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Structural System-Based Evaluation of Steel Girder Highway Bridges and Artificial Neural Network (ANN) Implementation for Bridge Asset Management
Transportation agencies and bridge owners face ongoing challenges to extend service lives for aging bridge inventories and optimize maintenance strategies with limited resources. Conventional load rating based on line-girder analyses results in conservative capacity assessment and bridge management decisions, such as load posting. More rigorous system-based capacity assessment can benefit bridge management, but requires an investment of time and cultivation of expertise. When and how to use detailed 3D bridge analysis remains unclear in current practice. There is a need for an intermediate measure between routine and rigorous methods to supplement decision-making in bridge asset management. ^ This study examined the potential benefits of detailed structural system-based evaluation and capacity assessment, and provided insight into using artificial neural networks (ANNs) in decision-making for when to employ more rigorous analysis methods or load testing in bridge asset management. Parametric case studies, using a detailed 3D FEM-based modeling approach, highlighted the complexities of structural system -behavior that are ignored or obscured in the 1D-line girder approach, but have potential to inform and improve bridge asset management. Structural systems exhibited substantial unacknowledged capacity for a modest level of support restraint consistent with actual support conditions. For skewed bridges, inclusion of concrete cracking in analytical models substantially increased distribution factors at higher skews. System-based ultimate capacity increased with skew, but the first yielding initiation load-capacity was poorly correlated with skew despite the reduction of moment demands in girders. An assessment of load distribution methodologies (such as response based fractions and beam-line approach) showed significant disparities in estimating distribution factors at moderate to higher skews.^ The proposed ANN models, single-best-network and committee networks (CN), demonstrated prediction capability sufficient for capturing the complexity of mapping geometric and material properties to refine load ratings of existing steel girder bridges. The CN model showed improved accuracy with higher confidence on error than single-best-network due to diversity of its composition networks. The study highlighted the potential for ANNs to be employed in bridge management as a supplementary measure, helping bridge owners decide when to invest in more costly modeling and testing methods to increase recognized capacity in the existing bridge inventory.^
Sofi, Fayaz Ahmad, "Structural System-Based Evaluation of Steel Girder Highway Bridges and Artificial Neural Network (ANN) Implementation for Bridge Asset Management" (2017). ETD collection for University of Nebraska - Lincoln. AAI10682985.