Optimized Proportioning Model and Efficacy Analysis of High Performance Concrete in Bridge Construction

As an important part of the raw materials for building construction, high performance concrete is well known for its many properties. This paper analyzes the prerequisites for high-performance concrete proportioning from the technical point of view and processes involved in designing high-performance concrete proportioning. A particle swarm algorithm combined with a neural network is used to establish the relationship model between concrete strength and proportion, and the constructed PSO-BP model is used for the prediction of concrete mixing ratio strength to get the best material proportion. The prediction ability of the BP neural network model, GP model, and PSO-BP model is compared. In order to test the applicability and reliability of the high-performance concrete formulated by the PSO-BP model in members, static load tests were carried out in this paper on a 20m test single girder to test the strain and deflection of concrete in the control section. The results show that the relative error of the high-performance concrete strength predictions predicted by the PSO-BP model is within 2%. It shows that the optimum high-performance concrete can be formulated using the PSO-BP model. The test beams can meet design and practical engineering requirements in terms of strength and stiffness and can be applied to real bridges. Based on the research and analysis, suggestions have been made for the application of high-performance concrete in bridge construction from four aspects.