Adaptive MEC–RBF Neural Network-Based Deflection Prediction for Prestressed Concrete Continuous Rigid Frame Bridges During Construction

A deflection prediction approach based on an adaptive MEC–RBF neural network was developed in this study. By dynamically optimizing the centres, widths, and weights of the RBF network, the proposed method substantially increases the prediction accuracy, and it achieves an R2 of 0.9789 and an RMSE of 1.4978 on the training dataset. It effectively resolves the stability challenges that are associated with nonlinear construction conditions in traditional models. An orthogonal experimental design analysis revealed that the girder block length and the cantilever-to-span length ratio (d/L) were the most influential factors that affected deflection, whereas the effects of uniformly distributed loads and temperature were negligible, thereby providing a sound basis for parameter simplification. The application of the model to the Hannan Yangtze River Bridge yielded a maximum discrepancy of only 5.56 mm (17.7% error rate) between the predicted and measured values, thus demonstrating its practical engineering reliability. By innovatively integrating intelligent optimization techniques with neural networks, this approach overcomes the limitations in terms of real-time responsiveness and long-term stability of conventional methods and offers an efficient and reliable technical tool for the control of large-scale bridge construction.