Surrogate-Assisted Cost Optimization for Post-Tensioned Concrete Slab Bridges

Abstract

The study uses surrogate modeling techniques to evaluate cost optimization methodologies for post-tensioned concrete slab bridges. These structures are key components in transportation infrastructure, where design efficiency can yield significant economic benefits. The research focuses on a three-span slab bridge, with spans of 24, 34, and 28 m, optimized through the Kriging surrogate model combined with heuristic algorithms such as simulated annealing. Input variables included deck depth, base geometry, and concrete grade, with Latin Hypercube Sampling ensuring diverse design exploration. Results reveal that the optimized design achieves a 6.54% cost reduction compared to conventional approaches, primarily by minimizing material usage—concrete by 14.8% and active steel by 11.25%. Among the predictive models analyzed, the neural network demonstrated the lowest prediction error but required multiple runs for stability, while the Kriging model offered accurate local optimum identification. This work highlights surrogate modeling as a practical and efficient tool for bridge design, reducing costs while adhering to structural and serviceability criteria. The methodology facilitates better-informed decision-making in structural engineering, supporting more economical bridge designs.