Research on risk prediction of bridge support maintenance construction based on machine learning

Song Wang; Xiaozhong Li

doi:10.1117/12.3034433

16 October 2024 Research on risk prediction of bridge support maintenance construction based on machine learning

Song Wang, Xiaozhong Li

Author Affiliations +

Proceedings Volume 13291, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024); 1329160 (2024) https://doi.org/10.1117/12.3034433
Event: Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), 2024, Changchun, China

Abstract

The risk prediction in the process of bridge support maintenance and construction is very important to the stability and safety of the whole bridge structure. Taking a bridge support maintenance construction project as a case, this study analyzed and sorted out the risk factors from three aspects of construction environment, support maintenance construction and construction management through expert investigation and literature reading based on the machine learning algorithm prediction model, established the risk prediction index system, and adopted the grid search algorithm to optimize the hyperparameters of the prediction model. The model evaluation indexes R2 and MAE were used to evaluate the prediction results. The results show that the prediction model of the neural network algorithm can predict the construction risk more accurately than that of the random forest algorithm. However, the performance of the random forest algorithm on the test set is poor due to the over-fitting of data on the training set, and the performance of the model needs to be improved by combining related optimization algorithms.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Song Wang and Xiaozhong Li "Research on risk prediction of bridge support maintenance construction based on machine learning", Proc. SPIE 13291, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), 1329160 (16 October 2024); https://doi.org/10.1117/12.3034433

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