experimental works models from previous researches and present FEA model. A parametric study have been done. It was investigated for the significant factors which affect on the load carrying capacity and flexural behavior of deep beams subjected to elevated temperatures. Three properties were chosen: temperature (T) (20-500)˚C, compressive strength ( ) (20-70) MPa, and length to effective depth ratio (a/d) ratio(1-3). A 105 models were analyzed and discussed. For changing the temperature, the results showed that by increasing temperature for the R.C. deep beam, the load capacity and deflection at failure are decreased with the same dimension and same compressive strength. For the ultimate flexural capacity, an artificial neural network is developed using different software programs (MATLAB & SPSS) and the ultimate load capacity of each specimen is determined from these networks. It is found that the average ratio of predicted results was (R2=0.94). it is apparent that neural networks provide an efficient alternative method in predicting the strength capacity of R.C. deep beams exposed to elevated temperatures. III Also, this study explores the use of artificial neural networks (ANNs) for predicting the ultimate carrying capacity of deep beam subjected to elevated temperatures. One hundred five finite element data issued from the verified FE model with the literature. The data are arranged information such that seven input parameters cover the geometrical and material properties of the deep beam and corresponding one out value is ultimate load. | en_US |
dc.language.iso | en | en_US |
dc.title | Behavior of Reinforced Concrete Deep Beams Exposure to Elevated Temperature | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | الهندسة المدنية |
Files in This Item:
File | Description | Size | Format | |
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Title (20 files merged).pdf | 9.07 MB | Adobe PDF | View/Open |
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