Urban Railway Bridge Fast Evaluation Based on Cloud Computing


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Although modern bridge are carefully designed and well constructed, damage may occur in them due to unexpected causes. Currently, many different techniques have been proposed and investigated in bridge condition assessment. However, evaluation efficiency of condition assessment has not been paid much attention by the researchers. A fast evaluation of the urban railway bridge condition based on the cloud computing is presented. In this paper dynamic FE model and Artificial neural networks technique is applied to model updating. The cloud computing model provides the basis for fast analyses. It was found that when applied to the actually railway bridges, the proposed method provided results similar to those obtained by experts, but can improve efficiency of bridge



Edited by:

Dongye Sun, Wen-Pei Sung and Ran Chen






M. Chen and W. Zhou, "Urban Railway Bridge Fast Evaluation Based on Cloud Computing", Applied Mechanics and Materials, Vols. 71-78, pp. 4501-4505, 2011

Online since:

July 2011





[1] Saptarshi Sasmal, K. Ramanjaneyulu: Condition evaluation of existing reinforced concrete bridges using fuzzy based analytic hierarchy approach. Expert Systems with Applications Vol. 35(2008), p.1430–1443.

DOI: 10.1016/j.eswa.2007.08.017

[2] Kei Kawamura, Ayaho Miyamoto: Condition state evaluation of existing reinforced concrete bridges using neuro-fuzzy hybrid system. Computers & Structures Vol. 81(2003), p.1931–(1940).

DOI: 10.1016/s0045-7949(03)00213-x

[3] Kevin L. Rens, Carnot L. Nogueira, David J. Transue: Bridge Management and Nondestructive Evaluation. Journal of Performance of Constructed Facilities Vol. 19(2005), p.3–16.

DOI: 10.1061/(asce)0887-3828(2006)20:3(301)

[4] Ming-Cheng Chen, Chin-Kuo Huang, Chung-Yue Wang: Crack Condition Evaluation Approach for the Deteriorated Girder of an RC Bridge. Journal of Bridge Engineering Vol. 12(2007), p.654–661.

DOI: 10.1061/(asce)1084-0702(2007)12:5(654)

[5] James A. Swanson, Arthur J. Helmicki, Victor J. Hunt: Modal Contribution Coefficients in Bridge Condition Evaluation. Journal of Bridge Engineering Vol. 10(2005), p.169–178.

DOI: 10.1061/(asce)1084-0702(2005)10:2(169)

[6] Ying-Ming Wang, Taha M.S. Elhag: An adaptive neuro-fuzzy inference system for bridge risk assessment. Expert Systems with Applications Vol. 34(2008), p.3099–3160.

DOI: 10.1016/j.eswa.2007.06.026

[7] Ma Yali, Wang Dongwei, Zhang Ailin: A Structural Health-evaluation Method of Bridge on Service: Multi-phase Fuzzy Comprehensive Evaluation. Journal of Beijng University of Technology Vol. 31(2005), p.36–38, in Chinese.

[8] Yang Zeying, Huang Chengkui, Qu Jianbo: Durability Evaluation of Bridges Based on ANFIS and Genetic Algorithms. China Civil Engineering Journal Vol. 39(2006), p.16–22, in Chinese.

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