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A platform for research: civil engineering, architecture and urbanism
Influence of the Primary Bridge Component Condition on the Overall Bridge Condition Rating
Bridge condition rating is evaluated based on the material condition of the secondary and primary bridge components. This paper aims to investigate the influence of the condition of the primary bridge components to the overall condition of pre-stressed concrete beam bridge (PCBB), reinforced concrete beam bridge (RCBB) and steel beam bridge (SBB). Four primary bridge components namely surfacing, deck slab, beam/girder and abutments are used as input parameters and bridge condition rating as output parameters. This study utilizes multiple linear regression analysis (MRA) and artificial neural networks (ANN) to investigate the variance of the bridge condition rating with respect to the condition of the primary bridge components. The MRA results show that 62.83, 91.77 and 86.18 % of the proportion of the variance in the condition rating of PCBB, RCBB and SSB are explained by all the primary bridge components in the range of the training data set. Meanwhile ANN yields 67.35, 90.54 and 81.77 % for PCBB, RCBB and SSB, respectively. The results indicate that the condition rating of surfacing, deck slab, beam/girder and abutments highly contribute to the condition rating of RCBB and SSB, however for the PCBB, the influence is slightly lower. In term of modeling, MRA shows better performance for RCBB and SBB; however ANN seems suitable for PCBB.
Influence of the Primary Bridge Component Condition on the Overall Bridge Condition Rating
Bridge condition rating is evaluated based on the material condition of the secondary and primary bridge components. This paper aims to investigate the influence of the condition of the primary bridge components to the overall condition of pre-stressed concrete beam bridge (PCBB), reinforced concrete beam bridge (RCBB) and steel beam bridge (SBB). Four primary bridge components namely surfacing, deck slab, beam/girder and abutments are used as input parameters and bridge condition rating as output parameters. This study utilizes multiple linear regression analysis (MRA) and artificial neural networks (ANN) to investigate the variance of the bridge condition rating with respect to the condition of the primary bridge components. The MRA results show that 62.83, 91.77 and 86.18 % of the proportion of the variance in the condition rating of PCBB, RCBB and SSB are explained by all the primary bridge components in the range of the training data set. Meanwhile ANN yields 67.35, 90.54 and 81.77 % for PCBB, RCBB and SSB, respectively. The results indicate that the condition rating of surfacing, deck slab, beam/girder and abutments highly contribute to the condition rating of RCBB and SSB, however for the PCBB, the influence is slightly lower. In term of modeling, MRA shows better performance for RCBB and SBB; however ANN seems suitable for PCBB.
Influence of the Primary Bridge Component Condition on the Overall Bridge Condition Rating
Springer Tracts on Transportation, Traffic
Caner, Alp (editor) / Gülkan, Polat (editor) / Mahmoud, Khaled (editor) / Hamid, R. (author) / Khairullah, Y. (author) / Khalim, A. R. (author)
2015-08-12
10 pages
Article/Chapter (Book)
Electronic Resource
English
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