Intelligent Quality Evaluation Method for Highway Prefabricated Beam Bridges Based on Key Parameter Control and Weighted Neural Network Optimization

To reduce dependence on expert judgement and improve the consistency and resolution of construction quality evaluation for highway prefabricated girder bridges (HPGBs), this study proposes an intelligent evaluation method integrating key parameter control and weighted neural network optimization. First, a three-level evaluation system covering the finished bridge, first-level indicators, and secondary indicators is established for the full construction process from prefabrication to installation. Threshold-type indicators are scored using a two-point rule, whereas finite interval indicators are converted into continuous scores using a two-sided piecewise-normalized truncated normal model. Grey relational analysis is then used to identify key quality parameters and quantify inter-indicator relational strength, and an adaptive weight decay mechanism attenuates redundant non-key indicators while retaining the key parameters. Data-driven indicator importance is subsequently learned using a weighted neural network, and hierarchical weighted aggregation is used to obtain the final bridge score. The model development database contains 10 HPGBs and nine expert evaluations per bridge, yielding 90 labelled expert-bridge records; Bridges 1–7 were used for training and Bridges 8–10 for bridge-wise testing. On the test set, the proposed method achieved R2 = 0.9147, RMSE = 1.294, MAE = 1.0987, and MRE = 1.36%. Compared with the traditional AHP method, MAE was reduced by approximately 22.01%, and R2 increased by approximately 5.86%. An engineering application yielded a comprehensive score of 84.4, corresponding to Grade B. These results demonstrate the feasibility of the proposed framework for refined quality evaluation within the HPGB class considered in this study.