Prediction Method of Bridge Pre-camber Based on Artificial Intelligence Method

Traditional methods for predicting bridge camber often rely on mathematical models and statistical analysis, but they face challenges in handling multicollinearity and complex bridge structures. Therefore, we introduce regression penalization methods that can penalize and sparsify coefficient estimates, handle multicollinearity, and perform feature selection. This study aims to explore the application of regression in predicting bridge camber. Accurate prediction of bridge camber is crucial for ensuring the safety and success of bridge construction. Predictive models are established with actual bridge data, including temperature changes, construction loads, prestress tensioning errors, concrete shrinkage and creep, and formwork elevation settings. By analyzing the performance of different regression methods, we will evaluate their advantages and disadvantages in improving the accuracy and stability of bridge camber predictions. This research will provide empirical support for improving camber prediction methods in bridge engineering and is expected to offer insights and references for future similar studies. Through this in-depth investigation of a critical issue, we aim to enhance the efficiency and safety of bridge construction.