Analysis of Two-Dimensional Truss Bridge Design Under Uncertainty

Abstract

In civil engineering, computational analysis offers invaluable insights into understanding and predicting the responses of complex structures, with truss bridges as an example. Truss bridges, characterized by their various types and applications in construction projects, are mired in uncertainties arising from factors such as load variability and environmental conditions. While traditional deterministic approaches determine their design and performance, modern analytical techniques such as Neural Networks promise enhanced comprehension of these uncertainties. This report investigates the application of Neural Networks as a surrogate model to the FEM, with application to the analysis of truss bridge configurations, specifically five key designs: Warren, Pratt, Howe, K-Truss, and Bowstring. Preliminary findings illustrate initial agreement between FEM and Neural Network predictions, with the latter's higher computational speed during the prediction phase. However, the research acknowledges certain limitations, emphasizing the importance of comprehensive analyses incorporating real-world parameters and ground-truth validations.