KEYWORDS: Bridges, Analytical research, Error analysis, Data modeling, Safety, Reliability, Received signal strength, Time metrology, Earth sciences, Statistical analysis
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.
In this paper, according to the characteristics of the grey forecast method and the neural network, constructed the parallel grey neural network model(PGNN) and apply to forecast a tunnel monitoring point’s settlement displacement data based on Nanjing metro. The results showed that the prediction accuracy of PGNN is significantly higher than that of unitary grey and neural forecast method. proves that the effectiveness of PGNN in the tunnel settlement prediction. Keywords: Tunnel settlement, grey model, neural network model, prediction
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