A deformation analysis method of stepwise regression for bridge deflection prediction

Yueqian Shen; Ying Zeng; Lei Zhu; Teng Huang

doi:10.1117/12.2207876

9 December 2015 A deformation analysis method of stepwise regression for bridge deflection prediction

Yueqian Shen, Ying Zeng, Lei Zhu, Teng Huang

Proceedings Volume 9808, International Conference on Intelligent Earth Observing and Applications 2015; 98082Q (2015) https://doi.org/10.1117/12.2207876
Event: International Conference on Intelligent Earth Observing and Applications, 2015, Guilin, China

Abstract

Large-scale bridges are among the most important infrastructures whose safe conditions concern people’s daily activities and life safety. Monitoring of large-scale bridges is crucial since deformation might have occurred. How to obtain the deformation information and then judge the safe conditions are the key and difficult problems in bridge deformation monitoring field. Deflection is the important index for evaluation of bridge safety. This paper proposes a forecasting modeling of stepwise regression analysis. Based on the deflection monitoring data of Yangtze River Bridge, the main factors influenced deflection deformation is chiefly studied. Authors use the monitoring data to forecast the deformation value of a bridge deflection at different time from the perspective of non-bridge structure, and compared to the forecasting of gray relational analysis based on linear regression. The result show that the accuracy and reliability of stepwise regression analysis is high, which provides the scientific basis to the bridge operation management. And above all, the ideas of this research provide and effective method for bridge deformation analysis.

Citation Download Citation

Yueqian Shen, Ying Zeng, Lei Zhu, and Teng Huang "A deformation analysis method of stepwise regression for bridge deflection prediction", Proc. SPIE 9808, International Conference on Intelligent Earth Observing and Applications 2015, 98082Q (9 December 2015); https://doi.org/10.1117/12.2207876

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