A Graded Evaluation Method for Construction Quality of Prefabricated Beam Bridges Based on Manifold Learning and Probability Distributions

Abstract

This study aims to construct a graded evaluation method for the construction quality of Highway Prefabricated Beam Bridges, in order to support refined quality control beyond the traditional “qualified/unqualified” judgment. Existing evaluation methods have difficulty effectively distinguishing quality differences within the qualified range, may cause redundant calculations for indicator information with non-linear correlations, are susceptible to the influence of outlier expert judgments, and may mask local critical defects when adopting linear weighted aggregation. This paper uses historical quality inspection and expert evaluation records from 50 prefabricated beam bridges to validate the proposed method. Experimental results show that the proposed method achieves an RMSE of 1.24, outperforming the traditional AHP method (1.79) and the equal-weight method (2.52). Five-fold grouped cross-validation at the bridge level gave an RMSE of 1.2180 ± 0.0802 and an R2 of 0.9536. It can provide accurate intelligent decision support for quality and safety control during the construction of Highway Prefabricated Beam Bridges, and promotes the digital upgrade of quality management in the construction industry. The proposed framework can be integrated into a digital construction quality management platform to support bridge quality ranking and early warning of safety-critical defects. The originality of this paper lies in the integration, within a unified evaluation framework, of continuous score conversion based on probability distributions, manifold-learning-based redundancy correction for indicators, geometrically median-based robust fusion of expert weights, and a threshold-triggered risk-sensitive weight adjustment mechanism.