Mr. Yan Zhao Profile

Yan Zhao

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Proceedings Article | 23 January 2024 Paper

Prediction and analysis of uneven settlement of urban bridges and surrounding ground surface by combining GA-BP algorithm

Tianyuge Yuan, Xianglei Liu, Yan Zhao

Proceedings Volume 12978, 1297825 (2024) https://doi.org/10.1117/12.3019570

KEYWORDS: Bridges, Neural networks, Education and training, Evolutionary algorithms, Genetic algorithms, Deformation, Interferometric synthetic aperture radar, Environmental monitoring, Genetics, Error analysis

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The aging of bridges, traffic congestion, and vehicle overloading have inevitably aggravated the damage of urban bridges in recent years, and it is extremely important to predict and analyze the settlement of urban bridges to monitor their safety conditions. The PS-InSAR (Permanent Scatterer Synthetic Aperture Radar) technique compensates for the drawbacks of the traditional InSAR technique such as atmospheric phase delay, and thus can obtain more accurate settlement information, which has unique advantages in long-term slow accumulation monitoring of surface deformation, and is especially suitable for this experiment to monitor and analyze the uneven settlement of urban bridges. In this paper, we obtain the uneven settlement of urban bridges based on PS-InSAR technique, and combine genetic algorithm and neural network algorithm (GA-BP) to achieve the prediction and analysis of uneven settlement of urban bridges. The method is also applied to the simulation experiment and analysis of uneven settlement prediction of Suzhou Bridge and the surrounding ground surface in Beijing. The experimental results show that the root mean squared error (RMSE, Root Mean Squared Error) of applying the GA-BP algorithm to predict the uneven settlement of urban bridges and the surrounding ground surface is 0.57500 mm, which has a good accuracy and proves that the method can effectively predict the uneven settlement of urban bridges and the surrounding ground surface, and with the increase of the number of experimental data. The best iteration the fewer the number of iterations, proving that its prediction performance will be better with the growth of the number of acquired data.

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