Analysis of highway bridges using computer-assisted modeling, neural networks, and data compression techniques

By making use of modern computing facilities, it is now possible to routinely apply finite element analysis (FEA) techniques to the analysis of complex structural systems. While these techniques may be successfully applied to the area of highway bridge analysis, there arise certain considerations specific to bridge analysis that must be addressed.; To properly analyze bridge systems for rating purposes, it is necessary to model each distinct structural stage of construction. Also, due to the nature of moving vehicular loading, the modeling of such loads is complex and cumbersome. To address these issues, computer assisted modeling software has been developed that allows an engineer to easily model both the construction stages of a bridge and complex vehicular loading conditions.; Using the modeling software an engineer can create large, refined FEA models that otherwise would have required prohibitively large quantities of time to prepare manually. However, as the size of these models increases so does the demand on the computing facilities used to perform the analysis. This is especially true in regard to temporary storage requirements and required execution time.; To address these issues a real time lossless data compression strategy suitable for FEA software has been developed, implemented, and tested. The use of this data compression strategy has resulted in dramatically reduced storage requirements and, in many cases, also a significant reduction in the analysis execution time. The latter result can be attributed to the reduced quantity of physical data transfer which must be performed during the analysis.; In a further attempt to reduce the analysis execution time, a neural network has been employed to create a domain specific equation solver. The chosen domain is that of two-span flat-slab bridges. A neural network has been trained to predict displacement patterns for these bridges under various loading conditions. Subsequently, a preconditioned conjugate gradient equation solver was constructed using the neural network both to seed the solution vector and to act as a preconditioner. Results are promising but further network training is needed to fully realize the potential of the application.